Sensor-augmented pump therapy lowers HbA1c in suboptimally controlled Type 1 diabetes; a randomized controlled trial

UITNODIGING

Voor het bijwonen van de openbare verdediging van het proefschrift van

Jeroen Hermanides

Measure for Measure

Consequences, Detection and Treatment

of Hyperglycaemia

Op vrijdag 4 juni 2010 om 14:00 in de Agnietenkapel,

Oudezijds Voorburgwal 231, Amsterdam

Receptie ter plaatse na afloop van de verdediging

Paranimfen

Bas van [email protected]

Wietse [email protected]

Jeroen HermanidesTugelaweg 139F

1091VX [email protected]

Measure for MeasureConsequences, Detection and Treatment

ofHyperglycaemia

Jeroen Hermanides“Pissing Evil”

That is how sir Thomas Willis in the 17th century described the high concentrations of glucose found in the urine of patients. The sweet urine described by dr. Willis was the result of pathologically elevated concentrations of glucose in the blood called hyperglycaemia. In those days, hyperglycaemia would eventually mean certain death. Since then we have come a long way, but are still left with questions on the consequences ofhyperglycaemia and how we best measure it in order to take the appropriate measures. In this thesis, several studies are presented that investigated the consequences, detection and treatment of hyperglycaemia.

Measure for M

easure

Jeroen Herm

anides

Measure for Measure

Consequences, Detection and Treatment of

Hyperglycaemia

Jeroen Hermanides

Author: Jeroen Hermanides

Cover: Design of a perpetuum mobile by Johann Joachim Becher, adapted

from Technica Curiosa (Casper Scott, 1664)

Printed by: Gildeprint Drukkerijen B.V.

ISBN/EAN 978-90-9025313-8

The printing of this thesis was financially supported by:

Abbott Diabetes Care, B. Braun Medical B.V., Bayer B.V., Boehringer Ingelheim

B.V., Eli Lilly Nederland B.V., GlaxoSmithKline, J.E. Jurriaanse Stichting,

Menarini Diagnostics Benelux, Merck Sharp & Dohme B.V., NovoNordisk B.V.,

sanofi-aventis Netherlands B.V., Stichting Amstol, Stichting Asklepios, and the

Universiteit van Amsterdam.

Copyright© 2010, J. Hermanides, Amsterdam, The Netherlands No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means, without prior written permission of the author

Measure for Measure

Consequences, Detection and Treatment of

Hyperglycaemia

Academisch proefschrift

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus

prof.dr. D.C. van den Boom ten overstaan van een door het college voor promoties

ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op

vrijdag 4 juni 2010, te 14.00 uur

door

Jeroen Hermanides

Geboren te Amsterdam

PROMOTIECOMMISSIE

Promotor Prof.dr. J.B.L. Hoekstra

Co-promotor Dr. J.H. de Vries

Overige leden Prof.dr. H.R. Büller

Prof.dr. J.C. Pickup

Prof.dr. F.R. Rosendaal

Prof.dr. W.S. Schlack

Prof.dr. D.F. Zandstra

Faculteit der Geneeskunde

Financial support by the Netherlands Heart Foundation and the Dutch Diabetes

Research Foundation for the publication of this thesis is gratefully acknowledged.

TABLE OF CONTENTS

Chapter 1 Introduction 7

PART I Consequences of Hyperglycaemia

Chapter 2 Glucose: a prothrombotic factor? 15

Chapter 3 Venous thrombosis is associated with 31

hyperglycaemia at diagnosis: a case–control study

Chapter 4 Hip surgery sequentially induces stress 43

hyperglycaemia and activates coagulation

Chapter 5 Stress-induced hyperglycaemia and venous 53

thromboembolism following total hip or total knee

arthroplasty

Chapter 6 Early postoperative hyperglycaemia is associated with 67

postoperative complications after pancreatoduodenectomy

PART II Detection and Treatment of Hyperglycaemia

Chapter 7 Sense and nonsense in sensors 87

Chapter 8 Sensor augmented pump therapy 97

lowers HbA1c in suboptimally controlled

type 1 diabetes; a randomised controlled trial

Chapter 9 Sensor augmented insulin pump 117

therapy to treat hyperglycaemia at the

coronary care unit; a randomised clinical pilot trial

Chapter 10 No apparent local effect of insulin on microdialysis 133

continuous glucose-monitoring measurements.

Chapter 11 Algorithm to treat postprandial glycaemic 141

excursions using a closed-loop format: a pilot study

Chapter 12 Glucose variability is associated with ICU mortality 151

Chapter 13 Hypoglycaemia is associated with ICU mortality 165

Chapter 14 Mean glucose during ICU admission is 179

related to mortality by a U-shaped curve;

implications for clinical care

Chapter 15 Summary and future considerations 197

Samenvatting en toekomstperspectief 205

List of publications 213

Biografie 217

Co-authors 221

Dankwoord 225

Introduction

J Hermanides

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„Pissing Evil‰

That is how sir Thomas Willis, the famous London physician, described the high

concentrations of glucose found in the urine of his patients.1 For this, the

underlying illness became known as „diabetes mellitus‰, translated as „flowing

sweetness‰. The sweet urine described by dr. Willis was the result of pathologically

elevated concentrations of glucose in the blood called hyperglycaemia. With no

treatment available at that time, patients with hyperglycaemia would eventually

face certain death. Nowadays in the Western world insulin and other glucose

lowering agents have become available to treat hyperglycaemia and life expectancy

of patients with diabetes continues to increase.2 However, patients with diabetes

mellitus type 1 still face intensive, bothersome and lifelong treatment, trying to

prevent complications.3;4 Recently the growing epidemic of diabetes mellitus type 2

has put this form of diabetes in the spotlight.5 Even more since morbidity and

mortality are worrying.6

In 2001 another form of hyperglycaemia drew everyoneÊs attention (at least in the

medical world) when Greet van den Berghe in Leuven showed that strict glycaemic

control in the Intensive Care Unit (ICU) reduced mortality by a relative 43%.7

Especially patients without known diabetes, but with transient hyperglycaemia

because of severe illness, benefitted from this intervention.8 This so-called „diabetes

of injury‰ or „stress-hyperglycaemia‰ turned out to be a risk factor for poor outcome

in a variety of acute- and severe illnesses.9;10

But what exactly is the harm of hyperglycaemia? It is known to cause micro-,

macrovascular and neural damage in longstanding diabetes.3;4;6 Another major

cause of morbidity and mortality is the influence of hyperglycaemia on the

coagulation system11, thereby causing arterial and venous thrombosis, leading to

stroke, myocardial infarction and pulmonary embolism. The consequences of stress

hyperglycaemia in different patient groups remain unclear. In general it is thought

to have its impact on the immune system, the endothelium and the coagulation

system.9 The exact effects seem to vary between patient groups and many research

questions remain unanswered. In PPART I of this thesis the effects of

hyperglycaemia on the coagulation system are investigated in venous thrombosis

and after orthopaedic surgery. CChapter 2 gives a review of the current evidence on

hyperglycaemia and thrombosis. The association between venous thrombosis and

hyperglycaemia is investigated in CChapter 3, whereas CChapter 4 and 5 study the

influence of orthopaedic surgery on glucose metabolism, coagulation activation and

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postoperative venous thrombosis. Furthermore, the consequences of

hyperglycaemia after pancreatoduodenectomy are studied in CChapter 6.

With developing technologies, new possibilities have become available to target

hyperglycaemia. After advancing from bulky insulin needles to the delicate pens

today, the development of the insulin pump made it possible to continuously

administer and adjust insulin administration.12 This flexible insulin dosing proved

beneficial in treating diabetes mellitus.13 Hereafter, new ways of measuring glucose

became available with the continuous glucose sensors. Instead of the snapshot

glucose values gained with fingerstick measurements, there was now a continuous

stream of data available.14 Only recently, these two devices have been combined in

one integrated sensor augmented insulin pump15, a major step towards the

development of a closed-loop system or artificial pancreas. Clearly, the detection

and treatment of hyperglycaemia is being modernised and provides a source for

interesting research questions in PPART II.

Chapter 7 comments on the development of the glucose sensors so far. In CChapter 8

the results of a randomised controlled trial investigating the efficacy of sensor

augmented pump therapy are presented. In CChapter 9 the application of sensor

augmented pump therapy in patients with acute myocardial infarction and

hyperglycaemia is studied. Also further steps towards the closed-loop system have

been studied and described by infusing insulin in the close proximity of

subcutaneous glucose sensors in CChapter 10 and the development of a closed-loop

algorithm in CChapter 11.

After the first Leuven study by Greet van den Berghe, following trials could not

reproduce her impressive results. Even more, the evidence from the recent NICE-

SUGAR study implies that strict glycaemic control in the ICU does more harm than

good.16 Whether or not strict glycaemic control in the ICU should be common

practice and what guidelines to follow is being heavily debated. Factors not yet

studied could be involved and explain the differences between the trials. We

therefore used a different approach in measuring hyperglycaemia, by taking

glucose variability into account, which is done in CChapter 12. A major side effect of

treating hyperglycaemia and important factor in interpreting study results is

hypoglycaemia. The consequence of hypoglycaemia in the ICU has been

investigated in CChapter 13. Finally, the implications for clinical care resulting from

our data and the Leuven and NICE-SUGAR trials are discussed in CChapter 14.

The title of this thesis refers to the Shakespeare play „Measure for Measure‰, which

means that every action is followed by a reaction or every upside has its downside.

As with ShakespeareÊs play, this thesis is about searching for „truths‰ and

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questioning methods of measuring as these will directly influence the measures

that are taken. The bad guy in this thesis is however not a harsh judge from

ancient Vienna named Angelo, but a pathological phenomenon called

hyperglycaemia.

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REFERENCES (1) Willis T. Pharmaceutice rationalis. 1679.

(2) Gale EA. How to survive diabetes. Diabetologia 2009; 52(4):559-567.

(3) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med 1993; 329(14):977-986.

(4) Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Sustained Effect of Intensive Treatment of Type 1 Diabetes Mellitus on Development and Progression of Diabetic Nephropathy: The Epidemiology of Diabetes Interventions and Complications (EDIC) Study. JAMA 2003; 290(16):2159-2167.

(5) Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The Continuing Epidemics of Obesity and Diabetes in the United States. JAMA 2001; 286(10):1195-1200.

(6) Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998; 352(9131):837-853.

(7) Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M et al. Intensive insulin therapy in the critically ill patients. N Engl J Med 2001; 345(19):1359-1367.

(8) Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F et al. Intensive insulin therapy in mixed medical/surgical intensive care units: benefit versus harm. Diabetes 2006; 55(11):3151-3159.

(9) Dungan KM, Braithwaite SS, Preiser JC. Stress hyperglycaemia. Lancet 2009; 373(9677):1798-1807.

(10) Vriesendorp TM, de Vries JH, Hoekstra JB. [Hyperglycaemia during hospitalisation]. Ned Tijdschr Geneeskd 2007; 151(23):1278-1282.

(11) Carr ME. Diabetes mellitus: a hypercoagulable state. J Diabetes Complications 2001; 15(1):44-54.

(12) Pickup JC, Keen H, Parsons JA, Alberti KG. Continuous subcutaneous insulin infusion: an approach to achieving normoglycaemia. Br Med J 1978; 1(6107):204-207.

(13) Retnakaran R, Hochman J, DeVries JH, Hanaire-Broutin H, Heine RJ, Melki V et al. Continuous Subcutaneous Insulin Infusion Versus Multiple Daily Injections: The impact of baseline A1c. Diabetes Care 2004; 27(11):2590-2596.

(14) Wentholt IM, Hoekstra JB, DeVries JH. Continuous glucose monitors: the long-awaited watch dogs? Diabetes Technol Ther 2007; 9(5):399-409.

(15) Mastrototaro JJ, Cooper KW, Soundararajan G, Sanders JB, Shah RV. Clinical experience with an integrated continuous glucose sensor/insulin pump platform: a feasibility study. Adv Ther 2006; 23(5):725-732.

(16) Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med 2009; 360(13):1283-1297.

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CONSEQUENCES OF HYPERGLYCAEMIA

Glucose: a prothrombotic factor?

J Hermanides, BA Lemkes, JH DeVries, F Holleman,

JCM Meijers and JBL Hoekstra

Submitted for publication

ABSTRACT

Evidence is mounting that both diabetes and stress induced hyperglycaemia

contribute to coagulation activation and hypofibrinolysis, resulting in a

procoagulant state that predisposes patients to thrombotic events. Hyperglycaemia

is often accompanied by hyperinsulinaemia and their combined effects may be even

stronger. In this review we discuss the current evidence regarding the role of

glucose as prothrombotic factor, not only in relation to diabetes, but also with

regard to acute hyperglycaemia. Furthermore, the effects of glucose lowering

therapies to prevent hypercoagulability are considered.

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INTRODUCTION

Patients with diabetes are notorious for their risk of vascular events. Apart from

the effects of diabetes and its prerequisite hyperglycaemia on the development of

atherosclerosis, this high risk may also be caused by the procoagulant state found

in diabetes.1;2

In recent years hyperglycaemia per se, even without overt diabetes, has gained

interest as a potential target to improve clinical outcomes in hospitalised patients

with acute illness.3 In this setting the effects of hyperglycaemia on the coagulation

system may be of greater importance than previously considered. Here we discuss

the current evidence regarding potentially harmful changes in the coagulation

system and subsequent risk of thrombotic disease, not only caused by diabetes but

also by acute hyperglycaemia.

CHRONIC HYPERGLYCAEMIA

Type 2 diabetes

Type 2 diabetes (DM2) is defined by hyperglycaemia, but often accompanied by

hyperinsulinaemia, dyslipidaemia, hypertension and obesity. Its effects on the

coagulation system can therefore not easily be attributed to either one of these

entities4, but the impact of glucose on coagulation in diabetes has been studied

extensively.

Markers of fibrinolysis and coagulation Both parameters of increased coagulability as well as a fibrinolytic impairment

have been found in DM2, although there are many different markers in the

circulation to measure these abnormalities. Platelet-dependent thrombin

generation, for instance, was measured in patients with poor glycaemic control,

good glycaemic control and healthy controls. In vitro induced thrombin generation

was found to be increased in platelet-rich plasma from diabetes patients compared

to healthy controls and a significant elevation of thrombin levels was also

demonstrated in plasma from poorly controlled DM2 when compared to well

controlled patients.5 In a placebo controlled trial using troglitazone combined with

diet modification a significant association was shown between improved glycaemic

control and blood thrombogenicity as reflected by a reduction in ex-vivo thrombus

formation in a Badimon perfusion chamber. Improved glycaemic control was the

only significant predictor of a decrease in blood thrombogenicity irrespective of

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treatment allocation.6 In a group of poorly controlled DM2 patients (HbA1c 10%)

extraordinarily high concentrations of plasminogen activator inhibitor-1 (PAI-1),

indicating hypofibrinolysis, were detected leading the authors to conclude that

profound hyperglycaemia is accompanied by profound increases in PAI-1.

Subsequent treatment of hyperglycaemia by either glipizide or metformine as

monotherapy comparably decreased PAI-1, which argues for an effect of glucose

lowering rather than a specific medication effect.7 This state of hypofibrinolysis was

recently confirmed in a case-control study; patients with DM2 were found to have a

prolonged clot lysis time as well as elevated levels of PAI-1 and von Willebrand

factor (vWf).8 The impairment in the fibrinolytic system in DM2 of interest since

these impairments are independent primary risk factors for myocardial

infarction.9;10 In addition to hypofibronolysis, the levels of prothrombin fragment

1+2 (F1+2) were also found to be associated with the presence of proven

cardiovascular disease in DM2 patients.8

Hyperinsulinaemia To disentangle the effects of glucose and insulin in type 2 diabetes, Boden et al

studied the effects of acute correction of hyperglycaemia with insulin followed by

either 24 hours of experimently induced normo-insulinaemic euglycaemia, 24 hours

of euglycaemic hyperinsulinemia or 24 hours of combined hyperinsulinaemia and

hyperglycaemia, in DM2 patients as well as healthy controls. They found baseline

elevations of tissue factor procoagulant activity (TF-PCA), monocyte TF mRNA and

plasma factor VII, factor VIII and thrombin-antithrombin (TAT) complexes in

patients with DM2 compared to healthy controls. Normalizing glucose significantly

decreased TF-PCA. Increasing insulin levels raised TF-PCA and elevating glucose

and insulin levels together resulted in a much larger rise of TF-PCA, which was

associated with increases in TAT and F1+2.11 Thus glucose and insulin both seem

to play a role in the pathogenesis of the prothrombotic state in type 2 diabetes.

Effect of glucose lowering therapies The effects of improved glycaemic control on PAI-1 levels in DM2 have been

demonstrated for different oral antidiabetic therapies, such as metformin alone12 or

in combination with pioglitazone or rosiglitazone13 and glimepiride in combination

with pioglitazone or rosiglitazone.14 Metformin, which already had proven

beneficial cardiovascular effects in the United Kingdom Prospective Diabetes Study

trial, also reduced factor VII and fibrinogen levels and shortened clot lysis time.15

When metformin was added to a sulphonylurea derivate in poorly controlled elderly

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DM2 patients, the resulting substantial improvement in glycaemic control was

accompanied by beneficial changes in markers of platelet function (platelet factor 4

and beta-thromboglobulin), thrombin generation (fibrinopeptide A, F1+2, and D-

dimer) and fibrinolysis (PAI-1 activity and antigen).16

In the Diabetes Prevention Program, which studied the stages preceding diabetes,

i.e. impaired glucose tolerance, lifestyle interventions even more than metformin

treatment showed a modest, but significant, amelioration in fibrinogen levels.17

Data on the effects of insulin therapy on coagulation and fibrinolysis markers in

DM2 are scarce, and more conflicting than for the oral antidiabetic treatments.

Although some authors report beneficial effects of insulin18, others were unable to

find improvements with insulin therapy.19;20

Type 1 Diabetes

Markers of fibrinolysis and coagulation In type 1 diabetes (DM1) patients the specific contribution of hyperglycaemia to the

prothrombotic state is clearer as they lack the other risk factors which confound the

relationship in DM2 patients. In a long term follow up study, a highly significant

correlation was found between mean HbA1c in DM1 patients over the course of 18

years and impaired fibrinolysis as represented by elevated PAI-1 and decreased

tissue plasminogen activator (t-PA).21 In a smaller setting, eight DM1 patients on

continuous subcutaneous insulin infusion therapy were withheld treatment for the

duration of four hours which caused a rise of PAI-1 and plasma TF. Although the

authors conclude that early ketogenesis causes a prothrombotic change in DM1

patients, the effects of acute hyperglycaemia in this setting cannot be excluded.22

Platelet function tests, including aggregation and platelet adhesion tests, did not

improve with intensive glycaemic control in DM1. However, platelet function tests

are notoriously variable and the authors may not have included a sufficient number

of patients to overcome this disadvantage.23 Finally, despite the abundant evidence

of fibrinolytic impairment in diabetes, not all markers of fibrinolysis are abnormal.

Thrombin-activatable fibrinolysis inhibitor (TAFI) for instance, showed no

difference between patients with DM1 and healthy controls24 a finding which was

recently confirmed in DM2 patients.8

Diabetes and thrombosis

DM2 and, maybe to a lesser extent, DM1 are known for a high risk of developing

atherothrombotic events. This is at least partly explained by hyperglycaemia, given

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the continuous relationship between the development of cardiovascular disease and

glycaemic control, also in DM2.25 Moreover, intensive blood glucose control in the

early stages of the disease proved effective in lowering the long term incidence of

cardiovascular disease in both disease entities.26;27

Recently it has become clear that not only atherothrombotic events are seen more

often in patients with diabetes but that venous thromboembolism (VTE) is also

more frequent in this patient group. Movahed found an odds ratio (OR) of 1.27 (95%

CI 1.19 to 1.35) for the occurrence of pulmonary embolism in DM patients.28

Earlier, Tsai et al also found diabetes to be a risk factor for VTE with a hazard

ratio (HR) of 1.46 (95% CI 1.03 to 2.05), even after adjusting for BMI, a known

predictor of VTE.29 A recent meta-analysis on cardiovascular risk factors for VTE

showed DM to be an independent risk factor with an OR of 1.41 (95%CI 1.12 to

1.77).30 Although the effect of glucose lowering on VTE risk remains to be

established, the overrepresentation of both venous as well as atherothrombosis in

diabetes is highly suggestive of a prothrombotic effect of its main component,

hyperglycaemia, in addition to its more established effects on atherosclerosis.

ACUTE HYPERGLYCAEMIA

Apart from chronic hyperglycaemia, it is important to consider the role of acute

hyperglycaemia. Frequently, this is transient hyperglycaemia resulting from

metabolic deterioration during (severe) illness.31 Although this may result from

pre-existing and undiagnosed diabetes, 30-40% of patients with „stress-

hyperglycaemia‰ will revert to normoglycaemia with follow-up.32;33 Transient

hyperglycaemia will usually be accompanied by transient hyperinsulinaemia.

Markers of fibrinolysis and coagulation The effect of hyperglycaemia and hyperinsulinaemia on the coagulation system in

subjects without diabetes has been studied rather extensively. Already in 1988,

Ceriello showed that experimentally increased glucose levels activated the

coagulation system in non-diabetic subjects by increasing factor VII.34 Stegenga

and co-workers demonstrated in healthy volunteers that hyperglycaemia (12

mmol/l), irrespective of insulin levels, activates coagulation, marked by an increase

in TAT complexes and soluble tissue factor (sTF).35 In contrast, hyperinsulinaemia

inhibited fibrinolysis by increasing PAI-1 levels. This was even more profound

when systemic inflammation was induced.36 In vitro, studies with endothelial cells

from pig aortas exposed to increasing glucose concentrations indicated that PAI-1

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secretion and synthesis increased in parallel to glucose levels. Activation of the

tissue factor pathway following induction of hyperglycaemia in healthy volunteers

was also observed in by Rao and colleagues.37 In a subsequent study, 29 healthy

volunteers were exposed to combinations of euglycaemia or hyperglycaemia with

normoinsulinaemia or hyperinsulinaemia.38 They found that selective

hyperglycaemia and hyperinsulinaemia activated the coagulation system, but the

combination of both showed the largest increase in sTF procoagulant activity, TF

expression on monocytes and TF mRNA in monocytes, TAT, factor VII, factor VIII

and platelet activation, measured by platelet expression of soluble CD40 ligand.

Finally, Nieuwdorp and co-workers discovered that hyperglycaemia in healthy

volunteers concomitantly reduced the protective glycocalyx of the endothelium and

the function of the endothelium itself and increased prothrombin fragment 1+2 and

D-dimer levels.39

Acute hyperglycaemia and coagulation in the ICU Two studies have attempted to elucidate the role of the coagulation system in

relation to strict glycaemic control. In the first Leuven trial, van den Berghe

successfully implemented strict glycaemic control in the ICU, showing a clear

mortality benefit.3 She published a subanalysis, investigating the effect of strict

glucose control on coagulation and fibrinolysis.40 Although a variety of parameters

was assessed, no differences were found between the intensively treated group and

the control group. However, samples were obtained only at 5 and 10 days after

admission and an acute effect within the first 5 days could have been missed.

Savioli and coworkers investigated the effect of strict glucose control on coagulation

and fibrinolysis in patients with septic shock on admission and up to 28 days after

admission.41 They found that strict glucose control reduced the impairment of the

fibrinolytic system, as measured by PAI-1. However, strict glucose control in the

ICU is now being heavily debated because of the recently published NICE-SUGAR

trial, which showed increased mortality in the intervention group.42

Acute hyperglycaemia and thrombosis Several thrombotic conditions are associated with acute hyperglycaemia, most

importantly myocardial infarction (MI), stroke and venous thromboembolism

(VTE).

During MI, admission hyperglycaemia predicts morbidity and mortality in patients

without previously diagnosed diabetes.43-47 Furthermore, elevated admission

glucose levels are directly related to the infarct size and reductions in coronary flow

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after stent implantation in non-diabetic patients.48;49 This might be related to

intravascular thrombotic events. Patients with acute coronary syndrome and

admission glucose >7.0 mmol/l had elevated values of thrombin-antithrombin

complexes and platelets activation, measured by soluble CD40 ligand levels, as

compared to patients admitted with glucose <7.0 mmol/l.50 Also the fibrin clot lysis

time was impaired in hyperglycaemic subjects. In another study activation of

platelets, as measured by beta-thromboglobulin, was associated with

hyperglycaemia after MI, independent of pre-existing diabetes.51 In a rabbit model,

reducing hyperglycaemia using acarbose resulted in decreased infarct size.52

In stroke patients, admission hyperglycaemia was related to the infarct size53-55

and a strong predictor of post-stroke morbidity and mortality.56;57 Ribo and

colleagues showed that acute but not chronic hyperglycaemia during stroke was

associated with lower tissue-type plasminogen activator recanalization rates,

suggesting an impairment of the fibrinolytic system by hyperglycaemia.58

A few studies investigated the relation between venous thromboembolism (VTE)

and hyperglycaemia. Pre-surgery hyperglycaemia (>11.1 mmol/l) is associated with

an OR of 3.2 for pulmonary embolism after orthopaedic surgery.59 However, this

might reflect undiagnosed and uncontrolled diabetes. Recently, we published data

on glucose levels at presentation for suspected VTE and showed that higher glucose

levels at presentation are associated with actually having VTE, in a clear dose-

response fashion.60 During hip surgery, glucose levels rise in patients without

diabetes and this precedes a rise in factor VIII, vWF and F1+261, but whether this

is a causal relation needs further investigation.

Unfortunately, clinical trials in patients with MI and stroke have not been able to

reach their targets and trials aiming to prevent VTE are awaited. After proven

beneficial in patients with DM in the DIGAMI study, the value of glucose-insulin-

potassium (GIK) infusion on outcome after MI in patients without diabetes was

investigated, but no significant contrast in glucose control between the intervention

and control groups was achieved,62-65 The GIST-UK trial randomised stroke

patients to GIK infusion or saline infusion to investigate the effect of glucose

modulation with GIK. In this trial no clinical benefit was observed, however the

trial was underpowered and the glucose lowering effect of GIK was small and

patients were treated for only 24 hours.66

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POSSIBLE MECHANISMS

Many theories on how hyperglycaemia leads to hypercoagulability have already

been proposed and studied.67 First, on a cellular level, hyperglycaemia and also

hyperinsulinaemia increases the expression of PAI-1 on vascular smooth muscle

cells in vitro, thereby increasing its concentration and activity. As a result, the

activity of t-PA is reduced thereby decreasing the fibrinolytic potential.68 The

authors suggested that a direct effect of glucose and insulin on gene transcription

could be responsible. Indeed, hyperglycaemia in the presence of insulin increases

the activity of transcription factor nuclear factor kappa-B (NF-kB) in human

hepatocyte cells and the gene transcription of PAI-1 in vitro, which suggests that

PAI-1 transcription is increased via NF-kB.69 Because this effect disappeared when

an antioxidant was added to the medium, hyperglycaemia-induced oxidative stress

was hypothesised to be the major activator of NF-kB. Khechai and coworkers

studied the effect of advanced glycation end products (AGE) on TF expression in

human monocytes and concluded this AGE-induced TF expression at the mRNA

level, which could be diminished by adding antioxidants. The effect of AGEs on

coagulation activation was also seen when human umbilical vein endothelial cells

were exposed to AGEs70 and AGEs dose-dependently increased procoagulant

activity and TF levels. Withholding insulin in patients with DM1 increased TF and

PAI-1 levels, which was accompanied by a rise in malondialdehyde (MDA) and

protein carbonyl groups (PCG), both markers of oxidative stress.22

Next, hyperglycaemia directly influences the vulnerability of the vascular

endothelium by affecting the glycocalyx, a protective layer of proteoglycans

covering the vessel wall. This results in enhanced platelet-endothelial cell adhesion

and release of coagulation factors harboured within the endothelial glycocalyx.71

Finally, hyperglycaemia exerts its effects also extracellular by direct glycation of

proteins involved in coagulation. Verkleij et al. showed that glycated TAFI looses

its fibrinolytic properties in vitro, although this could not be reproduced in vivo.8

Fibrin clots from patients with diabetes type 2 are more dense as compared to

controls and displayed an altered structure, resulting in longer clot lysis time.72 In vivo glycaemic control was directly correlated to the clot density from the patients.

A likely explanation for this is the possible non-enzymatic glycation of fibrin.73 It is

conceivable that other coagulation proteins are also glycated, altering their activity.

Thus, multiple complex pathways are likely to be involved in the induction of

hypercoagulability by hyperglycaemia, and its effect is more profound in

combination with hyperinsulinaemia.

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SUMMARY

In summary, the evidence is compelling that both chronic and acute

hyperglycaemia contribute to coagulation activation and hypofibrinolysis, resulting

in a procoagulant state that predisposes patients to thrombotic events (Figure 1).

What is more, hyperglycaemia is often accompanied by hyperinsulinaemia and

their combined effects may result in an even stronger hypercoagulable state.

Although we separately discussed the effects of chronic and acute hyperglycaemia

on coagulation, the current evidence gives no reason to assume that the underlying

mechanisms differ.

Intensive glycaemic control in patients with diabetes reduces the incidence of

thrombotic diseases such as myocardial infarction and stroke in the long run.

Whether intensive glucose control during acute hyperglycaemia during acute MI,

stroke and VTE could prevent hypercoagulability and thereby improve outcome

awaits further investigation in randomised controlled trials.

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Figure 1- A simplified impression of the relations between hyperglycaemia, hyperinsulinaemia and coagulation, leading to clinical outcome.

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Venous thrombosis is associated with

hyperglycaemia at diagnosis: a case-control study

J Hermanides, DM Cohn, JH DeVries,

TPW Kamphuisen, R Huijgen, JCM Meijers,

JBL Hoekstra and HR Büller

Journal of Thrombosis and Haemostasis 2009 June;7(6):945-9

ABSTRACT

Background Patients with (undiagnosed) diabetes mellitus, impaired glucose

tolerance or stress-induced hyperglycaemia may be at greater risk for venous

thrombosis and present with relative hyperglycaemia during the thrombotic event.

Objectives To assess whether venous thrombosis is associated with hyperglycaemia

at diagnosis.

Patients/methods We performed a case-control study, derived from a cohort of

consecutive patients referred for suspected deep vein thrombosis. Cases were

patients with confirmed symptomatic venous thrombosis of the lower extremity.

Controls were randomly selected in a 1:2 ratio from individuals in whom this

diagnosis was excluded. We measured plasma glucose levels upon presentation to

the hospital.

Results In total, 188 patients with thrombosis and 370 controls were studied. The

glucose cut-off level for the first to fourth quartiles were as follows: first quartile

<5.3 mmol/l; second quartile 5.3 to 5.7 mmol/l; third quartile 5.7 to 6.6 mmol/l; and

the fourth quartile >=6.6 mmol/l. When adjusted for body mass index, a known

history of diabetes mellitus, age, sex, ethnicity and whether known risk factors for

deep vein thrombosis were present, the odds ratios for deep vein thrombosis in the

second, third and fourth quartiles of glucose levels compared with the first quartile

were 1.59 (95% confidence interval [CI] 0.89 to 2.85), 2.04 (95 % CI 1.15 to 3.62) and

2.21 (95% CI 1.20 to 4.05), respectively, p for trend =0.001.

Conclusions Increased glucose levels measured at presentation were associated

with venous thrombosis. Experimental evidence supports a potential causal role for

hyperglycaemia in this process. As this is the first report on the association

between (stress) hyperglycaemia and venous thrombosis, confirmation in other

studies is required.

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INTRODUCTION

Venous thromboembolism (VTE) is a common disease with an annual incidence of

2-3 per 1000 inhabitants.1 Both acquired and inherited risk factors are known to

play a role in the development of thrombosis.2 Nevertheless, in approximately 25%

of patients with VTE neither an acquired nor an inherited risk factor can be

demonstrated.3;4 Evidence is growing that classic risk factors for arterial disease

are also involved in the development of VTE.5;6 Hyperglycaemia is associated with

arterial thrombosis7-9 and, indeed, patients with diabetes mellitus or the metabolic

syndrome also have an increased risk of VTE.10-12 This increased risk of VTE can in

part be explained by the platelet activation and hypercoagulability present in

diabetes mellitus.13 Activation of the coagulation system has also been observed in

acute experimentally induced hyperglycaemia in healthy male volunteers.14 During

acute illness such as myocardial infarction, a phenomenon called stress

hyperglycaemia may occur independently of the presence of known diabetes.

As hyperglycaemia stimulates coagulation, we hypothesised that higher glucose

levels -independent of known diabetes mellitus- would be more common in patients

presenting with acute VTE.

METHODS

A case-control study was performed. Cases and controls were selected from the

Amsterdam Case-Control Study on Thrombosis, which was initiated in 1999 to

identify new risk factors for deep vein thrombosis (DVT). All consecutive

outpatients older than 18 years referred to the Academic Medical Centre in

Amsterdam between September 1999 and August 2006 with clinically suspected

DVT of the lower extremity were eligible for this study. The study protocol was

approved by the Medical Ethics Review Committee and all participants provided

written informed consent. At presentation, the patientÊs medical history was

obtained through a standardised questionnaire including specific questions about

symptom duration, presence of known risk factors (concomitant malignancy,

pregnancy, use of hormonal replacement therapy, oral contraceptives or selective

oestrogen receptor modulators, recent trauma (within last 60 days), being

bedridden for >3 days, uncommon travel (>6hrs) within the last 3 months, paralysis

of the symptomatic leg, or surgery within the last 4 weeks), concomitant diseases

and medication use. In addition, body mass index (BMI) was calculated. Cases were

patients with thrombosis of the lower extremity confirmed by compression

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ultrasonography, including proximal DVT (i.e. proximal thrombosis of the iliac or

superficial femoral vein, calf vein thrombosis, involving at least the upper third

part of the deep calf veins), symptomatic calf vein thrombosis, and superficial

thrombophlebitis. The diagnosis was confirmed following a diagnostic management

strategy, based on the WellsÊ criteria15 and a Tinaquant D-dimer assay (Roche

Diagnostics, Basel, Switzerland), followed by compression ultrasound if indicated

as validated and described previously.16 Controls were selected in a 1:2 ratio from

those individuals in whom thrombosis was ruled out using the above mentioned

strategy. Selection was performed randomly, only taking into account the

male/female ratio of the cases.

Sample storage and laboratory analysis

On admission and prior to diagnostic testing, blood samples were drawn and

collected in tubes containing 0.109 mmol/l trisodium citrate. Within one hour after

collection, platelet poor plasma was obtained by centrifugation twice for 20 min at

1600 x g and 4°C. The plasma was stored in 2 ml cryovials containing 0.5 ml of

plasma at -80° C.

Glucose was measured using the HK/G-6PD method (Roche/Hitachi, Basel,

Switzerland) and corrected for the 10% dilution with sodium citrate. To assess

whether elevated glucose levels were related to an acute phase response induced by

the thrombotic event itself, we analysed C-reactive protein (CRP) levels from 20

cases and 20 controls, randomly selected from each quartile, giving a total of 160

patients.

Statistical analysis

Results are presented as mean � standard deviation or median with interquartile

range (IQR), depending on the observed distribution. The primary objective of this

study was to assess the relationship between glucose levels at presentation and

VTE, which was expressed as odds ratios (ORs), with 95% confidence intervals.

Glucose levels of the controls were divided into quartiles, as glucose levels are

typically non-normally distributed. Subsequently, the cases were assigned to these

quartiles according to their admission glucose values. Binary logistic regression

was used. The regression model was created on the basis of clinically relevant

potential confounders (BMI, concomitant known diabetes mellitus, sex, ethnicity,

age at diagnosis, and whether known risk factors for VTE were present).

Furthermore, ORs were calculated for cases in which the diagnosis of thrombosis

was restricted to DVT only, excluding calf vein thrombosis and superficial

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thrombophlebitis. To assess the correlation between glucose levels and CRP levels a

scatter plot was performed and the correlation (expressed in r) coefficient was

calculated. All statistical analyses were performed in SPSS version 15.0 (SPSS Inc.,

Chigaco, IL, USA).

RESULTS

In total 188 patients with confirmed thrombosis and 370 controls were included in

this study, as blood samples were not available for two cases and 10 controls. The

baseline characteristics of the two study groups are shown in Table 1. Mean age

and gender distribution were comparable. The control group consisted of a smaller

proportion of white patients and a greater proportion of black patients as compared

with the cases. The mean BMI was higher in the control group, as tended to be the

number of patients with known diabetes mellitus. Median glucose levels were 5.9

mmol/l (IQR 5.3 to 6.6) in patients with thrombosis, and 5.6 mmol/l (IQR 5.2 to 6.6)

in the controls. In thrombosis patients, 38% had one or more acquired risk factors

and the distribution of thrombosis was as follows: 82% had DVT, 6% had calf vein

thrombosis, and the remaining 12% had superficial thrombophlebitis. The cut-off

glucose levels were as follows: first quartile <5.3 mmol/l (n=141); second quartile

5.3 to 5.7 mmol/l (n=134); third quartile 5.7 to 6.6 mmol/l (n=139); and fourth

quartile >=6.6 mmol/l (n=144) (Table 2).

After adjustment for BMI, concomitant known diabetes mellitus, sex, ethnicity, age

at diagnosis, and whether known risk factors for VTE were present, the ORs for

thrombosis in the second, third and fourth quartiles of glucose levels as compared

with the first quartile were 1.40 (95% CI 0.82 to 2.85), 1.69 (95 % CI 1.00 to 2.87)

and 1.94 (95% CI 1.12 to 3.39), respectively; p for trend =0.01. The same trend of an

increasing OR could be observed when adjusting only for BMI, age, sex and known

diabetes mellitus. As most risk factors predominantly relate to DVT a separate

analysis was planned for DVT only, thus excluding patients with superficial

thrombophlebitis or calf vein thrombosis, leaving 154 cases and 370 controls. Here

also, the OR increases with glucose levels: 1.59 (95% CI 0.89 to 2.85), 2.04 (95% CI

1.15 to 3.62) and 2.21 (95% CI 1.20 to 4.05) for the second, third and fourth quartile

of glucose levels, respectively (see Table 2, p for trend =0.001). Finally, the

Spearman rank correlation coefficient for CRP and plasma glucose was 0.09, with a

p-value of 0.27.

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Tabl

e 1-

bas

elin

e ch

arac

teris

tics

of th

e tw

o st

udy

grou

ps

C

ases

(n=1

88)

Con

trol

s (n

=370

) A

ge, y

ears

(mea

n ±S

D)

57±1

7 56

±16

Fem

ale

(%)

Ethn

icity

(%)

- W

hite

-

Bla

ck

- A

sian

/Pac

ific

isla

nder

-

Oth

er

57.4

78

.7

10.1

2.

7 8.

5

58.1

69

.2

18.1

4.

2 8.

5 B

MI k

g/m

² (m

edia

n, IQ

R)

26.6

(23.

9 to

29.

1)

27.2

(24.

2 to

31.

3)

Dia

bete

s ty

pe 1

or 2

(%)

3.7

10.0

G

luco

se m

mol

/l (m

edia

n, IQ

R)

5.9

(5.3

to 6

.6)

5.6

(5.2

to 6

.6)

Kno

wn

risk

fact

ors:

con

com

itant

mal

igna

ncy,

pre

gnan

cy, u

se o

f hor

mon

al re

plac

emen

t the

rapy

, or

al c

ontra

cept

ives

or s

elec

tive

estro

gen

rece

ptor

mod

ulat

ors,

rece

nt tr

aum

a (w

ithin

last

60

days

),

bedr

idde

n >

3 da

ys, u

ncom

mon

trav

el (>

6hr

s) w

ithin

the

last

3 m

onth

s, p

aral

ysis

of t

he s

ympt

omat

ic le

g,

or s

urge

ry w

ithin

the

last

4 w

eeks

Ta

ble

2- o

dds

ratio

s (O

Rs)

for v

enou

s th

rom

boem

bolis

m.

Qua

rtile

(g

luco

se m

mol

/l)

Cas

es

Con

trol

s C

rude

OR

(9

5% C

I) A

djus

ted

OR

* (9

5% C

I) C

ases

† C

ontr

ols†

C

rude

OR

† (9

5% C

I) A

djus

ted

OR

††

(95%

CI)

1st : <

5.3

39

10

2 re

fere

nce

refe

renc

e 28

10

2 re

fere

nce

refe

renc

e 2nd

: 5.3

to 5

.7

46

88

1.37

(0.8

2 to

2.2

8)

1.40

(0.8

2 to

2.3

8)

37

88

1.53

(0.8

7 to

2.7

0)

1.59

(0.8

9 to

2.8

5)

3rd :

5.7

to 6

.6

52

87

1.56

(0.9

4 to

2.5

9)

1.69

(1.0

0 to

2.8

7)

46

87

1.93

(1.1

1 to

3.3

4)

2.04

(1.1

5 to

3.6

2)

4th :

� 6.

6 51

93

1.

43 (0

.87

to 2

.37)

1.

94 (1

.12

to 3

.39)

43

93

1.

68 (0

.97

to 2

.93)

2.

21 (1

.20

to 4

.05)

p fo

r tre

nd =

0.1

4 p

for t

rend

= 0

.01

p fo

r tre

nd =

0.0

5 p

for t

rend

= 0

.001

* a

djus

ted

for B

MI,

know

n di

abet

es m

ellit

us, s

ex, a

ge, e

thni

city

and

kno

wn

risk

fact

ors

†ana

lyse

s fo

r dee

p ve

nous

thro

mbo

sis

only

††

anal

yses

for d

eep

veno

us th

rom

bosi

s on

ly, a

djus

ted

for B

MI,

diab

etes

mel

litus

, sex

, age

, eth

nici

ty a

nd k

now

n ris

k fa

ctor

s C

I=co

nfid

ence

inte

rval

��

36

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

DISCUSSION

In this case-control study, increased glucose levels measured at the time of

presentation were associated with venous thrombosis. This could be a relevant

clinical concept as the general population is becoming increasingly glucose

intolerant. The relationship between glucose and DVT was not readily explained by

an acute phase reaction due to the thrombotic event itself.

Whereas our results indicate that increased glucose levels and VTE coincide, it is

impossible with the current design to demonstrate a causal relationship. However,

a causal relationship seems plausible. To assess this one can apply the diagnostic

criteria for causation.17 A causal relationship is supported by the available

biological evidence from experiments in humans. Stegenga et al. showed that

experimentally induced acute hyperglycaemia activates the coagulation system in

healthy volunteers.14 From a pathophysiologic point of view, hyperglycaemia is

known to induce coagulation activation through glycocalyx damage18, up regulation

of tissue factor19;20, non-enzymatic glycation and the development of increased

oxidative stress.21 Long term exposure to hyperglycaemia such as occurs in

diabetes mellitus, is a known risk factor for VTE.22 In addition, the effect of

hyperglycaemia on coagulation seems to be modifiable in diabetes patients, as

treating hyperglycaemia among these patients led to down regulation of

coagulation activation in several randomised controlled trials.23;24 Furthermore,

our results are in line with the findings by Mraovic et al. who demonstrated that

hyperglycaemia increases the risk of pulmonary embolism after major orthopaedic

surgery.25 Thus, direct and indirect evidence supports a possible association for

acute and chronic hyperglycaemia in the development of VTE. Furthermore, the

association is consistent from this study to other studies, which is in line with the

criterion for repetitive demonstration of causality.

In this study, an adjusted OR of 2.21 (95 % CI 1.20 to 4.05) for DVT was observed

in the highest quartile which suggests a strong relationship. In comparison, the OR

for the well established risk factor for VTE, the prothrombin 20210A mutation is

2.8 (95% CI 1.4 to 5.6).26 The OR for venous thrombosis increases with increasing

glucose levels, from 1.40 (95% CI 0.82 to 2.38) in the second quartile to 1.69 (95%

1.00 to 2.87) in the third quartile and 1.94 (95 % CI 1.12 to 3.39) in the fourth

quartile. We tested for differences in ORs among the quartiles of glucose levels and

found a significant linear trend (p=0.01). This is in concordance with a dose-

response gradient, another criterion for causality.

��

37

The question arises of whether elevated glucose levels during a VTE result from the

inflammatory and counter regulatory hormone action initiated by the VTE event

itself, or whether hyperglycaemia preceded the VTE event. Although a significant

proportion of the patients with hyperglycaemia during an episode of VTE will have

an undisturbed glucose tolerance at follow-up27, undiagnosed impaired glucose

tolerance is likely to have been present in a proportion of patients before the VTE

event itself, and may therefore have contributed to the development of thrombosis.

We therefore suspect a temporal relationship. In addition, no correlation was found

between the acute phase reaction, measured by CRP, and glucose levels. The

presence of stress hyperglycaemia during a thrombotic event, independent of its

cause, could be relevant: it has been shown to have evident clinical consequences in

patients with myocardial infarction and patients admitted to the intensive care

unit (especially without known diabetes mellitus)28;29, although results from recent

intervention trials have been disappointing.30;31

Interestingly, we found a greater proportion of patients with diabetes in the control

group than in the case group. In fact, this higher rate can be caused by referral

bias. Patients with diabetes are usually under chronic medical care and are prone

to leg- and foot problems that can resemble DVT, such as erysipelas. Thus, they are

more easily referred for suspicion of DVT. We had no information on the use of

anti-diabetic drugs. Differences in the distribution of diabetes treatment in both

cases and controls could be a source of bias. However, we believe this effect to be

limited. The model was adjusted for diabetes mellitus as a potential confounder.

Our study has several limitations. First, the control group consisted of patients

with complaints of their legs instead of healthy controls. Consequently, it may be

possible that glucose levels were increased in the controls because of an underlying

disease such as infection. However, this would have led to an underestimation of

the association between glucose and DVT. Second, the glucose levels were

measured on admission, and it was unknown whether these were fasting or non-

fasting samples. However, owing to the study design there were no differences

between cases and controls with respect to the time of presentation and larger

dispersion of glucose levels would therefore have affected cases as much as controls.

Third, citrated plasma is not the plasma of choice for determining glucose and CRP

levels, because of the dilution with sodium citrate. Also, sodium citrate does not

inhibit ex vivo glycolysis. However, we corrected glucose levels for the dilution and

the obtained blood samples were centrifuged and stored within one hour thereby

minimizing glycolysis. Again, as blood samples of both cases and controls were

obtained and processed in the same manner, the results for glucose levels were

��

38

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

affected equally. Because this is the first report on the association between stress

hyperglycaemia and VTE, confirmation in other studies is required.

In conclusion, our findings suggest that higher glucose levels are a risk factor for

the development of venous thrombosis. It will therefore be of importance to analyse

whether disturbed glucose homeostasis persists after the acute phase of venous

thrombosis.

ACKNOWLEDGEMENTS

We would like to acknowledge M.M. Levi for assessing the manuscript and for his

significant intellectual contribution. In addition we would like to acknowledge the

„vasculists‰, a group of medical students responsible for execution of this study,

ranging from design of the study to recruitment and data collection.

��

39

REFERENCES

(1) Cushman M, Kuller LH, Prentice R, Rodabough RJ, Psaty BM, Stafford RS et al. Estrogen plus progestin and risk of venous thrombosis. JAMA 2004; 292(13):1573-1580.

(2) Rosendaal FR. Venous thrombosis: a multicausal disease. Lancet 1999; 353(9159):1167-1173.

(3) Lensing AW, Prandoni P, Prins MH, Büller HR. Deep-vein thrombosis. Lancet 1999; 353(9151):479-485.

(4) Spencer FA, Emery C, Lessard D, Anderson F, Emani S, Aragam J et al. The Worcester Venous Thromboembolism study: a population-based study of the clinical epidemiology of venous thromboembolism. J Gen Intern Med 2006; 21(7):722-727.

(5) Eliasson A, Bergqvist D, Bjorck M, Acosta S, Sternby NH, Ogren M. Incidence and risk of venous thromboembolism in patients with verified arterial thrombosis: a population study based on 23,796 consecutive autopsies. J Thromb Haemost 2006; 4(9):1897-1902.

(6) Sorensen HT, Horvath-Puho E, Pedersen L, Baron JA, Prandoni P. Venous thromboembolism and subsequent hospitalisation due to acute arterial cardiovascular events: a 20-year cohort study. Lancet 2007; 370(9601):1773-1779.

(7) Bruno A, Levine SR, Frankel MR, Brott TG, Lin Y, Tilley BC et al. Admission glucose level and clinical outcomes in the NINDS rt-PA Stroke Trial. Neurology 2002; 59(5):669-674.

(8) Laakso M. Hyperglycemia and cardiovascular disease in type 2 diabetes. Diabetes 1999; 48(5):937-942.

(9) Malmberg K, Ryden L, Hamsten A, Herlitz J, Waldenstrom A, Wedel H. Effects of insulin treatment on cause-specific one-year mortality and morbidity in diabetic patients with acute myocardial infarction. DIGAMI Study Group. Diabetes Insulin-Glucose in Acute Myocardial Infarction. Eur Heart J 1996; 17(9):1337-1344.

(10) Ay C, Tengler T, Vormittag R, Simanek R, Dorda W, Vukovich T et al. Venous thromboembolism a manifestation of the metabolic syndrome. Haematologica 2007; 92(3):374-380.

(11) Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005; 112(17):2735-2752.

(12) Petrauskiene V, Falk M, Waernbaum I, Norberg M, Eriksson JW. The risk of venous thromboembolism is markedly elevated in patients with diabetes. Diabetologia 2005; 48(5):1017-1021.

(13) Grant PJ. Diabetes mellitus as a prothrombotic condition. J Intern Med 2007; 262(2):157-172.

(14) Stegenga ME, van der Crabben SN, Levi M, de Vos AF, Tanck MW, Sauerwein HP et al. Hyperglycemia stimulates coagulation, whereas hyperinsulinemia impairs fibrinolysis in healthy humans. Diabetes 2006; 55(6):1807-1812.

(15) Wells PS, Anderson DR, Bormanis J, Guy F, Mitchell M, Gray L et al. Value of assessment of pretest probability of deep-vein thrombosis in clinical management. The Lancet 350(9094):1795-1798.

(16) Kraaijenhagen RA, Piovella F, Bernardi E, Verlato F, Beckers EA, Koopman MM et al. Simplification of the diagnostic management of suspected deep vein thrombosis. Arch Intern Med 2002; 162(8):907-911.

(17) How to read clinical journals: IV. To determine etiology or causation. Can Med Assoc J 1981; 124(8):985-990.

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12

13

14

15


(19) Khechai F, Ollivier V, Bridey F, Amar M, Hakim J, de PD. Effect of advanced glycation end product-modified albumin on tissue factor expression by monocytes. Role of oxidant stress and protein tyrosine kinase activation. Arterioscler Thromb Vasc Biol 1997; 17(11):2885-2890.

(20) Min C, Kang E, Yu SH, Shinn SH, Kim YS. Advanced glycation end products induce apoptosis and procoagulant activity in cultured human umbilical vein endothelial cells. Diabetes Res Clin Pract 1999; 46(3):197-202.


(22) Ageno W, Becattini C, Brighton T, Selby R, Kamphuisen PW. Cardiovascular risk factors and venous thromboembolism: a meta-analysis. Circulation 2008; 117(1):93-102.

(23) Caballero AE, Delgado A, guilar-Salinas CA, Herrera AN, Castillo JL, Cabrera T et al. The Differential Effects of Metformin on Markers of Endothelial Activation and Inflammation in Subjects with Impaired Glucose Tolerance: A Placebo-Controlled, Randomized Clinical Trial. J Clin Endocrinol Metab 2004; 89(8):3943-3948.

(24) De Jager J, Kooy A, Lehert P, Bets D, Wulffele MG, Teerlink T et al. Effects of short-term treatment with metformin on markers of endothelial function and inflammatory activity in type 2 diabetes mellitus: a randomized, placebo-controlled trial. J Intern Med 2005; 257(1):100-109.


(26) Poort SR, Rosendaal FR, Reitsma PH, Bertina RM. A common genetic variation in the 3'-untranslated region of the prothrombin gene is associated with elevated plasma prothrombin levels and an increase in venous thrombosis. Blood 1996; 88(10):3698-3703.

(27) Ishihara M, Inoue I, Kawagoe T, Shimatani Y, Kurisu S, Hata T et al. Is admission hyperglycaemia in non-diabetic patients with acute myocardial infarction a surrogate for previously undiagnosed abnormal glucose tolerance? Eur Heart J 2006; 27(20):2413-2419.


(29) Whitcomb BW, Pradhan EK, Pittas AG, Roghmann MC, Perencevich EN. Impact of admission hyperglycemia on hospital mortality in various intensive care unit populations. Crit Care Med 2005; 33(12):2772-2777.



��

41

Hip surgery sequentially induces stress

hyperglycaemia and activates coagulation

J Hermanides, R Huijgen, CP Henny, N Haj

Mohammad, JBL Hoekstra, MM Levi and JH DeVries

The Netherlands Journal of Medicine, 2009 June;67(6):226-9

ABSTRACT

Background A frequent complication of orthopaedic procedures is venous

thromboembolism (VTE). Hyperglycaemia has been shown to activate the

coagulation system and is associated with postoperative morbidity and mortality.

Therefore we hypothesised that glucose levels increase during orthopaedic surgery

and are associated with an activation of the coagulation system.

Methods Nine adult patients undergoing elective hip replacement were included.

Venous blood samples were taken before, during and after surgery. Plasma glucose

levels, factor VIII clotting activity (fVIII:c), von Willebrand ristocetin cofactor

activity, von Willebrand factor antigen and prothrombin fragment 1+2 were

measured.

Results Immediately after induction of anaesthesia, plasma glucose levels started

to increase until the second day postoperatively (peak 8.0 mmol/l). After seven

weeks glucose values had returned to baseline (6.1 mmol/l), p<0.001 with ANOVA.

All coagulation parameters increased during surgery, subsequent to the rise in

glucose. The change in mean FVIII:c and von Willebrand ristocetin cofactor activity

significantly correlated with mean glucose values.

Conclusions These observations indicate that total hip replacement surgery causes

an increase in glucose levels that precedes the proportional rise of the measured

coagulation parameters. This suggests a possible role of glucose in the activation of

the coagulation system during hip surgery.

��

44

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

INTRODUCTION

A frequent complication of surgical procedures is venous thromboembolism (VTE),

manifesting as deep venous thrombosis or pulmonary embolism.1 Especially after

orthopaedic surgery the incidence of postoperative symptomatic VTE is high,

occurring in 1.5 to 10% of the patients despite adequate anticoagulant

prophylaxis.2 Risk factors for the development of VTE after surgery include

underlying malignancy and advanced age.2 In an experimental setting

hyperglycaemia has been shown to activate the coagulation system in healthy

volunteers, in particular by stimulating the tissue factor pathway.3;4 This is of

interest since hyperglycaemia in response to surgery is a common finding.5-7

Counter regulatory hormone action initiated by the surgical trauma can induce

„stress hyperglycaemia‰, even without known diabetes mellitus.8 Recently, pre-

operative hyperglycaemia has been identified as a risk factor for pulmonary

embolism, independent of diabetes mellitus.9

Thus, we hypothesised that glucose levels increase during orthopaedic surgery and

may contribute and therefore be related to an activation of the coagulation system.

MATERIALS AND METHODS

Study design and population

We performed an observational study, assessing the correlation between

perioperative changes in plasma glucose levels and a number of coagulation

parameters. Adult patients undergoing elective hip replacement surgery were

recruited from the Department of Orthopaedic Surgery at the Academic Medical

Centre, University of Amsterdam, the Netherlands. Exclusion criteria were:

previous VTE, revision hip replacement, use of �-blockers, known diabetes

mellitus, inability or unwillingness to give written informed consent, and inability

to be followed up. The study was approved by the Medical Ethics Commission of the

Academic Medical Centre, University of Amsterdam and written informed consent

was obtained from each patient.

Data collection

Clinical data including date of birth, sex, height, weight, blood and indication for

hip replacement were recorded. Venous blood samples were taken at 14 set points

in time (Figure 1). The samples on the day of surgery were taken in the fasting

��

45

state. The samples on the other days (non-fasting) were taken between 8 and 10 am

to exclude the influence of circadian fluctuations on the haemostatic parameters.

Laboratory determinations

Blood was collected in ice-cooled tubes of 2.7 ml containing 0.109 M trisodium

citrate, which were centrifuged within one hour. The citrate samples were

centrifuged at 3000 rpm at 4 °C for 20 minutes and the separated plasma again at

4000 rpm and 4 °C for five minutes and stored immediately at -80ÀC. Plasma

glucose was measured using the HK/G-6PD method (Roche/Hitachi, Basel,

Switzerland) and corrected for the 10% dilution with sodium citrate. Factor VIII

clotting activity (FVIII:c) and von Willebrand ristocetin cofactor activity

(vWF:RiCof) assays were performed on an automated coagulation analyser

(Behring Coagulation System) with reagents and protocols from the manufacturer

(Dade Behring, Marburg, Germany), and are expressed as a percentage of reference

activity. Measurements of prothrombin fragment 1+2 (F1+2) (Dade Behring,

Marburg, Germany) and von Willebrand factor antigen (vWF:Ag) (antibodies from

Dako, Glostrup, Denmark) were performed by ELISA.

Statistical analyses

Mean glucose levels perioperatively and during surgery were plotted against time.

Sequential glucose values were analysed with the repeated measurements ANOVA.

For the ANOVA analyses, missing values per patient were linearly interpolated.

Post-hoc testing was performed using the paired t-test with HolmÊs sequential

Bonferroni correction, comparing the non-fasting samples with the baseline mean

glucose values and the fasting samples with the pre-induction sample. To assess

the correlation between mean glucose levels and mean FVIII:c, vWF:Ag,

vWB:RiCof., F1+2 values we calculated the correlation coefficient . Correlation was

considered relevant in case of r >0.5. The level of significance was p<0.05. RESULTS

During the study period 17 patients scheduled for elective hip replacement surgery

were evaluated. Nine patients met the inclusion criteria and provided written

informed consent. The investigated cohort included five males and four females

with a mean age of 63 years (SD 22 years) and mean BMI of 27.7 kg/m² (SD 2.8).

Indication for hip surgery was coxarthrosis in seven patients, one case of idiopathic

femur head necrosis and prednisone induced femur head necrosis in one patient.

��

46

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

This last subject had stopped taking prednisone two years before inclusion in this

study. Low-molecular weight heparin (LMWH), starting the day before surgery,

was used as thromboprofylaxis in five patients. One patient started LMWH the day

after surgery and three patients used oral anticoagulants.

Glucose values

Mean plasma glucose levels and the number of successful samples per time point

are depicted in Figure 1 and Table 1. The repeated measurements ANOVA for all

sequential glucose samples was p<0.001. Missing values are due to occlusion of the

intravenous sampling catheter during surgery. Mean plasma glucose levels

changed significantly during surgery as compared with pre-induction. Directly after

induction of anaesthesia glucose levels increased from 5.6 to 6.0 mmol/l (p=0.002).

Two hours after surgery, glucose levels were still significantly increased as

compared with pre-induction (7.3 mmol/l, p=0.01). Postoperatively non-fasting

glucose levels peaked at the second postoperative day and remained increased up to

the 4th day after surgery as compared to baseline non-fasting mean glucose values.

After seven weeks non-fasting glucose levels returned to baseline values.

Coagulation factors

The mean levels of FVIII:c, vWF:Ag, vWF:RiCof. and F1+2 are presented in Table

1. All values increased significantly during surgery. FVIII:c and F1+2 returned to

baseline values seven weeks after surgery. However, vWF:Ag and vWF:RiCof

remained elevated. In Figure 1 the increase in mean levels of coagulation factors

per time point are shown. In contrast with the steep increase in glucose levels after

placement of the prosthesis cup (S4), vWF:Ag, vWF:RiCof and FVIII:c levels

somewhat lagged behind the glucose pattern. Both FVIII:c (r=0.69, p=0.03) and

vWF:RiCof (r=0.69, p=0.006) were significantly correlated with the mean glucose

levels. Correlation coefficients of F1+2 (r=0.58, p=0.07) and vWF:Ag (r=0.59,

p=0.06) had borderline significance.

��

47

Tim

e

Glu

cose

(mm

ol/l)

F1

+2 (n

mol

/L)

vWF:

Ag

(IU/d

L)

vWB

:RiC

of (I

U/d

L)

FVIII

:c (I

U/d

L)

Surg

ery

-1 d

ay

Bas

elin

e 6.

2 (±

1.0)

1.

0 (±

0.3)

11

6.8

(±37

.2)

101.

8 (±

34.3

) 11

3.1

(±25

.3)

Bef

ore

indu

ctio

n

S1

5.6

(±0.

4)

1.1

(±0.

5)

115.

4 (±

27.5

) 10

5.4

(±36

.7)

100.

9 (±

23.7

) A

fter i

nduc

tion

S2

6.0(

±0.4

) 1.

0 (±

0.5)

11

4.0

(±29

.0)

101.

0 (±

35.2

) 10

1.0

(±24

.8)

Afte

r ski

n in

cisi

on

S3

6.0

(±0.

7)

1.0

(±0.

4)

103.

6 (±

28.2

) 91

.1 (±

39.6

) 89

.3 (±

19.0

) A

fter p

laci

ng c

up

S4

6.9

(±0.

6)

1.3

(±0.

4)

102.

1 (±

46.2

) 10

4.5

(±59

.8)

92.5

(±38

.4)

Afte

r pl

acin

g pr

osth

esis

S

5 7.

2 (±

0.6)

2.

0 (±

0.8)

12

2.0

(±34

.8)

140.

4 (±

47.4

) 11

6.3

(±28

.8)

2 ho

urs

afte

r sur

gery

S

6 7.

3 (±

1.4)

2.

6 (±

1.5)

12

3.6

(±50

.0)

138.

6 (±

58.6

) 11

0.4

(±36

.6)

Surg

ery

+1 d

ay

D1

7.8

(±1.

7)

1.3

(±0.

4)

151.

4 (±

48.4

) 17

5.9

(±54

.6)

124.

3 (±

28.7

) Su

rger

y +2

day

s D

2 8.

0 (±

1.7)

1.

4 (±

0.4)

19

5.4

(±42

.8)

240.

6 (±

52.1

) 16

3.3

(±24

.5)

Surg

ery

+3 d

ays

D3

7.9

(±1.

2)

1.6

(±0.

4)

228.

0 (±

69.3

) 26

4.0

(±87

.8)

167.

7 (±

37.0

) Su

rger

y +4

day

s D

4 6.

9 (±

1.4)

1.

7 (±

0.5)

23

1.4

(±54

.7)

250.

6 (±

71.2

) 17

1.3

(±37

.0)

Surg

ery

+49

days

Fi

nal

6.1

(±0.

8)

1.1

(±0.

7)

164.

6 (±

57.9

) 14

9.2

(±62

.7)

128.

4 (±

35.2

)

Tabl

e 1-

Mea

n le

vels

of g

luco

se a

nd c

oagu

latio

n pa

ram

eter

s pe

r tim

e po

int,

disp

laye

d w

ith m

ean

± S

D

Fact

or V

III c

lotti

ng a

ctiv

ity=F

VIII

:c, v

on W

illeb

rand

rist

ocet

in c

ofac

tor a

ctiv

ity =

vWF:

RiC

of

prot

hrom

bin

fragm

ent 1

+2=F

1+2,

von

Will

ebra

nd fa

ctor

ant

igen

= vW

F:A

g

��

48

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

F1+2

Baseline S1 S2 S3 S4 S5 S6 D1 D2 D3 D4 Final

F1

+ F2

(nm

ol/l)

0,5

1,0

1,5

2,0

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Figure 1- Mean per- and postoperative glucose levels (2 upper graphs are identical) and coagulation parameters with 95% CI.+p< 0.05 as compared to baseline, *p< 0.05 as compared to S1 (glucose fasting samples only) Repeated measurements ANOVA: p<0.001 for glucose, vWF:ag, vWF:RiCof, F1+2 and FVIII:c

Sampling time points Baseline (n=9) Baseline: surgery -1 day S6 (n=8) 2 hours after surgery S1 (n=7) Before induction D1 (n=9) Surgery +1 day S2 (n=8) After induction D2 (n=7) Surgery +2 days S3 (n=8) After skin incision D3 (n=9) Surgery +3 days S4 (n=8) After placing cup D4 (n=7) Surgery +4 days S5 (n=8) After placing prosthesis Final (n=9) Surgery +49 days

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DISCUSSION

In this observational study we have demonstrated how plasma glucose levels

increase in response to hip replacement surgery. Following this rise in glucose,

FVIII:c, vWF:Ag, vWF:RiCof and F1+2 levels also increased. These changes in

mean glucose levels and mean levels of coagulation parameters during hip

replacement surgery were closely correlated. All measured parameters remained

elevated for several days up to seven weeks postoperatively.

The link between glucose increase and activation of the coagulation system has

been established before. Stegenga and co-workers showed in clamp studies that

hyperglycaemia leads to up regulation of coagulation parameters in healthy

volunteers, measured by soluble tissue factor and thrombin-antithrombin

complexes.3 Furthermore, exposure to prolonged hyperglycaemia (diabetes

mellitus) is an established risk factor for VTE.10 Coagulation activation by

hyperglycaemia may be explained by mechanisms such as glycocalyx damage, non-

enzymatic glycation, or the development of increased oxidative stress.10;11 From the

present study we cannot conclude whether increasing glucose levels directly

activates the coagulation system. Activation of coagulation could be due to other

causes, such as vascular damage and bleeding induced by the surgery. However, it

is of interest to note that the rise in glucose levels precedes the increase in the

measured coagulation parameters and is closely correlated with these parameters.

Our study was limited by the small sample size (n=9). This was therefore a pilot

study, to determine whether hip surgery does indeed cause hyperglycaemia and

whether this is associated with activation of the coagulation system. To investigate

the direct influence of perioperative glucose levels on coagulation parameters, a

randomised controlled trial is needed, comparing an intervention group in which

normoglycaemia is maintained with untreated controls, as in our study population.

A consideration in interpreting the results is the possible lowering of coagulation

parameters in response to the use of thromboprophylaxis and anticoagulants,

especially the use of oral anticoagulants in three patients and the influence on

F1+2. However, one would have expected an even larger increase in the coagulation

parameters when no anticoagulants or thromboprophylaxis were used, and this is

therefore not likely to have biased the results.

Glucose values were measured both fasting (preoperatively) and non-fasting

(perioperatively). In the analyses we have attempted to overcome this limitation by

comparing the non-fasting samples to the baseline mean glucose values and the

fasting samples with the pre-induction sample. It should also be noted that the

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medium in which the blood was collected, trisodium citrate, is not the medium of

choice for glucose measurements. We have however corrected for the dilution factor.

In addition, samples were stored in ice-cooled tubes which were centrifuged

immediately. This limits possible glycolysis.

In conclusion, our observations indicate that total hip replacement surgery causes

glucose levels to increase, prior to a rise in the concentration of the measured

coagulation parameters. This suggests a possible role of glucose in the activation of

the coagulation system during hip surgery. Confirmation of this observation in

interventional studies is needed.

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REFERENCES


(2) Geerts WH, Bergqvist D, Pineo GF, Heit JA, Samama CM, Lassen MR et al. Prevention of venous thromboembolism: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest 2008; 133(6 Suppl):381S-453S.

(3) Stegenga ME, van der Crabben SN, Levi M, de Vos AF, Tanck MW, Sauerwein HP et al. Hyperglycemia Stimulates Coagulation, Whereas Hyperinsulinemia Impairs Fibrinolysis in Healthy Humans. Diabetes 2006; 55(6):1807-1812.

(4) Rao AK, Chouhan V, Chen X, Sun L, Boden G. Activation of the tissue factor pathway of blood coagulation during prolonged hyperglycemia in young healthy men. Diabetes 1999; 48(5):1156-1161.

(5) Gandhi GY, Nuttali GA, Abel MD, Mullany CJ, Schaff HV, Williams BA et al. Intraoperative Hyperglycemia and Perioperative Outcomes in Cardiac Surgery Patients. Mayo Clinic Proceedings 2005; 80(7):862-866.

(6) Thorell A, Efendic S, Gutniak M, Haggmark T, Ljungqvist O. Insulin resistance after abdominal surgery. Br J Surg 1994; 81(1):59-63.

(7) Engin A, Bozkurt BS, Ersoy E, Oguz M, Gokcora N. Stress hyperglycemia in minimally invasive surgery. Surg Laparosc Endosc 1998; 8(6):435-437.

(8) Wolfe RR, Martini WZ. Changes in Intermediary Metabolism in Severe Surgical Illness. World Journal of Surgery 2000; 24(6):639-647.

(9) Mraovic B, Hipszer BR, Epstein RH, Pequignot EC, Parvizi J, Joseph JI. Preadmission Hyperglycemia is an Independent Risk Factor for In-Hospital Symptomatic Pulmonary Embolism After Major Orthopedic Surgery. The Journal of Arthroplasty In Press, Corrected Proof.



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Stress-induced hyperglycaemia and venous

thromboembolism following total hip or total

knee arthroplasty

J Hermanides, DM Cohn, JH DeVries, PW Kamphuisen,

S Kuhls, M Homering, JBL Hoekstra, AW Lensing and

HR Büller


ABSTRACT

Background Stress-induced hyperglycaemia is common during orthopaedic surgery.

In addition, hyperglycaemia activates coagulation. Whether stress-induced

hyperglycaemia is associated with symptomatic or asymptomatic venous

thromboembolism (VTE) following orthopaedic surgery is unknown.

Methods We performed post-hoc analyses in the four RECORD studies (REgulation

of Coagulation in major Orthopedic surgery reducing the Risk of Deep venous

thrombosis and pulmonary embolism). Separate analyses were performed for

patients undergoing total hip or knee replacement. Outcome measures were

symptomatic VTE and „total VTE‰ (defined as the composite of symptomatic VTE,

asymptomatic DVT assessed by per protocol venography and all cause mortality).

Glucose levels were measured at day 0 (pre-surgery visit) and post-surgery on day

1, categorised into quartiles, based on the distribution in the respective cohorts.

The influence of glucose, adjusted for BMI, age, gender and diabetes mellitus on

VTE was assessed by logistic regression analyses.

Results A total of 12383 patients were eligible for assessment of symptomatic VTE

and 8512 patients were eligible for assessment of total VTE. Increased glucose

levels after total hip replacement were associated with total VTE; adjusted OR

highest versus lowest quartile 1.9 (95%CI 1.3 to 3.0). Furthermore, increase in

glucose levels during hip surgery was associated with total VTE (OR highest versus

lowest quartile 1.8 (95%CI 1.2 to 2.8). This was not observed in patients undergoing

total knee replacement.

Conclusion Stress-induced hyperglycaemia following total hip replacement was

associated with total VTE. This was not demonstrated in patients undergoing total

knee replacement, which is likely due to the surgical procedure.

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INTRODUCTION

Venous thromboembolism (VTE) may manifest as either deep venous thrombosis

(DVT) or pulmonary embolism (PE), or a combination of both. VTE affects 2-3

persons per 1000 inhabitants annually in Western Societies.1 Acquired risk factors,

inherited risk factors or both can be found in approximately 75% of patients with

VTE.2;3 This implies, that in approximately 25% of patients with VTE no risk

factors can be identified.

In several reports, acute hyperglycaemia was shown to cause coagulation activation

in an experimental setting as demonstrated by increased levels of thrombin-

antithrombin complexes and soluble tissue factor.4;5 During acute illness or

surgery, a phenomenon called stress hyperglycaemia may occur independently of

the presence of known diabetes.6 Indeed, hip surgery has been shown to induce

hyperglycaemia peaking the days after the procedure, followed by post-operative

procoagulant activity peaking on the third and fourth postoperative days.7

Hyperglycaemia at presentation has recently been shown to be associated with

VTE in an outpatient population.8 Whether stress-hyperglycaemia is associated

with VTE in hospitalised patients, such as orthopaedic patients, has not been

previously addressed. The associated risk of hyperglycaemia could be of particular

interest in surgical patients, since the coagulation system is already activated due

to the surgical procedure.9 Furthermore, VTE is a frequently occurring

complication following orthopaedic surgery. Despite anticoagulant prophylaxis,

symptomatic VTE occurs in approximately 2% of all patients undergoing hip or

knee replacements.10 In this study we aimed to assess whether pre- and

postoperative hyperglycaemia is associated with (a)symptomatic VTE.

METHODS

Patients

For the present analysis we made use of the large phase III trial programme:

„REgulation of Coagulation in major Orthopaedic surgery reducing the Risk of

Deep venous thrombosis and pulmonary embolism studies‰ (RECORD 1-4).11-14

These trials compared the efficacy and safety of Rivaroxaban (a direct factor Xa

inhibitor) 10 mg qd relative to standard treatment with Enoxaparin 40 mg qd 11-13

or 30 mg b.i.d.14 Because efficacy and safety outcomes, as well as independent,

blinded adjudication committees were the same in all RECORD studies, the studies

allowed pooling of the data. Patients were eligible if they were aged 18 years or

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older and were scheduled for elective total hip or total knee arthroplasty. Major

exclusion criteria included active bleeding or high risk of bleeding, significant liver

disease, anticoagulant therapy that could not be interrupted, use of HIV-protease-

inhibitors and a contraindication for prophylaxis with enoxaparin or a condition

that would require a dose adjustment for enoxaparin. Detailed in- and exclusion

criteria were reported in the original publications of the RECORD studies.11-14

Procedures

Deep-vein thrombosis was assessed per protocol, by ascending, bilateral

venography with a standardised technique.15 This was performed at day 36 (range

32 to 40) in patients with total hip replacements (RECORD 1 and 2)11;12 and at day

13 (range 11 to 15) in patients with total knee replacement (RECORD 3 and 4).13;14

Symptomatic VTE was objectively confirmed by standard imaging techniques.

Treatment duration was 5 weeks for RECORD study 1 and 2 (including the 3 weeks

of placebo treatment in the comparator group). Treatment duration in RECORD 3

and 4 was 2 weeks. Patients were followed up for 30 to 35 days after the last dose.

Glucose levels were sampled upon admission (day 0) and 6 hours after surgery (day

1). All samples were analysed in the central laboratory.

Outcomes

The aim of this study was to assess whether hyperglycaemia (measured pre- and

post-surgery) was associated with an increased risk of VTE. We assessed the effect

of hyperglycaemia on symptomatic VTE (until Day 42 in hip replacement and Day

17 in knee replacement) in the participants included in the safety population of the

four RECORD studies. The safety population was defined as patients who received

at least one dose of study medication. Furthermore, we investigated the association

between hyperglycaemia and total VTE, which was defined as the composite

outcome of symptomatic VTE and asymptomatic DVT as detected by venography

and all cause mortality. The analyses for total VTE were performed in the modified

intention to treat (mITT) population, comprising all patients from the safety

analyses with surgery and adequate assessment of both proximal and distal veins

on venography. Only events occurring in the planned treatment phase of the

studies were considered.

Statistical Analyses

Descriptive results are presented as mean � standard deviation (SD) or median

with interquartile range (IQR). To investigate the association of hyperglycaemia

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and VTE multiple logistic regression analyses were performed separately for

patients undergoing total hip and total knee arthroplasty. Separate analyses were

carried out to assess the influence of glucose on Day 0 (pre-surgery) and Day 1

(post-surgery), and of the difference in glucose levels (Day 1-Day 0) on symptomatic

VTE and total VTE. In all analyses glucose levels were categorised into quartiles,

based on the distribution in the respective cohorts. Adjusted odds ratios (OR) for

categorised glucose levels were derived from a logistic regression model with

variables for glucose, treatment group, study and other relevant covariates (BMI

(<25, 25 to 35, >=35), age (<65, 65 to 75, >75 years), gender, concomitant known

diabetes mellitus). The reported p-value (from likelihood ratio test on 3 d.f.) for

glucose refers to a global test of differences in outcome rates with categorised

glucose levels. All statistical analyses were performed in SAS version 9.1 (SAS

institute, Cary, NC).

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RESULTS

Baseline characteristics

The safety population of the four RECORD studies consisted of 12383 patients, of

whom 6890 underwent total hip arthroplasty and 5493 total knee arthroplasty,

respectively. The baseline characteristics of both cohorts are shown in Table 1.

High BMI and female gender were more common in the knee arthroplasty cohort

(mean BMI 30 kg/m2 vs. 28 kg/m2, and 66% vs. 55%, respectively). Mean age was

slightly higher in the knee surgery cohort: 66 years vs. 63 years. The mITT

population comprised 8512 patients (hip replacement 4886 patients, knee

replacement 3626 patients).

Table 1-baseline characteristics of both cohorts (safety population) VTE=venous thromboembolism, SD=standard deviation

Total hip arthroplasty (RECORD 1&2) (n=6890)

Total knee arthroplasty (RECORD 3&4) (n=5493)

Age – years (mean ± SD) 63 (12) 66 (10) Sex, female n (%) 3780 (55%) 3652 (66%) Body-mass index – kg/m² (mean ± SD) 28 (5) 30 (6) History of VTE, n (%) 132 (2%) 156 (3%) Previous orthopaedic surgery, n (%) 1447 (21%) 1690 (31%) Type of surgery, n (%) -Primary -Revision -Missing/no surgery

6562 (95%) 254 (4%) 74 (1%)

5329 (97%) 119 (2%) 45 (1%)

Type of anesthesia -General only -General and regional -Regional only -Missing -None

1983 (29%) 619 (9%) 4215 (61%) 73 (1%) 0 (0%)

1034 (19%) 1126 (21%) 3292 (60%) 0 (0%) 41 (1%)

Duration of surgery – minutes (mean ± SD) 98 (48) 99 (40) Time to mobilisation – days (mean ± SD) 2.2 (4) 1.6 (2) Ethnic origin, n (%) -White -Asian -Hispanic -Black -Other/missing

5687 (83%) 498 (7%) 329 (5%) 103 (1%) 273 (4%)

4037 (73%) 736 (13%) 353 (6%) 181 (3%) 186 (3%)

Randomisation allocation, n (%) -Rivaroxaban -Comparator (enoxaparin)

3437 (50%) 3453 (50%)

2746 (50%) 2747 (50%)

On glucose lowering therapy, n (%) 407 (6%) 654 (12%) Diabetes Mellitus n (%) 532 (8%) 890 (16%) Glucose measured on Day 0, n (%) 6844 (99%) 5433 (99%) Glucose measured on Day 1, n (%) 6680 (97%) 5323 (97%) Glucose measured on Day 0 & Day 1, n (%) 6648 (96%) 5272 (96%)

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Total hip arthroplasty

The median pre-operative glucose level at day 0 was 5.4 mmol/l (IQR 4.9 to 6.3,

Table 2a). Glucose levels on Day 0 were not associated with either symptomatic

VTE or total VTE (p=0.22 and p=0.31 respectively, see Table 2a).

At day 1 (post surgery), the median glucose level was 6.5 mmol/l (IQR 5.5 to 7.9,

Table 2b). Glucose levels in the highest quartile (>7.9 mmol/l) were associated with

both symptomatic VTE and total VTE, when compared to the lowest glucose

quartile (<5.5 mmol/l): adjusted OR were 2.3 (95% CI 0.9 to 6.0) and OR 1.9 (95%CI

1.3 to 3.0), respectively. The p-value of the Likelihood Ratio test for glucose on Day

1 was significant for both outcomes (p=0.04 for symptomatic VTE and p<0.0001 for

total VTE). In addition, the amount of increase of the glucose level between day 1

and day 0 was associated with total VTE (p=0.01, see Table 2c). Median difference

of glucose levels between these two time points was 0.9 mmol/l (IQR -0.3 to 2.2).

The highest quartile of this difference was associated with a nearly twofold

increased risk for total VTE compared to the lowest quartile (adjusted OR 1.8,

95%CI 1.2 to 2.8). For symptomatic VTE no association with the difference of

glucose on day 1 and day 0 could be observed (p=0.24, Table 2c).

Total knee arthroplasty

The distribution in glucose levels at day 0 and day 1 in patients undergoing knee

arthroplasty was comparable to that in the hip surgery cohort. Median glucose was

5.5 mmol/l (IQR 4.9 to 6.4) at day 0 and 6.8 mmol/l (IQR 5.7 to 8.3) at day 1 (Table

3a and 3b).

For glucose levels measured at day 0, there was no association between glucose

levels and symptomatic VTE (p=0.46, Table 3a). A non-significant trend was

observed for total VTE at day 0 (Likelihood Ratio test: p=0.14). The adjusted OR for

highest versus lowest quartile was 1.4 (95%CI 1.03 to 2.0, Table 3a). Glucose levels

measured at day 1 were not significantly associated with symptomatic VTE:

adjusted OR 1.8 (95%CI 0.8 to 4.1, Table 3b). For total VTE, the adjusted OR for

highest versus lowest glucose quartile was 1.3 (95%CI 0.96 to 1.8, Table 3b).

No association was found between the increase in glucose levels (median difference

1.1 mmol/l, IQR -0.2 to 2.6) and VTE; adjusted OR for symptomatic VTE: 0.8

(95%CI 0.3 to 1.8) and for total VTE: 1.1 (95%CI 0.8 to 1.4, Table 3c).

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Table 2- odds ratio for glucose levels measured at day 0, day 1 and difference in glucose levels between day 1 and day 0 in relation to symptomatic VTE and total VTE in hip surgery patients (RECORD 1&2)

a) quartiles glucose levels measured at day 0* <4.9 �4.9 to 5.4 >5.4 to 6.3 >6.3 all patients (N) 1487 1713 2014 1630 symptomatic VTE (n) 5 13 11 6

crude OR (95%CI) 1 (ref) 2.3 (0.8 to 6.4) 1.6 (0.6 to 4.7) 1.1 (0.3 to 3.6) adjusted OR (95%CI) 1 (ref) 2.3 (0.8 to 6.5) 1.7 (0.6 to 4.9) 0.9 (0.3 to 3.2) LR test p=0.22

patients valid for the modified intention to treat analyses (N) 1051 1220 1449 1141 total VTE (n) 37 41 60 35


b) quartiles glucose levels measured at day 1* <5.5 �5.5 to 6.5 >6.5 to 7.8 >7.8 all patients (N) 1590 1722 1671 1697 symptomatic VTE (n) 6 4 6 17



crude OR (95%CI) 1 (ref) 0.9 (0.5 to 1.5) 1.0 (0.6 to 1.6) 2.3 (1.5 to 3.5) adjusted OR (95%CI) 1 (ref) 0.8 (0.5 to 1.4) 0.9 (0.5 to 1.4) 1.9 (1.3 to 3.0) LR test p<0.0001

c) quartiles difference in glucose levels day 1 - day 0* �-0.3 >-0.3 to 0.9 >0.9 to 2.2 >2.2 all patients (N) 1633 1655 1699 1661 symptomatic VTE (n) 5 6 8 14




VTE=venous thromboembolism; OR=Odds Ratio, LR test=Likelihood Ratio test for testing whether increase in glucose levels (in quartiles) has a significant influence on the occurrence of VTE. Total VTE is the composite of symptomatic VTE, asymptomatic DVT and all cause mortality. *mmol/l

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Table 3- odds ratio for glucose levels measured at day 0, day 1 and difference in glucose levels between day 1 and day 0 in relation to symptomatic VTE and total VTE in knee surgery patients (RECORD 3&4)

a) quartiles glucose levels measured at day 0* <4.9 �4.9 to 5.5 >5.5 to 6.4 >6.4 all patients (N) 1216 1480 1320 1417 symptomatic VTE (n) 12 18 11 19




b) quartiles glucose levels measured at day 1* <5.7 �5.7 to 6.8 >6.8 to 8.3 >8.3 all patients (N) 1476 1300 1266 1281 symptomatic VTE (n) 10 22 12 14




c) quartiles difference in glucose levels day 1 - day 0* �-0.2 >-0.2 to 1.1 >1.1 to 2.6 >2.6 all patients (N) 1331 1304 1347 1290 symptomatic VTE (n) 13 21 13 10




VTE=venous thromboembolism; OR=Odds Ratio, LR test=Likelihood Ratio test for testing whether increase in glucose levels (in quartiles) has a significant influence on the occurrence of VTE. Total VTE is the composite of symptomatic VTE, asymptomatic DVT and all cause mortality. *mmol/l

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DISCUSSION

This study shows an association between stress-induced hyperglycaemia and total

VTE in patients undergoing total hip arthroplasty. This was not observed in knee

surgical patients, in whom only glucose levels prior to surgery were associated with

total VTE.

An association between preoperatively elevated glucose levels and VTE in patients

undergoing orthopaedic surgery has previously been reported.16 Mraovic and

colleagues found that preadmission glucose levels exceeding 11.1 mmol/l

independently increased the risk of pulmonary embolism (OR 3.19, 95%CI 1.25 to

8.10) when compared to glucose levels of less than 6.1 mmol/l in patients who

underwent hip or knee arthroplasty. We did not find a relation between

preoperative hyperglycaemia and VTE in patients undergoing hip surgery.

However, 11.1 mmol/l is well above the cut-off value of the highest quartile in the

hip surgery cohort, 8.0 mmol/l, and is likely to reflect patients with undiagnosed

diabetes mellitus before surgery, a known risk factor for postsurgical

complications.17 We also assessed the influence of post-surgical stress-induced

glucose increase in relation to the development of VTE to investigate whether

stress-hyperglycaemia is an independent risk factor for VTE following orthopaedic

surgery.

Available pathophysiological evidence supports the relation between (acute)

hyperglycaemia and hypercoagulability. In patients with diabetes mellitus, the

concentration of several procoagulant factors are increased (fibrinogen, von

Willebrand antigen, factor VII antigen, factor VIII) and antifibrinolytic factors are

decreased, such as plasminogen activator inhibitor-1 (PAI-1).18 Furthermore, hip

surgery has been shown to induce hyperglycaemia, which preceded a rise of factor

VIII clotting activity, von Willebrand ristocetin cofactor activity, von Willebrand

factor antigen and prothrombin fragment 1+2.7 In healthy volunteers, acute

hyperglycaemia activates the coagulation system in an experimental setting.4

In this study, only those patients in the highest quartile of post-surgical stress-

induced hyperglycaemia were at increased risk for VTE, instead of a linear increase

in risk. This implies that glucose levels apparently need to exceed a certain

threshold to reach a significant association with VTE. Interestingly, the cut-off

between the third and fourth quartiles in hip surgery patients, 7.9 mmol/l, is close

to the classic cut-off for impaired glucose tolerance following the glucose tolerance

test, 7.8 mmol/l.19

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Hyperglycaemia was not clearly associated with VTE in patients undergoing total

knee replacement as in patients undergoing total hip replacement. This

discrepancy cannot be explained by major differences in patients characteristics, as

these were included as covariates in the regression model. The most important

factor may concern the surgical procedure. Total knee replacement is performed

with application of a tourniquet, occluding arterial and venous flow.20 Tourniquet

use is related to the formation of thrombi.21-24 It is thus possible that the effect of

hyperglycaemia is in part outweighed by other risk factors for VTE in patients

undergoing total knee arthroplasty.

Although preoperative glucose samples were drawn at predefined time points,

patients were not mandatory in the fasting state. We have, however, no reason to

assume that there was a difference in distribution in fasting/non-fasting samples in

patients with- or without VTE. Furthermore, this substudy involved a non-

prespecified analysis. Nevertheless, we have investigated a prospective database,

with a very large sample size. In addition, the most important assessments, VTE

and glucose, were collected prospectively and comprehensively. The outcome „total

VTE‰ not only included symptomatic VTE and asymptomatic DVT, but also „all

cause mortality‰. As the number of subjects who died during the treatment phase is

very low in the four RECORD studies (n=23/8512, 0.3%), it is not likely that the

inclusion of the latter outcome measure affected the results.

CONCLUSION

Stress-induced post-surgical hyperglycaemia is associated total VTE following total

hip replacement. Likely due to the surgical procedure, this was not found in

patients undergoing total knee arthroplasty. Future studies should assess whether

the risk of VTE can be decreased by glucose lowering therapy in patients with

stress-induced hyperglycaemia.

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REFERENCES

(1) Naess IA, Christiansen SC, Romundstad P, Cannegieter SC, Rosendaal FR, Hammerstrom J. Incidence and mortality of venous thrombosis: a population-based study. J Thromb Haemost 2007; 5(4):692-699.

(2) Lensing AW, Prandoni P, Prins MH, Büller HR. Deep-vein thrombosis. Lancet 1999; 353(9151):479-485.


(4) Stegenga ME, van der Crabben SN, Levi M, de Vos AF, Tanck MW, Sauerwein HP et al. Hyperglycemia stimulates coagulation, whereas hyperinsulinemia impairs fibrinolysis in healthy humans. Diabetes 2006; 55(6):1807-1812.

(5) Stegenga ME, van der Crabben SN, Blumer RM, Levi M, Meijers JC, Serlie MJ et al. Hyperglycemia enhances coagulation and reduces neutrophil degranulation, whereas hyperinsulinemia inhibits fibrinolysis during human endotoxemia. Blood 2008; 112(1):82-89.


(7) Hermanides J, Huijgen R, Henny CP, Mohammad NH, Hoekstra JB, Levi M et al. Hip surgery sequentially induces stress hyperglycaemia and activates coagulation. Neth J Med 2009; 67(6):226-229.

(8) Hermanides J, Cohn DM, Devries JH, Kamphuisen PW, Huijgen R, Meijers JC et al. Venous thrombosis is associated with hyperglycemia at diagnosis: a case-control study. J Thromb Haemost 2009; 7(6):945-949.

(9) Galster H, Kolb G, Kohsytorz A, Seidlmayer C, Paal V. The pre-, peri-, and postsurgical activation of coagulation and the thromboembolic risk for different risk groups. Thromb Res 2000; 100(5):381-388.

(10) Warwick D, Friedman RJ, Agnelli G, Gil-Garay E, Johnson K, FitzGerald G et al. Insufficient duration of venous thromboembolism prophylaxis after total hip or knee replacement when compared with the time course of thromboembolic events: findings from the Global Orthopaedic Registry. J Bone Joint Surg Br 2007; 89(6):799-807.

(11) Eriksson BI, Borris LC, Friedman RJ, Haas S, Huisman MV, Kakkar AK et al. Rivaroxaban versus enoxaparin for thromboprophylaxis after hip arthroplasty. N Engl J Med 2008; 358(26):2765-2775.

(12) Kakkar AK, Brenner B, Dahl OE, Eriksson BI, Mouret P, Muntz J et al. Extended duration rivaroxaban versus short-term enoxaparin for the prevention of venous thromboembolism after total hip arthroplasty: a double-blind, randomised controlled trial. Lancet 2008; 372(9632):31-39.

(13) Lassen MR, Ageno W, Borris LC, Lieberman JR, Rosencher N, Bandel TJ et al. Rivaroxaban versus enoxaparin for thromboprophylaxis after total knee arthroplasty. N Engl J Med 2008; 358(26):2776-2786.

(14) Turpie AG, Lassen MR, Davidson BL, Bauer KA, Gent M, Kwong LM et al. Rivaroxaban versus enoxaparin for thromboprophylaxis after total knee arthroplasty (RECORD4): a randomised trial. Lancet 2009; 373(9676):1673-1680.

(15) Rabinov K, Paulin S. Roentgen diagnosis of venous thrombosis in the leg. Arch Surg 1972; 104(2):134-144.


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(17) Marchant MH, Jr., Viens NA, Cook C, Vail TP, Bolognesi MP. The impact of glycemic control and diabetes mellitus on perioperative outcomes after total joint arthroplasty. J Bone Joint Surg Am 2009; 91(7):1621-1629.


(19) Diagnosis and classification of diabetes mellitus. Diabetes Care 2008; 31 Suppl 1:S55-S60.

(20) Sharrock NE, Go G, Sculco TP, Ranawat CS, Maynard MJ, Harpel PC. Changes in circulatory indices of thrombosis and fibrinolysis during total knee arthroplasty performed under tourniquet. J Arthroplasty 1995; 10(4):523-528.

(21) Parmet JL, Horrow JC, Pharo G, Collins L, Berman AT, Rosenberg H. The incidence of venous emboli during extramedullary guided total knee arthroplasty. Anesth Analg 1995; 81(4):757-762.

(22) Parmet JL, Horrow JC, Berman AT, Miller F, Pharo G, Collins L. The incidence of large venous emboli during total knee arthroplasty without pneumatic tourniquet use. Anesth Analg 1998; 87(2):439-444.

(23) Westman B, Weidenhielm L, Rooyackers O, Fredriksson K, Wernerman J, Hammarqvist F. Knee replacement surgery as a human clinical model of the effects of ischaemia/reperfusion upon skeletal muscle. Clin Sci (Lond) 2007; 113(7):313-318.

(24) Carles M, Dellamonica J, Roux J, Lena D, Levraut J, Pittet JF et al. Sevoflurane but not propofol increases interstitial glycolysis metabolites availability during tourniquet-induced ischaemia-reperfusion. Br J Anaesth 2008; 100(1):29-35.

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Early postoperative hyperglycaemia is associated

with postoperative complications after

pancreatoduodenectomy

WJ Eshuis, J Hermanides, JW van Dalen, G van

Samkar, ORC Busch, TM van Gulik, JH DeVries,

JBL Hoekstra and DJ Gouma


ABSTRACT

Background Perioperative hyperglycaemia is associated with complications after

various types of surgery. This relation was never investigated for

pancreatoduodenectomy. Aim of this study was to investigate the relation between

perioperative hyperglycaemia and complications after pancreatoduodenectomy.

Methods In a consecutive series of 330 patients undergoing

pancreatodoudenectomy, glucose values were collected from the hospital

information system during three periods: pre-, intra- and early postoperative. The

average glucose value per period was calculated for each patient and divided in

duals according to the median group value. Odds ratios for complications were

calculated for the upper versus lower dual, adjusted for age, gender, ASA-

classification, BMI, diabetes mellitus, intraoperative blood transfusion, duration of

surgery, intraoperative insulin administration and octreotide use. The same

procedures were carried out to assess the consequences of increased glucose

variability, expressed by the standard deviation.

Results Average glucose values were 7.5 (preoperative), 7.4 (intraoperative) and 7.9

mmol/l (early postoperative). Pre- and intraoperative glucose values were not

associated with postoperative complications. Early postoperative hyperglycaemia

(>7.8 mmol/l) was significantly associated with complications (OR 2.9, 95%CI 1.7 to

4.9). Overall, high glucose variability was not significantly associated with

postoperative complications, but early postoperative patients who had both high

glucose values and high variability had an OR for complications of 3.6 (95%CI 1.9

to 6.8) compared to the lower glucose dual.

Conclusions Early postoperative hyperglycaemia is associated with postoperative

complications after pancreatoduodenectomy. High glucose variability may enhance

this risk. Future research must demonstrate whether strict glucose control in the

early postoperative period prevents complications after pancreatoduodenectomy.

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INTRODUCTION

Although mortality of pancreatoduodenectomy (PD) is well below 5% in high

volume centres, complications occur in up to 50% of patients. Most prevalent

surgical complications include delayed gastric emptying, leakage of

pancreaticojejunostomy or hepaticojejunostomy and infectious complications.

Complications that are not directly related to surgery are mostly of cardiac,

pulmonary or urogenital origin.1-4

Numerous studies have been carried out to identify risk factors for the development

of complications after PD. Factors reported to be associated with morbidity after

PD include high body mass index, diabetes mellitus, high preoperative blood urea

nitrogen and low preoperative serum albumin, longer duration of surgery,

intraoperative blood transfusion and a small, non-dilated main pancreatic duct.5-11

Higher perioperative glucose levels have been shown to be associated with

complications after various types of cardiac, general, vascular and orthopaedic

surgery.12-18 This effect is generally attributed to an impaired immune response, in

combination with an excessive release of stress hormones and inflammatory

cytokines resulting in diminished insulin action.12;15;17 However, perioperative

hyperglycaemia does not increase postoperative morbidity in all types of surgery: in

oesophageal resection no relation was found between early postoperative

hyperglycaemia and postoperative complications.19

The relation between perioperative glucose levels and postoperative complications

after PD has never been investigated. Apart from surgical stress hyperglycaemia,

patients undergoing PD might be extra susceptible to disarrangement of glucose

levels: pancreatic cancer - the main indication for PD - is associated with

progressive hyperglycemia and (new onset) diabetes mellitus in 20% to 30% of

patients before diagnosis.20-22 The operation itself involves manipulation and

resection of pancreatic tissue, and resection of the duodenum, one of the enteric

regions where the incretin hormone GIP (glucose-dependent insulinotropic

polypeptide) is released, which assists in maintaining glucose homeostasis.

Together, this may render patients hyperglycaemic.23;24

Aiming for near-normal glucose levels (4.4 to 6.1 mmol/l) in a surgical intensive

care unit (ICU) significantly reduced mortality.25 Although subsequent studies on

such strict glucose control has yielded conflicting results in various populations, it

seems to reduce mortality and morbidity in surgical ICU patients.26

Because patients undergoing PD are at risk for hyperglycaemia, it seems important

to establish whether increased perioperative glucose levels are associated with poor

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outcome, especially since strict glucose control may be beneficial in this

population.26 The aim of the present study was therefore to investigate the

association of perioperative glucose levels with postoperative complications after

PD.

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METHODS

Patients and study outline

In a consecutive series of 330 patients undergoing elective PD between July 2000

and December 2006 for suspected pancreatic or periampullary malignancy,

clinicopathologic data, demographics and postoperative outcomes were

prospectively registered. Glucose values were analysed retrospectively. Since the

present study involved a retrospective analysis of anonymised data, the Dutch

Ethical Review Board regulations do not require informed consent.

Surgical Procedure

The standard surgical procedure was the pylorus-preserving PD with removal of

lymph nodes at the right side of the portal vein, as earlier described.27 In case of

tumor ingrowth in pylorus or duodenum, a classic WhippleÊs resection was

performed. If minimal tumor ingrowth in the portal or superior mesenteric vein

was found, a segmental or wedge resection was carried out.28 Reconstruction was

performed by retrocolic hepaticojejunostomy and pancreaticojejunostomy and

retrocolic or antecolic duodenojejunostomy, without Roux-Y reconstruction. One

silicone drain was left in the foramen of Winslow near the hepaticojejunostomy and

pancreaticojejunostomy.27 Feeding jejunostomy was not standard.29 Octreotide was

routinely administered for 7 days until 2002, and afterwards on indication of soft

pancreatic tissue or small pancreatic duct. Postoperatively, patients stayed at the

recovery room until the morning of the first postoperative day, before returning to

the surgical ward.

Measures of glucose

The most recent venous plasma glucose level within 4 months before surgery was

collected from the clinical information system or patient chart. Details about

fasting or non-fasting state were mostly unknown. All available intraoperative and

early postoperative glucose values (from operation until the end of the first

postoperative day) were collected. During surgery and in the early postoperative

period (defined as the period at the recovery room, or in case of extended stay at the

recovery room, until postoperative day 1), all blood glucose values were fasting.

They were measured by a blood gas/pH analyzer (Ciba Corning 865, Chiron

Diagnostics, Medford, MA, USA) in blood samples obtained from an arterial

catheter, and automatically stored in the hospital information system. We

calculated the mean intraoperative and early postoperative glucose value per

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patient. Furthermore, as a measure of glucose variability, the standard deviations

(SD) of intraoperative and early postoperative glucose values in each patient were

calculated.

Outcome measures

Primary outcome measure was whether the patient experienced any postoperative

complication. All surgical complications and non-surgical complications were taken

into account.

Secondary outcome measures were the occurrence of a surgical complication, the

occurrence of an infectious complication, a score of IIIa or higher in the Dindo-

Clavien classification of complications, delayed gastric emptying and

pancreaticojejunostomy leakage (grade B or C according to the International Study

Group of Pancreatic Surgery criteria), relaparotomy, admission to intensive or

medium care unit or readmission to the recovery room, length of hospital stay, and

hospital readmission within 30 days.30-32


Results are presented as mean � SD or median with interquartile range (IQR),

depending on the distribution of the data.

All glucose variables (the preoperative value, mean and SD of intraoperative and

early postoperative values) were divided into duals of equal group size. Using

multivariate binary logistic regression, we calculated Odds Ratios (OR) for all

dichotomous outcome measures for patients in the upper versus the lower dual.

Correlation with length of hospital stay (Natural log (Ln)-transformed) was

calculated using linear regression. Calculations were carried out separately for

each period (preoperative, intraoperative and early postoperative). Analyses were

adjusted for age, gender, ASA-classification, body mass index (BMI), diabetes

mellitus, intraoperative blood transfusion, duration of surgery, intraoperative

insulin administration and octreotide use. Analyses of SD were also adjusted for

the mean glucose value in the respective period.

Finally, secondary outcomes analyses were corrected for multiple testing according

to Benjamini and Hochberg.33 p-values below 0.05 were considered statistically

significant. All analyses were performed in SPSS version 16.0 (SPSS Inc.,Chicago,

IL, USA).

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RESULTS

Patient population

Patient and operative characteristics are summarised in Table 1. Mean age of the

cohort was 62 (SD 12); 56% was male and 16% had diabetes mellitus. A

preoperative glucose value was available in 79% of patients. At least one

intraoperative glucose value was available in 97% of patients and at least one early

postoperative glucose value was available in 99% of patients (Table 2).

Table 1-baseline and operative characteristics of 330 patients undergoing pancreatoduodenectomy in the period July 2000 – December 2006

Baseline characteristics Age, years (mean, SD) 62.3 12 Male sex, n (%) 185 (56) ASA I, n (%) 58 (18) ASA II, n (%) 209 (63) ASA III, n (%) 63 (19) Body Mass Index, n (%) -Underweight (<18.5) 19 (6) -Normal weight (18.5 – 24.9) 173 (52) -Overweight (25 – 29.9) 120 (36) -Obese (30 or higher) 18 (6) Diabetes Mellitus, n (%) 53 (16) Underlying disease, n (%) -Pancreatic adenocarcinoma 103 (31) -Ampullary adenocarcinoma 77 (23) -Distal CBD adenocarcinoma 43 (13) -Other (pre)malignant 58 (18) -Chronic pancreatitis 32 (10) -Other benign 17 (5)

Operative characteristics

Operative time, minutes (median, IQR) 280 (242-338) Blood transfusions, n (%) -None 259 (78) -1-3 59 (18) -4 or more 12 (4) Pylorus preserved, n (%) 299 (91)

SD, standard deviation; ASA, American Society of Anesthesiologists; CBD, common bile duct; IQR, interquartile range

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Preoperative Intraoperative Early Postoperative

Glu

cose

(mm

ol/l)

with

95%

CI

P<0.0018.2

8.0

7.8

7.6

7.4

7.2

7.0

6.8

Mean preoperative glucose was 7.5 (SD 4.2) mmol/l and mean intraoperative

glucose 7.4 (SD 1.9) mmol/l. Intraoperative insulin was administered in 14% of

patients. During stay at recovery, mean glucose was significantly higher as

compared to the intraoperative period: 7.9 (SD 1.5) mmol/l (p<0.001, paired t-test,

Figure 1).

Table 2-perioperative glucose values of 330 patients undergoing pancreatoduodenectomy

Moment of sampling Preoperative -Preoperative value available, n (%) 259 (79) -Preoperative value, mmol/l* (mean, SD) 7.5 (4.2) Intraoperative -At least 1 value available, n (%) 320 (97) ->1 value available, n (%) 263 (80) -No. of samples per patient (mean, SD) 2.9 (1.6) -Intraoperative value, mmol/l (mean, SD) 7.4 (1.9) -Standard deviation mmol/l (mean, SD) 1.0 (1.2) Early postoperative -At least 1 value available, n (%) 329 (100) ->1 value available, n (%) 328 (99) -No. of samples per patient (mean,SD) 4.3 (1.9) -Postoperative value, mmol/l (mean, SD) 7.9 (1.5) -Standard deviation, mmol/l (mean, SD) 1.1 (0.9)

SD, standard deviation

Figure 1- mean glucose values pre-, intra-, and early postoperative

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Postoperative morbidity was 58%. Surgical complications occurred in 48% of

patients; most prevalent complications were delayed gastric emptying and

pancreaticojejunostomy leakage (Table 3); 83 patients (25%) had a complication of

grade IIIa or higher in the Dindo-Clavien classification (Table 4). There were no

significant differences in complications in patients with or without diabetes (Table

5).

Table 3-postoperative complications and other outcomes in 330 patients undergoing pancreatoduodenectomy

Complication

Any complication, n (%) 191 (58) Surgical complications, n (%) 158 (48) -Delayed gastric emptying (ISGPS grade B or C) 99 (30) -Pancreaticojejunostomy leakage (ISGPS grade B or C) 52 (16) -Postpancreatectomy hemorrhage (ISGPS grade B or C) 17 (5) -Hepaticojejunostomy leakage 11 (3) -Intra-abdominal abscess (without PJ- or HJ-leakage) 10 (3) -Wound infection 43 (13) -Other 45 (14) Non-surgical complications, n (%) 97 (29) -Pneumonia 17 (5) -Other pulmonary 21 (6) -Cardiac 25 (8) -Urinary tract infection 45 (14) -Other 31 (9) Infectious complications, n (%) 140 (42)

Other outcomes

In-hospital mortality, n (%) 7 (2) Reoperation, n (%) 22 (7) Admission to ICU/MC or readmission to recovery room, n (%) 42 (13) Length of hospital stay - days, (median, IQR) 12 (9-21) Hospital readmission within 30 days, n (%) 29 (9)

ISGPS, International Study Group of Pancreatic Surgery; PJ, pancreaticojejunostomy; HJ, hepaticojejunostomy; ICU, intensive care unit; MC, medium care; IQR, interquartile range

Table 4-classification of 330 patients undergoing pancreatoduodenectomy according to the Clavien-Dindo classification of complications

Clavien-Dindo grade, n (%) 0 – No complication 121 (37) I – No pharmacologic treatment or intervention needed* 21 (6) II – Requiring pharmacological treatment† 105 (32) IIIa – Requiring intervention – not under general anesthesia 35 (11) IIIb – Requiring intervention – under general anesthesia 3 (1) IVa – Life-threatening complication requiring MC/IC management, single organ dysfunction 34 (10) IVb – As IVa, but with multiple organ dysfunction 3 (1) V – Death 8 (2)

MC, medium care; IC, intensive care * Allowed: antiemetics, antipyretics, analgetics, diuretics, electrolytes, physiotherapy, wound infections opened at bedside † Includes blood transfusions and total parenteral nutrition

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Table 5-distribution of glucose levels and complications after 330 pancreatoduodenectomies in patients without diabetes and patients with diabetes

Glucose values in mmol/l, results presented as mean (SD). * Chi-squared test for between group difference in any complication p = 0.16

Perioperative glucose values and postoperative complications

Preoperative and mean intraoperative glucose values were not associated with the

occurrence of a postoperative complication (Figure 2). In the early postoperative

period, a higher mean glucose (>7.8 mmol/l) was significantly associated with

postoperative complication (OR 2.9, 95%CI 1.7 to 4.9) (Figure 2). A Receiver-

Operator-Characteristic curve (ROC-curve) showed that a cut-off point of 7.8

mmol/l was the best discriminative value for the occurrence of a postoperative

complication, with a sensitivity of 0.58 and a specificity of 0.63 (curve not shown).

High glucose variability during operation or in the early postoperative period was

not associated with the occurrence of a postoperative complication. Patients in the

upper dual for both mean glucose value and glucose variability in the early

postoperative period had an OR for the development of a postoperative complication

of 3.6 (95%CI 1.9 to 6.8) compared to the lower glucose dual.

Secondary outcome measures

Preoperative and mean intraoperative glucose values were not associated with any

of the secondary outcome measures (Figure 2). A higher mean early postoperative

glucose value was significantly associated with surgical and infectious

complications (ORs 2.0, 95% CI 1.2 to 3.2, and 2.3, 95% CI 1.4 to 3.8, respectively),

delayed gastric emptying (OR 2.1, 95% CI 1.2 to 3.7), Dindo-Clavien complication

score >=III (OR 2.3, 95% CI 1.3 to 4.3), and relaparotomy (OR 6.1, 95% CI 1.6 to

24.1). Length of stay was 1.2 days (95% CI 1.1 to 1.4, p=0.01) longer in the upper

dual. High glucose variability intraoperatively or in the early postoperative period

was not associated with any of the secondary outcome measures.

Preoperative Intraoperative Early postoperative

Any complication* N (%)

No diabetes (n=277) 6.5 (2.1) 7.1 (1.4) 7.9 (1.4) 165 (59.6) Diabetes (n=53) 11.5 (7.2) 8.7 (3.3) 8.2 (2.1) 26 (49.1)

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Odds ratio w

ith 95% C

I

0,11

10

Clavien => III

Readm

ission

ICU

admittance

Relaparotom

y

DG

E

PJ leakage

Infectious complications

Surgical com

plications

Any com

plication

Mean P

reoperative Glucose

0.9 (0.5-1.5)

1.1 (0.7-1.9)1.7 (0.8-3.9)

1.5 (0.8-2.7)

1.0 (0.6-1.8)

1.3 (0.6-2.4)

1.1 (0.3-3.8)2.7 (0.9-7.7)

1.6 (0.7-3.8)

Odds ratio w

ith 95% C

I

0,11

10

Clavien => III

Readm

ission

ICU

admittance

Relaparotom

y

DG

E

PJ leakage


Surgical com

plications

Any com

plication

Mean Intraoperative G

lucose

0.8 (0.5-1.4)

0.8 (0.5-1.4)

0.8 (0.5-1.3)

0.5 (0.2-1.2)

1.2 (0.7-2.2)

0.9 (0.3-2.9)

0.4 (0.2-1.0)

0.6 (0.2-1.5)

0.9 (0.5-1.7)

Odds ratio w

ith 95% C

I

0,11

10

Clavien => III

Readm

ission

ICU

admittance

Relaparotom

y

DG

E

PJ leakage


Surgical com

plications

Any com

plication

Mean E

arly Postoperative G

lucose

2.9 (1.7-4.9), P<0.001

2.0 (1.2-3.2), P=0.01

2.3 (1.4-3.8), P=0.02

1.6 (0.8-3.2), P=0.24

2.2 (1.2-3.7), P=0.02

1.5(0.7-3.5),P=0.28

6.2 (1.6-24.3), P=0.01

2.0 (0.8-5.0), P=0.17

2.4 (1.3-4.3), P=0.02

Figure 2A

-odds ratios

(OR

s) for

all outcom

es com

paring patients

with

preoperative glucose of >=6.30 mm

ol/l to patients <6.30 m

mol/l. A

fter correction for m

ultiple testing, none of the OR

s were

significant. ICU

= Adm

ission to intensive care

unit, m

edium

care unit

or readm

ission to the recovery room.

PJ,

pancreaticojejunostomy

leakage; D

GE

, delayed gastric emptying

Figure 2B

-odds ratios

(OR

s) for

all outcom

es com

paring patients

with

intraoperative glucose of >=7.06 mm

ol/l to patients <7.06 m

mol/l. A

fter correction for m

ultiple testing, none of the OR

s were

significant. ICU

= Adm

ission to intensive care unit, m

edium care unit or readm

ission to the recovery room

. P

J, pancreaticojejunostom

y leakage;

DG

E, delayed gastric em

ptying

Figure 2C

-odds ratios

(OR

s) for

all outcom

es comparing patients w

ith early postoperative glucose of >=7.78 m

mol/l to

patients <7.78

mm

ol/l. p

values are

adjusted for

multiple

testing. IC

U=

Adm

ission to intensive care unit, medium

care unit or readm

ission to the recovery room

. P

J, pancreaticojejunostom

y leakage;

DG

E, delayed gastric em

ptying

�

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DISCUSSION

In this study we showed that early postoperative hyperglycaemia (>7.8 mmol/l)

after pancreatoduodenectomy was significantly associated with the development of

a postoperative complication, possible confounders taken into account. This was

reflected by a higher risk of surgical and infectious complications, delayed gastric

emptying, Dindo-Clavien complication score >=III and relaparotomy. Also length of

stay was significantly longer in these patients. When elevated postoperative

glucose was accompanied by high glucose variability, the risk of a postoperative

complication seemed to increase. These novel findings were all adjusted for age,

gender, ASA-classification, BMI, diabetes mellitus, intraoperative blood

transfusion, duration of surgery, intraoperative insulin administration and use of

octreotide. Mean glucose levels before and during PD were comparable and

significantly lower as compared to early postoperative levels. Elevated preoperative

and intraoperative glucose and higher glucose variability were not associated with

the development of complications.

Our findings that early postoperative higher glucose levels are associated with

complications after PD are in line with previous studies investigating this relation

in other types of surgery. Ramos and coworkers identified postoperative

hyperglycaemia as a significant risk factor for postoperative infections in general

and vascular surgery.13 Vriesendorp et al observed an OR of 5.1 (95% CI 1.6 to

17.1) for postoperative infections when early postoperative glucose values were 8.4

mmol/l or above in peripheral vascular surgery.12 Also the seemingly additional

detrimental effect of high glucose variability was described before in populations of

critically ill and surgical intensive care unit patients.34;35 Although it is difficult to

elucidate whether hyperglycaemia results from postoperative complications or

indeed contributes to their development, we believe the latter mechanism is might

be most plausible, but should be proven in the future by early regulation and

control. As the increase in glucose levels preceded the development of complications

and possible confounders were accounted for in the analyses, a causal relation

between elevated glucose levels postoperatively and the development of

complications is suspected.

Early postoperative hyperglycaemia was associated with the most prevalent

complication in our cohort, delayed gastric emptying. Besides being a common

complication after pancreatoduodenectomy, gastroparesis is a well-known

complication of longstanding diabetes mellitus. It has also been associated with

acute hyperglycaemia.36

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Several explanations for the harmful effects of both stress hyperglycaemia and

glucose variability have been proposed, such as glucose toxicity and oxidative

stress, stimulating intracellular pathways that lead to detrimental tissue effects

such as mitochondrial dysfunction and immune dysregulation.22;37 Although the

application of strict glucose control has led to conflicting results in a variety of

populations, a recent meta-analysis suggests that surgical ICU patients benefit

from strict glucose control.26 Okabayashi and coworkers recently showed that strict

control of perioperative blood glucose following pancreatic resection is possible, and

may reduce postoperative infection rates.38;39

We found that in PD, intraoperative glucose levels were comparable to preoperative

levels, and that pre- and intraoperative glucose values and variability were not a

risk factor for postoperative complications after PD. Intraoperative glucose values

have earlier been described as risk factor for adverse outcomes in cardiac surgery

and liver transplantation.14;15;40;41 Contrary to PD, these operations involve

substantial ischemia and reperfusion injury, which may explain these seemingly

contradictory different findings.

Interestingly, the mean glucose in patients with diabetes decreased after surgery as

compared to the intraoperative values, indicating a different glucose response to

surgery. Diabetes patients did not have more complications than non-diabeties

patients in our series, whereas diabetes mellitus has been described as a risk factor

for intra-abdominal complications after PD.8 Our cohort was however too small to

draw firm conclusions about the diabetes subpopulation.

Using an ROC-curve analysis we found that the cut-off value of 7.8 mmol/l early

postoperatively was the most discriminatory value when predicting complications.

This was comparable to our population mean and median value. Perhaps not

coincidentally, normal healthy subjects will not peak above 7.8 mmol/l when

challenged with oral glucose, supporting the strength of this cut-off value.42

Our study was limited by the retrospective collection of glucose values.

Preoperative values were not available in 21% of patients. However, postoperative

morbidity in these patients was not different from patients who did have a

preoperative value available (59% versus 58%, data not shown). Glucose sampling

was not standardised, leading to differences in timing, amount and indication of

glucose samples. To minimise these sources of error, the average glucose value per

period was taken. Also, intraoperative and postoperative glucose values were

available in nearly all patients, showing that frequent perioperative sampling was

common, irrespective of the condition of the patient.

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Our study is the first to investigate the effect of perioperative glucose levels in this

specific patient group with pancreatic disease. It was performed in a large

consecutive series of elective patients. Patient characteristics and hospital course

were prospectively scored.

In conclusion, early postoperative glucose levels above 7.8 mmol/l are significantly

associated with postoperative complications after PD. Future research must clarify

the underlying mechanisms and pathways of this relation. Treatment of

hyperglycaemia is possible and strict glucose control may be favourable in surgical

patients.26 A randomised clinical trial should be conducted to investigate whether a

strategy of strict glucose control (versus glucose correction on indication) in the

early postoperative period is beneficial in this patient group.

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REFERENCES

(1) Bassi C, Falconi M, Salvia R, Mascetta G, Molinari E, Pederzoli P. Management of complications after pancreaticoduodenectomy in a high volume centre: results on 150 consecutive patients. Dig Surg 2001; 18(6):453-457.

(2) de Castro SM, Busch OR, van Gulik TM, Obertop H, Gouma DJ. Incidence and management of pancreatic leakage after pancreatoduodenectomy. Br J Surg 2005; 92(9):1117-1123.

(3) van Heek NT, Kuhlmann KF, Scholten RJ, de Castro SM, Busch OR, van Gulik TM et al. Hospital volume and mortality after pancreatic resection: a systematic review and an evaluation of intervention in the Netherlands. Ann Surg 2005; 242(6):781-8, discussion.

(4) Yeo CJ, Cameron JL, Sohn TA, Lillemoe KD, Pitt HA, Talamini MA et al. Six hundred fifty consecutive pancreaticoduodenectomies in the 1990s: pathology, complications, and outcomes. Ann Surg 1997; 226(3):248-257.

(5) House MG, Fong Y, Arnaoutakis DJ, Sharma R, Winston CB, Protic M et al. Preoperative predictors for complications after pancreaticoduodenectomy: impact of BMI and body fat distribution. J Gastrointest Surg 2008; 12(2):270-278.

(6) Su Z, Koga R, Saiura A, Natori T, Yamaguchi T, Yamamoto J. Factors influencing infectious complications after pancreatoduodenectomy. J Hepatobiliary Pancreat Surg 2009.

(7) Williams TK, Rosato EL, Kennedy EP, Chojnacki KA, Andrel J, Hyslop T et al. Impact of obesity on perioperative morbidity and mortality after pancreaticoduodenectomy. J Am Coll Surg 2009; 208(2):210-217.

(8) Cheng Q, Zhang B, Zhang Y, Jiang X, Zhang B, Yi B et al. Predictive factors for complications after pancreaticoduodenectomy. J Surg Res 2007; 139(1):22-29.

(9) Fathy O, Wahab MA, Elghwalby N, Sultan A, EL-Ebidy G, Hak NG et al. 216 cases of pancreaticoduodenectomy: risk factors for postoperative complications. Hepatogastroenterology 2008; 55(84):1093-1098.

(10) Muscari F, Suc B, Kirzin S, Hay JM, Fourtanier G, Fingerhut A et al. Risk factors for mortality and intra-abdominal complications after pancreatoduodenectomy: multivariate analysis in 300 patients. Surgery 2006; 139(5):591-598.

(11) Winter JM, Cameron JL, Yeo CJ, Alao B, Lillemoe KD, Campbell KA et al. Biochemical markers predict morbidity and mortality after pancreaticoduodenectomy. J Am Coll Surg 2007; 204(5):1029-1036.

(12) Vriesendorp TM, Morelis QJ, Devries JH, Legemate DA, Hoekstra JB. Early post-operative glucose levels are an independent risk factor for infection after peripheral vascular surgery. A retrospective study. Eur J Vasc Endovasc Surg 2004; 28(5):520-525.

(13) Ramos M, Khalpey Z, Lipsitz S, Steinberg J, Panizales MT, Zinner M et al. Relationship of perioperative hyperglycemia and postoperative infections in patients who undergo general and vascular surgery. Ann Surg 2008; 248(4):585-591.

(14) Gandhi GY, Nuttall GA, Abel MD, Mullany CJ, Schaff HV, Williams BA et al. Intraoperative hyperglycemia and perioperative outcomes in cardiac surgery patients. Mayo Clin Proc 2005; 80(7):862-866.

(15) Park C, Hsu C, Neelakanta G, Nourmand H, Braunfeld M, Wray C et al. Severe intraoperative hyperglycemia is independently associated with surgical site infection after liver transplantation. Transplantation 2009; 87(7):1031-1036.

(16) Olsen MA, Nepple JJ, Riew KD, Lenke LG, Bridwell KH, Mayfield J et al. Risk factors for surgical site infection following orthopaedic spinal operations. J Bone Joint Surg Am 2008; 90(1):62-69.

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(17) Vilar-Compte D, Alvarez dI, I, Martin-Onraet A, Perez-Amador M, Sanchez-Hernandez C, Volkow P. Hyperglycemia as a risk factor for surgical site infections in patients undergoing mastectomy. Am J Infect Control 2008; 36(3):192-198.

(18) Ambiru S, Kato A, Kimura F, Shimizu H, Yoshidome H, Otsuka M et al. Poor postoperative blood glucose control increases surgical site infections after surgery for hepato-biliary-pancreatic cancer: a prospective study in a high-volume institute in Japan. J Hosp Infect 2008; 68(3):230-233.

(19) Vriesendorp TM, Devries JH, Hulscher JB, Holleman F, van Lanschot JJ, Hoekstra JB. Early postoperative hyperglycaemia is not a risk factor for infectious complications and prolonged in-hospital stay in patients undergoing oesophagectomy: a retrospective analysis of a prospective trial. Crit Care 2004; 8(6):R437-R442.

(20) Pannala R, Leibson CL, Rabe KG, Timmons LJ, Ransom J, de AM et al. Temporal association of changes in fasting blood glucose and body mass index with diagnosis of pancreatic cancer. Am J Gastroenterol 2009; 104(9):2318-2325.

(21) Chari ST, Leibson CL, Rabe KG, Timmons LJ, Ransom J, de AM et al. Pancreatic cancer-associated diabetes mellitus: prevalence and temporal association with diagnosis of cancer. Gastroenterology 2008; 134(1):95-101.


(23) Freeman JS. Role of the incretin pathway in the pathogenesis of type 2 diabetes mellitus. Cleve Clin J Med 2009; 76 Suppl 5:S12-S19.

(24) Slezak LA, Andersen DK. Pancreatic resection: effects on glucose metabolism. World J Surg 2001; 25(4):452-460.


(26) Griesdale DE, de Souza RJ, van Dam RM, Heyland DK, Cook DJ, Malhotra A et al. Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. CMAJ 2009; 180(8):821-827.

(27) Gouma DJ, Nieveen van Dijkum EJ, Obertop H. The standard diagnostic work-up and surgical treatment of pancreatic head tumours. Eur J Surg Oncol 1999; 25(2):113-123.

(28) van Geenen RC, ten Kate FJ, de Wit LT, van Gulik TM, Obertop H, Gouma DJ. Segmental resection and wedge excision of the portal or superior mesenteric vein during pancreatoduodenectomy. Surgery 2001; 129(2):158-163.

(29) Heslin MJ, Latkany L, Leung D, Brooks AD, Hochwald SN, Pisters PW et al. A prospective, randomized trial of early enteral feeding after resection of upper gastrointestinal malignancy. Ann Surg 1997; 226(4):567-577.

(30) Bassi C, Dervenis C, Butturini G, Fingerhut A, Yeo C, Izbicki J et al. Postoperative pancreatic fistula: an international study group (ISGPF) definition. Surgery 2005; 138(1):8-13.

(31) Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 2004; 240(2):205-213.

(32) Wente MN, Bassi C, Dervenis C, Fingerhut A, Gouma DJ, Izbicki JR et al. Delayed gastric emptying (DGE) after pancreatic surgery: a suggested definition by the International Study Group of Pancreatic Surgery (ISGPS). Surgery 2007; 142(5):761-768.

(33) Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society 1995; 57(1):289-300.

(34) Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris JM, Jr., May AK. Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg 2008; 74(8):679-685.

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(35) Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH. Glucose variability is associated with intensive care unit mortality. Crit Care Med 2010; 38(3):838-42

(36) De Block CE, De L, I, Pelckmans PA, Van Gaal LF. Current concepts in gastric motility in diabetes mellitus. Curr Diabetes Rev 2006; 2(1):113-130.

(37) Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes 2005; 54(6):1615-1625.

(38) Hanazaki K, Okabayashi T, Maeda H. Hepatectomized PatientsTight Glycemic Control Using an Artificial Pancreas to Control Perioperative Hyperglycemia Decreases Surgical Site Infection in Pancreatectomized or Hepatectomized Patients. Ann Surg 2009; 250(2):351.

(39) Okabayashi T, Nishimori I, Yamashita K, Sugimoto T, Maeda H, Yatabe T et al. Continuous postoperative blood glucose monitoring and control by artificial pancreas in patients having pancreatic resection: a prospective randomized clinical trial. Arch Surg 2009; 144(10):933-937.

(40) Ammori JB, Sigakis M, Englesbe MJ, O'Reilly M, Pelletier SJ. Effect of intraoperative hyperglycemia during liver transplantation. J Surg Res 2007; 140(2):227-233.

(41) Doenst T, Wijeysundera D, Karkouti K, Zechner C, Maganti M, Rao V et al. Hyperglycemia during cardiopulmonary bypass is an independent risk factor for mortality in patients undergoing cardiac surgery. J Thorac Cardiovasc Surg 2005; 130(4):1144.

(42) American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes care 2008; 31(Suppl. 1):S55-S60.

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DETECTION AND TREATMENT OF HYPERGLYCAEMIA

Sense and nonsense in sensors

J Hermanides and JH DeVries

Diabetologia, 2010 April;53:593-596

ABSTRACT

Continuous subcutaneous glucose monitoring (CGM) is a developing technology in

the treatment of diabetes mellitus. The first randomised controlled trials on its

efficacy have been performed. In several studies, CGM lowered HbA1c in adult

patients with suboptimally type 1 diabetes mellitus, when selecting compliant

patients who tolerate the device. However, as a preventive tool for hypoglycaemia,

CGM has not fulfilled the great expectations. Increasing reimbursement of CGM is

expected in the near future, awaiting studies on cost-effectiveness.

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INTRODUCTION

Pioneering work of artificially replacing the glucose-monitoring function of the

pancreas started in the 1970s.1 With the introduction of the microdialysis

technique in the early 1990s, the future of continuous subcutaneous glucose

monitoring (CGM) seemed bright and shiny.2;3 The great expectation was that, by

providing the patient with a continuous stream of data and alarms for otherwise

unrecognised (nocturnal) hypo- and hyperglycaemia, the HbA1c target of <7% was

in reach and the main barrier to effective diabetes treatment·the occurrence of

severe hypoglycaemia·could be overcome.4-6 Even more, integrating CGM and

continuous subcutaneous insulin infusion (CSII) would mean it would be a matter

of time before the closed-loop system would be available.7 Several years later, the

first needle-type sensor became clinically available, though it could only be read out

retrospectively.8 Currently, there are three real-time CGM systems on the market

that are approved by the US Food and Drug Administration (FDA) and have the

Conformité Européenne (CE) mark: the Freestyle Navigator (Abbott Diabetes Care,

Alameda, CA, USA); the Guardian Real-Time (Medtronic MiniMed, Northridge,

CA, USA); and the DexCom SEVEN (DexCom, San Diego, CA, USA; only available

in the USA). All these systems measure glucose in the subcutaneous tissue and

provide real-time glucose measurements every 1 to 5 min. The first randomised

controlled trials (RCTs) have now been performed with real-time CGM, prompting

the next questions: did the sensor fulfil expectations and is this reflected in the

current reimbursement status of the device?

EFFECT ON HbA1C

The first RCT, performed by Deiss and colleagues, investigated a needle-type

continuous subcutaneous glucose monitor in patients with poorly controlled type 1

diabetes (HbA1c >=8.1% on intensive treatment).9 There was a difference in HbA1c

reduction of 0.6% after 3 months in favour of patients who were instructed to use

the device continuously, as compared with patients using conventional treatment.

In a third arm, patients used CGM biweekly, and this did not result in a significant

HbA1c improvement. A subsequent 26 week randomised treat-to-target study

performed by Hirsch and coworkers (the STAR 1 trial) yielded disappointing

results.10 There was no significant difference in change in HbA1c between type 1

diabetes patients using CSII randomised to either augmenting their current

therapy with CGM or continuing with their standard self-monitoring of blood

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glucose (SMBG). In both groups, there was a decrease in HbA1c of 0.6 to 0.7%. That

the decreases in HbA1c were comparable in both groups was attributed to

intensification of the treatment in both the control and intervention groups. It

seems that in an attempt to assure equal attention times in both groups, the

control group was treated more intensively than would be feasible in daily practice.

In the Juvenile Diabetes Research Foundation (JDRF) study, three different age

groups (º25, 15 to 24 and 8 to 14 years) were randomised to either CGM or SMBG

continuation.11 All patients had type 1 diabetes and the vast majority were already

using CSII. The mean difference in HbA1c change was 0.5% after 26 weeks in

favour of patients using CGM, but this was only in those aged 25 years and older.

No significant difference in HbA1c change was detected in the other age groups. In

both the STAR 1 and the JDRF trials, the frequency of sensor usage was strongly

correlated to the decrease in HbA1c. This is in line with the study from Deiss et al.9

and the recently published RealTrend study12. In this latter study, patients

administering multiple daily injections and with an HbA1c >=8% at inclusion

started with either CSII therapy or sensor-augmented pump therapy for 26 weeks.

From a predefined analysis, HbA1c improved compared with the CSII group only in

patients using the sensor >70% of the time. Unfortunately, patients in the sensor-

augmented pump group had already used CGM for 9 days before the baseline

HbA1c measurement was performed and therefore the observed HbA1c difference,

0.41%, may have been underestimated. The combination of CGM and CSII has also

been investigated in the recently presented Eurythmics trial, where a difference in

HbA1c improvement of 1.21% in favour of the sensor-augmented pump group was

found when type 1 diabetes patients (HbA1c at entry >=8.2%), who were using

multiple daily injection therapy and SMBG, were randomised to continuing their

current therapy or starting CGM-augmented insulin-pump therapy.13 It is

interesting that the JDRF trial and the Eurythmics trial, both showing a

significant HbA1c improvement in the intervention group, confronted patients with

a short period of blinded CGM usage at baseline before randomisation. Patients

who did not tolerate the device, and therefore would be likely to drop out or be non-

compliant during the study course, were at least partly filtered out before

randomisation.

EFFECT ON SEVERE HYPOGLYCAEMIA

The improvement in HbA1c in the different RCTs was not accompanied by a

significant increase in severe hypoglycaemia.9;11-13 This seems reassuring, but

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CGM did not fulfil the expectation that its use would reduce the frequency of severe

hypoglycaemic events.14 Even more, in the STAR 1 trial, the use of CGM was

associated with a significant increase in severe hypoglycaemia. This is most likely

related to the reduced awareness of auditory and vibratory alarms during

hypoglycaemia and non-use of the device during high-risk activities, such as

intensive sports. No study so far has shown a decrease in the frequency of severe

hypoglycaemia in a CGM arm as compared with the control arm in type 1 diabetes.

Indeed, sensors have an inaccuracy of up to 21% when comparing plasma glucose

values with subcutaneous glucose values.15 This inaccuracy is even more

pronounced in the hypoglycaemic range. In addition, the usefulness of the CGM

devices for detecting forthcoming hypoglycaemia is limited by a putative

physiological delay between the blood glucose and glucose concentration in the

subcutaneous tissue, which is accompanied by an instrumental delay of the

sensor.16 In other words, patients need more time to prevent hypoglycaemia,

especially when they have hypoglycaemia unawareness. Perhaps the developing

technology will result in an alarm function for predicting hypoglycaemia by using

the rate of change in glucose in the lower euglycaemic range. For now, CGM is

insufficient for the prevention of severe hypoglycaemia.

COSTS AND REIMBURSEMENT

Interestingly, it is the argument of possibly preventing severe hypoglycaemia that

has persuaded healthcare organisations in Israel to reimburse CGM use. Children

with type 1 diabetes who have experienced more than two severe hypoglycaemic

episodes within 1 year are entitled to CGM compensation. In addition, real-time

CGM is now covered by the majority of health plans in the USA, including the

federal Medicare program. Reimbursement is generally available for type 1

patients with severe hypoglycaemia or those who are not meeting American

Diabetes Association HbA1c targets. In the Netherlands and part of Italy,

retrospective CGM is currently reimbursed. The Czech Republic covers up to four

sensors per year for retrospective CGM and in Sweden, real-time CGM is

reimbursed for patients using CSII and having two or more severe hypoglycaemic

episodes per year, children who require at least ten plasma glucose tests per 24 h

and patients with HbA1c >10% while receiving optimised insulin therapy. The

reimbursement indication of hypoglycaemia illustrates that so far, coverage was

based on feeling rather than the (scarce) evidence. This is not to say that CGM is

not able to reduce severe hypoglycaemia, but we need randomised trials in patients

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at high risk for severe hypoglycaemia (i.e. those suffering from hypoglycaemia

unawareness). From the results of the clinical trials to date, we can now argue that

CGM offers a clear health benefit, expressed as HbA1c lowering, for type 1 diabetes

patients with HbA1c values above 8%. The additional costs for sensors are US$4380

per person year compared with US$550-2740 when using SMBG.17 Consequently,

we need to calculate whether the long-term health benefits of CGM following from

ascertained lowering of HbA1c outweigh the costs when compared with standard

care with multiple daily injections and/or insulin pumps. A similar comparison was

performed by Roze and colleagues indicating that the cost-effectiveness of CSII is

acceptable.18 A technical appraisal from the National Institute for Health and

Clinical Excellence (England and Wales) seems timely.

OTHER PATIENT GROUPS

The application of CGM in patient groups other than those with type 1 diabetes is

limited. No trials with adequate duration assessing HbA1c change have been

performed for patients with type 2 diabetes.19 One particular patient group that

warrants special attention is pregnant women with diabetes. CGM has proven

effective in improving glucose control at the end of pregnancy.20 It resulted in fewer

cases of macrosomia in the offspring, with an odds ratio of 0.36 (95% CI 0.13 to

0.98).21 As it concerns a limited time span, reimbursement of CGM in this group

should impose no significant financial burden on the healthcare system. Finally,

the use of CGM for treating in-hospital hyperglycaemia could be valuable, and the

first randomised studies are awaited. However, the advantage of CGM in this

setting may be less evident, as frequent blood sampling is standard practice in the

intensive care unit and CGM accuracy may suffer if the circulation is

compromised.22

CONCLUSION

CGM with or without CSII has been proven to lower HbA1c in adult patients with

type 1 diabetes mellitus and HbA1c values above 8%, when compliant patients who

tolerate the device are selected. However, CGM is not the final answer to severe

hypoglycaemia. Awaiting a cost-effectiveness analysis, increasing reimbursement of

CGM is expected.

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ACKNOWLEDGDEMENTS

No funding was received for this commentary. We acknowledge N. Papo (Senior

Reimbursement Manager, Medtronic International Trading Sárl, Tolochenaz

Switzerland) for providing information on the current global reimbursement status

of CGM.

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REFERENCES

(1) Albisser AM, Leibel BS. 9 The artificial pancreas. Clinics in Endocrinology and

Metabolism 1977; 6(2):457-479.

(2) Bolinder J, Ungerstedt U, Arner P. Long-term continuous glucose monitoring with microdialysis in ambulatory insulin-dependent diabetic patients. Lancet 1993; 342(8879):1080-1085.

(3) Bolinder J, Ungerstedt U, Arner P. Microdialysis measurement of the absolute glucose concentration in subcutaneous adipose tissue allowing glucose monitoring in diabetic patients. Diabetologia 1992; 35(12):1177-1180.

(4) American Diabetes Association. Standards of Medical Care in Diabetes--2008. Diabetes Care 2008; 31(Supplement_1):S12-S54.

(5) Cryer PE. The barrier of hypoglycemia in diabetes. Diabetes 2008; 57(12):3169-3176.

(6) Wentholt IM, Maran A, Masurel N, Heine RJ, Hoekstra JB, DeVries JH. Nocturnal hypoglycaemia in Type 1 diabetic patients, assessed with continuous glucose monitoring: frequency, duration and associations. Diabet Med 2007; 24(5):527-532.

(7) Hanaire H. Continuous glucose monitoring and external insulin pump: towards a subcutaneous closed loop. Diabetes Metab 2006; 32(5 Pt 2):534-538.

(8) Gross TM, Bode BW, Einhorn D, Kayne DM, Reed JH, White NH et al. Performance evaluation of the MiniMed continuous glucose monitoring system during patient home use. Diabetes Technol Ther 2000; 2(1):49-56.

(9) Deiss D, Bolinder J, Riveline JP, Battelino T, Bosi E, Tubiana-Rufi N et al. Improved Glycemic Control in Poorly Controlled Patients with Type 1 Diabetes Using Real-Time Continuous Glucose Monitoring. Diabetes Care 2006; 29(12):2730-2732.

(10) Hirsch IB, Abelseth J, Bode BW, Fischer JS, Kaufman FR, Mastrototaro J et al. Sensor-Augmented Insulin Pump Therapy: Results of the First Randomized Treat-to-Target Study. Diabetes Technology & Therapeutics 2008; 10(5):377-383.

(11) The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Continuous Glucose Monitoring and Intensive Treatment of Type 1 Diabetes. N Engl J Med 2008; 359(14):1464-1476.

(12) Raccah D, Sulmont V, Reznik Y, Guerci B, Renard E, Hanaire H et al. Incremental value of continuous glucose monitoring when starting pump therapy in patients with poorly controlled type 1 diabetes: The RealTrend study. Diabetes Care 2009.

(13) Hermanides J., NŒrgaard K., Bruttomesso D., Mathieu C., Frid A., Dayan C.M. et al. Sensor augmented pump therapy substantially lowers HbA1c. Diabetologia 52[Supplement I], S43. 2009. Abstract

(14) Cryer PE. Diverse causes of hypoglycemia-associated autonomic failure in diabetes. N Engl J Med 2004; 350(22):2272-2279.


(16) Wentholt IM, Hart AA, Hoekstra JB, DeVries JH. Relationship between interstitial and blood glucose in type 1 diabetes patients: delay and the push-pull phenomenon revisited. Diabetes Technol Ther 2007; 9(2):169-175.

(17) Pham M. Medtronic Diabetes: Sizing the market for realtime, continuous blood glucose monitors from MDT, DXCM, and ABT. Available from www.research.hsbc.com/midas/Res/ RDV? p=pdf&key=ate2b8rygn&name=127057.PDF, accessed 28 September 2009

(18) Roze S, Valentine WJ, Zakrzewska KE, Palmer AJ. Health-economic comparison of continuous subcutaneous insulin infusion with multiple daily injection for the treatment of Type 1 diabetes in the UK. Diabet Med 2005; 22(9):1239-1245.

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(19) Yoo HJ, An HG, Park SY, Ryu OH, Kim HY, Seo JA et al. Use of a real time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes. Diabetes Res Clin Pract 2008; 82(1):73-79.

(20) Landon MB, Gabbe SG, Piana R, Mennuti MT, Main EK. Neonatal morbidity in pregnancy complicated by diabetes mellitus: predictive value of maternal glycemic profiles. Am J Obstet Gynecol 1987; 156(5):1089-1095.

(21) Murphy HR, Rayman G, Lewis K, Kelly S, Johal B, Duffield K et al. Effectiveness of continuous glucose monitoring in pregnant women with diabetes: randomised clinical trial. BMJ 2008; 337:a1680.

(22) Sair MPMF, Etherington PJB, Peter Winlove CD, Evans TWM. Tissue oxygenation and perfusion in patients with systemic sepsis. Critical Care Medicine 2001; 29(7):1343-1349.

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Sensor augmented pump therapy lowers HbA1c

in suboptimally controlled type 1 diabetes; a

randomised controlled trial

J Hermanides, K NŒrgaard, D Bruttomesso, C Mathieu,

A Frid, CM Dayan, P Diem, C Fermon, IME Wentholt,

JBL Hoekstra, JH DeVries


ABSTRACT

Aims To investigate the efficacy of sensor augmented pump therapy vs. multiple

daily injection therapy in patients with suboptimally controlled type 1 diabetes.

Methods In this investigator initiated multicentre trial (Eurythmics Trial) in 8

outpatient centres in Europe we randomised 83 Diabetes type 1 patients (40

woman) currently treated with multiple daily injections, age 18-65 years and

haemoglobin A1c (HbA1c)>=8.2% to 26 weeks of treatment with either a sensor

augmented insulin pump (SAP, n=44) (Paradigm® REAL-Time) or multiple daily

injections (MDI, n=39). Change in HbA1c between baseline and 26 weeks, sensor

derived endpoints and patient reported outcomes were assessed.

Results In the SAP group 43/44 (98%) patients completed the trial and in the MDI

group 35/39 (90%). Mean HbA1c at baseline and 26 weeks changed from 8.46% (SD

0.95) to 7.23% (SD 0.65) in the SAP group and from 8.59% (SD 0.82) to 8.46% (SD

1.04) in the MDI group. Mean difference in change in HbA1c after 26 weeks was -

1.21% (95% confidence interval -1.52 to -0.90, p<0.001) in favour of the SAP group.

This was achieved without an increase in percentage of time spent in

hypoglycaemia: between-group difference 0.0% (95% confidence interval -1.6 to 1.7,

p=0.96). Problem Areas In Diabetes and Diabetes Treatment Satisfaction

Questionnaire scores improved in the SAP group.

Conclusions Sensor augmented pump therapy effectively lowers HbA1c in type 1

diabetes patients suboptimally controlled on MDI

Clinical Trial Registration controlled-trials.com ISRCTN22472013

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INTRODUCTION

Intensive treatment of type 1 diabetes mellitus has become the standard of care

since it reduces diabetes-related complications marked by haemoglobin A1c (HbA1c)

reduction1. However, many patients do not reach the HbA1c target of <7%.2

Achieving euglycaemia is limited by symptomatic hypoglycaemia and undetected

hypo- and hyperglycaemia.3-5 With technical developments, continuous

subcutaneous insulin infusion (CSII) and continuous subcutaneous glucose

monitoring (CGM) have become available, providing more flexible insulin dosing

schemes and a continuous stream of glucose data.6;7 Indeed, recent meta-analyses

indicate that CSII is effective in lowering HbA1c in patients with high HbA1c at

start and in reducing severe hypoglycaemia in patients at risk.8-10 The Juvenile

Diabetes Research Foundation trial found a difference in change in HbA1c of -0.5%

in favour of CGM treated type 1 diabetes patients, as compared to a control group

performing self measurement of blood glucose (SMBG).11 Recently, an integrated

real-time CGM and insulin pump system has entered the market, with alarm

function for hypo- and hyperglycaemia and a mealtime bolus advisor (Paradigm

REAL-Time System; Medtronic MiniMed, Inc., Northridge, CA).12 Today, this

integrated platform is the most extensive technical support one can provide to

diabetes patients, without taking over some parts of patient self-control, the next

step towards the closed-loop or artificial pancreas.13 It could have potential for

patients failing on intensive optimised treatment with multiple daily injections

(MDI) and SMBG. As with any new device, it needs to be compared to standard

care to evaluate efficacy.

Therefore we compared sensor augmented pump therapy to intensive multiple daily

injection therapy in suboptimally controlled type 1 diabetes mellitus patients in a

randomised controlled multicentre trial.

METHODS

Study Design and Patient Population

This was a 26-week open-label, multinational, multi-centre randomised controlled

clinical trial, performed between April 2007 and January 2009 in 8 centres

experienced in clinical research and pump therapy. Centres qualified for the trial

after being able to verify sensor augmented insulin-pump experience with at least

three type 1 diabetes patients for a treatment period of two weeks.

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Eligible patients were aged 18 to 65 years, diagnosed with type 1 diabetes at least 1

year prior to study participation, currently treated with optimised multiple daily

injections, but having an HbA1c >=8.2% at screening, despite repeated attempts to

improve this by re-education, including the availability insulin pump therapy. This

cut-off for HbA1c was chosen because it represents the median HbA1c in the DCCT-

EDIC cohort.14 Patients treated with human insulin could also be included if

analogues had been tried in the past.

Exclusion criteria were hearing problems or impaired vision that might hinder

recognition of alarms, substance abuse other than nicotine, abdominal skin

abnormalities that might hinder subcutaneous insertion, current treatment for any

psychiatric disorder other than depression, treatment with CSII in the six months

prior to study entry, pregnancy, heart failure, cancer or kidney disease, and

participation in another therapeutic study. The study was approved by the ethics

committees of all participating centres and performed in accordance with the

declaration of Helsinki and Good Clinical Practice. All patients provided written

informed consent. Quality of the data was monitored by the Academic Medical

Centre (AMC) Clinical Research Unit, Amsterdam. The study is registered with

controlled-trials.com, ISRCTN22472013, as the EURopean (Y) Trial to lower HbA1c

by Means of an Insulin pump augmented by a Continuous glucose Sensor

(Eurythmics trial).

Study Procedures

The study flowchart is provided in Figure 1. Prior to randomisation, participants

underwent 6 days of CGM measurements (Guardian® REAL-Time Clinical,

Medtronic MiniMed, Inc., Northridge, CA), without visible sensor values or alarms.

Treatment advice was given based on the downloaded data to optimise therapy at

start in all patients. Hereafter, patients were randomised to receive either 26

weeks of treatment with a sensor augmented insulin pump (SAP) or standard care

with continuation of MDI therapy, optimised by the advice derived from the

preceding 6 days of CGM. Randomisation was stratified per centre in computer

generated sequences unknown to the investigator. Via a secured internet database

(Oracle Corporation, Redwood City, CA) the investigators performed the

randomisation. Patients in the SAP group received a Paradigm REAL-Time System

(Medtronic MiniMed, Inc., Northridge, CA), comprising a needle type subcutaneous

continuous glucose sensor with an insulin pump delivering short-acting insulin

analogues. A signal is generated by a glucose-oxidase reaction in the sensor and is

sent wirelessly to a monitor via radio frequency. Glucose values are displayed on a

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pager-sized insulin pump device. For initiation the monitor requires calibrations at

2 and before 6 hours and hereafter two calibrations per 24 hours. A glucose value is

displayed every 5 minutes. The system is equipped with an alarm function for

upcoming hypo- and hyperglycaemia. When responding to an alarm, patients were

instructed to verify the glucose displayed by the sensor with a SMBG and make

treatment decisions based on the SMBG value. Alarm settings were initially set

and adjusted at discretion of both the patient and treating study staff.

Furthermore, a Bolus Wizard® program provided mealtime insulin dosing advice to

the patient, taking into account current glucose value (based on sensor data or

SMBG values), estimated amount of active insulin, amount of carbohydrates in the

upcoming meal, individual insulin sensitivity, carbohydrate sensitivity, and glucose

target values. Thus, this device provides the patient with a combination of

continuous glucose data stream with the optimal way to deliver insulin, enabling

optimal self-management. Patients were trained to use the device within two weeks

after randomisation and to change both the insulin catheter and glucose sensor

every three days. At 13- and 26 weeks patients visited the investigating centre and

data from the sensor was downloaded and if necessary therapy adjustments were

made based on the downloaded data. No specific instructions with regard to insulin

dosing or other device specific adjustments were provided to the patients other than

during the training phase and 13- and 26 week visit.

The patients in the MDI group received standard care, which included MDI therapy

with rapid-acting insulin analogue before meals and long-acting analogues or

human insulin. The advised SMBG frequency was at least 3 times per day. The

MDI patients received a blinded 6-day CGM measurement before the 13- and 26

week visits, using the Guardian® REAL-Time Clinical. No treatment advice was

provided based on the CGM measurements at these time points. They did receive

regular treatment- and dose adjustment advice as usual.

In the second part of the trial (between 13- and 26 weeks) patients in the SAP and

MDI group were to receive the same amount of study staff attention. Carbohydrate

counting was strongly encouraged in all participants, but not compulsory. For both

groups blood was drawn at baseline, 13-, and 26 weeks and stored at -80°C for

HbA1c determination in the central lab in the Netherlands after the trial. All

samples were determined in one-run using the High Performance Liquid

Chromatography method (Tosoh-G8, Tosoh, Japan, DCCT aligned). The data from

the devices was downloaded at baseline, 13-, and 26-weeks via a web-based

application in the investigating centre, using Carelink® Clinical (Medtronic

MiniMed, Inc., Northridge, CA). The Carelink® system was not yet available for

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outpatient use at start of our trial. The contact time with the study personnel was

registered. The occurrence of severe hypoglycaemia (clinical episode of

hypoglycaemia <=2.8 mmol/l, resulting in seizure or coma, intravenous glucose or

glucagon, or any 3rd party assistance) and ketoacidosis were considered adverse

events. Finally, serious adverse events (SAE) and their possible relation to the

study and/or device were listed.

Outcome Measures

The primary outcome was difference in HbA1c between baseline and 26 weeks. Also

we calculated the change between 13 and 26 weeks. The sensor data were analyzed

for minutes in hyperglycaemia >11.1 mmol/l and hypoglycaemia <4.0 mmol/l

expressed as the percentage of the total monitoring time. Also the number of hyper-

and hypoglycaemia events per day were calculated. For both groups a time-window

of up to six-days prior to downloading of the available data was used to determine

Figure 1- study flowchart

Abbreviations: SAP=Sensor Augmented Pump group, MDI= Multiple Daily Injections, CGM= Continuous Glucose Monitoring, T=Time in weeks, HbA1c=Haemoglobin A1c

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the sensor derived endpoints. Sensor use was calculated for the SAP group as the

average hours/week sensor use and percentage of sensor use during the whole trial.

Furthermore, the proportion of patients reaching HbA1c <7%, the contact time with

the study personnel, the number of SMBG measurements per three weeks and total

daily insulin dose per patient were calculated.

Patient Reported Outcomes (PRO)

Questionnaires were administered at baseline and end of trial. Health-related

quality of life was assessed using the SF-36 version 2 (SF-36v2)15, which comprises

eight subscales: Physical Functioning (PF), Role-Physical (RP), Bodily Pain (BP),

General Health (GH), Vitality (VT), Social Functioning (SF), Role-Emotional (RE),

and Mental Health (MH). Scores were transformed to a 0 to 100 (highest health

score) scale. The Problem Areas in Diabetes Scale (PAID) is a 20 item questionnaire

that scores diabetes related physiological distress.16 Scores were transformed to a

0-100 scale, with higher scores indicating more problems. The Diabetes Treatment

Satisfaction Questionnaire (DTSQ) comprises six items and is scored on a 0-36

scale, with higher scores indicating higher satisfaction. Two additional items

concern perceived frequency of hypo- and hyperglycaemia, scored on a 0 (lowest

frequency) to 6 (highest frequency) scale.17 Finally, the 13-item worry-subscale of

the Hypoglycaemia Fear Survey (HFS) was administered.18 The HFS scores were

transformed to a 0-100 scale, higher scores indicating more hypoglycaemia related

fear. The HFS and PAID could not be administered in all centres, due to lack of

validated translations.

Statistical Methods

Assuming a standard deviation of 1.3% in HbA1c, a two-sided t-test with an alpha

level of 0.05, sample size calculations indicated that 36 patients per group would be

needed to detect a between-group difference of 1.0% in HbA1c with 90% power. A

drop-out rate of 30% was anticipated, and the original goal was to randomise 52

patients per group. As the drop-out rate was much lower than expected, we stopped

inclusion after having randomised 83 patients.

All analyses were predefined but the percentage of participants reaching the <7%

target and analysis on sensor use. Analyses were based on the intention to treat

principle. The primary outcome measure, change in HbA1c between baseline and 26

weeks, was analyzed using an ANCOVA model with treatment and centre as fixed

effects and the baseline HbA1c value as covariate. If a patient dropped out after 13

weeks, the last observation was carried forward (LOCF) to 26 weeks. HbA1c after 13

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weeks, the sensor-derived endpoints and questionnaire scores were analysed using

the ANCOVA model as defined above and are reported as mean with standard

deviation (SD). The difference in change in HbA1c between 13 and 26 weeks was

analysed using repeated measurements ANOVA with centre and treatment as fixed

effects and baseline HbA1c, time and the interaction between treatment as covariates

and subject as random factor. Hours of sensor use/week in the SAP group during the

trial were related to change in HbA1c after 26 weeks and corrected for baseline

HbA1c. Differences in contact time were reported as median with IQR and compared

using the Mann-Whitney U test. The differences in proportions of patients reaching

HbA1c <7% and experiencing a severe hypoglycaemic episode were calculated using

the chi-square test.

RESULTS

Patients

The participant flow is displayed in Figure 2. After screening ninety-three patients,

eighty-seven started the trial. Before randomisation, 4 patients dropped-out after

the baseline sensor measurement, all indicating that they did not tolerate the

sensor. Eighty-three patients were randomised. Baseline characteristics of the two

groups were comparable as displayed in Table 1. In the SAP group 43/44 (98%)

patients completed the trial and 35/39 (90%) in the MDI group. The patient

dropping out in the SAP group did not tolerate the intensity of the SAP treatment.

In the MDI group one patient stopped for unknown reasons, one patient due to

surgery for aorta bifurcation prosthesis, one patient because of dissatisfaction

about the randomisation outcome, and one patient was lost to follow up. Three

baseline HbA1c blood samples got lost. HbA1c analyses could be performed on 41/44

patients from the SAP group (1 drop-out, 2 missing baseline samples) and 36/39

patients from the MDI group (4 drop out, with 2 LOCF, one missing baseline

sample). After 26 weeks, the total daily insulin dose in the SAP group decreased to

46.7 (SD 16.5) units and somewhat increased in the MDI group to 57.8 (SD 18.1)

units per day, with a baseline and centre adjusted difference in change between the

groups of -11.0 units per day (95% confidence interval -16.1 to -5.9, p<0.001). Prior

to the study patients in the SAP group used 76 (SD 40) SMBG per three weeks,

which increased to 87 (SD 43) at the end of the trial. For patients in the MDI group,

the number of SMBGs per three weeks increased from 70 (SD 38) to 82 (SD 48).

This was a non-significant difference in change, p=0.38.

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Table 1- baseline characteristics

SAP (n=44) MDI (n=39) Age, years (mean, (SD)) 39.3 (11.9) 37.3 (10.7) Female sex (n, (%)) 22 (50.0) 18 (46.2) Diabetes duration, years (mean (SD)) 16.9 (10.7) 21.0 (9.4) HbA1c baseline, % (mean (SD)) 8.47 (0.94) 8.64 (0.86) Total daily insulin dose, units (mean (SD)) 54.5 (21.0) 53.9 (14.0) Patients experiencing severe hypoglycaemia the last 12 months before randomisation (n, (%))

6 (13.6)

3 (7.7)

Country (n, (%)) -Denmark -Switzerland -The Netherlands -Sweden -France -United Kingdom -Belgium -Italy

13 (29.5) 2 (4.5) 6 (13.6) 6 (13.6) 1 (2.3) 6 (13.6) 6 (13.6) 6 (13.6)

11 (28.2) 2 (5.1) 5 (12.8) 5 (12.8) 0 (0.0) 4 (10.3) 6 (15.4) 6 (15.4)

Figure 2- participant flow, horizontal arrows indicating reasons for dropping out

Abbreviations: SAP=Sensor Augmented Pump group, MDI=Multiple Daily Injections group, CGM= Continuous Glucose Monitoring, HbA1c=Haemoglobin A1c

Abbreviations: SAP=Sensor Augmented Pump group, MDI=Multiple Daily Injections group, HbA1c= haemoglobin A1c

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Glycaemic Control

The least square mean (LS mean) from the ANCOVA model for treatment effect,

adjusted for baseline HbA1c and centre was -1.21% (95% confidence interval -1.52 to

-0.90, p<0.001). The crude mean difference in change in HbA1c after 26 weeks was -

1.10% (95% confidence interval -1.47 to -0.73, p<0.001) in favour of the SAP group

(Figure 3 and Table 2). The LS mean difference in change in HbA1c after 13 weeks

was -1.22% (95% confidence interval -1.59 to -0.86, p<0.001). No significant

difference in change was observed between 13 and 26 weeks, p=0.85. The

proportion of patients reaching EASD/ADA HbA1c target of <7% was 34% in the

SAP group and 0% in the MDI group, p<0.001. The LS mean difference in change in

percentage of sensor-time spent in hyperglycaemia after 26 weeks was -17.3% (95%

confidence interval -25.1 to -9.5, p<0.001). No significant difference in change in

percentage of time spent in hypoglycaemia after 26 weeks was found between

groups: 0.0% (95% confidence interval -1.6 to 1.7, p=0.96). The change in number of

hyper- or hypoglycaemic events did not differ between the groups. Sensor derived

endpoints are summarised in Table 3.

Figure 3- haemoglobin A1c (HbA1c) results

*The baseline and treating centre adjusted HbA1c change (ANCOVA-model) was: -1.21% (95% CI -1.52 to -0.90, p<0.001)

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Table 2: haemoglobin A

1c results

SAP (n=41)

HbA

1c % (m

ean (SD))

MD

I (n=36) H

bA1c %

(mean (SD

)) D

ifference (95 % C

I) p value*

Baseline

8.46 (0.95) 8.59 (0.82)

0.13 (N/A

)

13 weeks

7.29 (0.71) 8.55 (1.21)

1.25 (0.79 to 1.72) <0.001

� B

aseline-13 weeks

-1.17 (0.93) -0.05 (0.73)

-1.13 (-1.51 to -0.74) <0.001

LSM �

Baseline-13 w

eeks†

-1.22 (-1.59 to -0.86)

<0.001 26 w

eeks 7.23 (0.65)

8.46 (1.04) 1.23 (0.83 to 1.63)

<0.001 �

Baseline-26 w

eeks -1.23 (1.01)

-0.13 (0.56) -1.10 (-1.47 to -0.73)

<0.001 LSM

� B

aseline-26 weeks†

-1.21 (-1.52 to -0.90) <0.001

� 13-26 w

eeks -0.06 (0.57)

-0.09 (0.51) -0.03 (-0.27 to 0.21)

0.83 LSM

� 13-26 w

eeks‡

-0.02 (-0.27 to 0.21)

0.85

Abbreviations: H

bA1c = haem

oglobin A1c, S

AP

=Sensor A

ugmented P

ump group, M

DI=M

ultiple Daily Injections group, N

/A= not applicable, LSM

= least square mean,

CI=confidence interval

*p value for comparison betw

een the SA

P and M

DI group

†Difference in H

bA1c change in H

bA1c betw

een the SA

P and M

DI group after 13 and 26 w

eeks. LSM

from the A

NC

OV

A m

odel, adjusted for investigating centre and baseline H

bA1c

‡Difference in change in H

bA1c betw

een the SAP

and MD

I Group betw

een 13 weeks and 26 w

eeks from repeated m

easurements A

NO

VA

, adjusted for centre, baseline H

bA1c , the factor tim

e and the interaction between treatm

ent and time. P

atient identification number w

as included as random factor

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Tabl

e 3:

sen

sor d

ata

SA

P (n

=40)

m

ean

(SD

) M

DI (

n=31

) m

ean

(SD

) D

iffer

ence

(95

% C

I) p

valu

e*

% o

f tim

e in

hyp

ergl

ycae

mia

B

asel

ine

38.0

(17.

4)

40.1

(18.

4)

2.1

(N/A

)

26 w

eeks

21

.6 (1

2.2)

38

.2 (2

1.5)

16

.5 (7

.8 to

25.

2)

<0.0

01

LSM

� B

asel

ine-

26 w

eeks

†

-1

7.3

(-25

.1 to

-9.5

) <0

.001

%

of t

ime

in h

ypog

lyca

emia

B

asel

ine

3.9

(4.7

) 2.

5 (2

.8)

1.4

(N/A

)

26 w

eeks

2.

7 (3

.4)

2.5

(3.6

) 0.

2 (-

1.4

to 1

.9)

0.79

LS

M �

Bas

elin

e-26

wee

ks†

0.0

(-1.

6 to

1.7

) 0.

96

Num

ber o

f hyp

ergl

ycae

mic

eve

nts‡

B

asel

ine

2.4

(0.6

) 2.

5 (0

.6)

0.2

(N/A

)

26 w

eeks

2.

1 (0

.8)

2.2

(0.7

) 0.

2 (-

0.2

to 0

.5)

0.30

LS

M �

Bas

elin

e-26

wee

ks†

-0.2

(-0.

6 to

0.1

) 0.

21

Num

ber o

f hyp

ogly

caem

ic e

vent

s‡

Bas

elin

e 0.

7 (0

.1)

0.5

(0.5

) 0.

2 (N

/A)

26

wee

ks

0.7

(0.7

) 0.

6 (0

.7)

0.1

(-0.

2 to

0.5

) 0.

40

LSM

� B

asel

ine-

26 w

eeks

†

-0

.1 (-

0.5

to 0

.2)

0.50

Abb

revi

atio

ns: H

bA1c

= ha

emog

lobi

n A

1c, S

AP

=Sen

sor A

ugm

ente

d P

ump

grou

p, M

DI=

Mul

tiple

Dai

ly In

ject

ions

gro

up, N

/A=

not a

pplic

able

, LSM

= le

ast s

quar

e m

ean,

CI=

conf

iden

ce in

terv

al

Hyp

ergl

ycae

mia

>11

.1 m

mol

/l, H

ypog

lyca

emia

<4.

0 m

mol

/l *p

val

ue fo

r com

paris

on b

etw

een

the

SA

P a

nd M

DI g

roup

†D

iffer

ence

in c

hang

e be

twee

n th

e S

AP

and

MD

I gro

up a

fter 2

6 w

eeks

; LS

M fr

om th

e A

NC

OV

A m

odel

, adj

uste

d fo

r inv

estig

atin

g ce

ntre

and

ba

selin

e va

lues

‡N

umbe

r of e

vent

s, s

tand

ardi

sed

per d

ay. A

n ev

ent w

as c

ount

ed w

hen

the

sens

or g

luco

se v

alue

cro

ssed

the

hype

r- o

r hyp

ogly

caem

ia

thre

shol

d, fo

llow

ed b

y a

30 m

inut

e pe

riod

betw

een

4.0

mm

ol/l

and

11.1

mm

ol/l

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Patient Reported Outcomes

The patient reported outcomes are shown in Table 4. The difference in change in

the PAID score was significant -7.9 (95% confidence interval -15.1 to -0.61, p=0.03)

in favour of the SAP group. Also the DTSQs and DTSQ „perceived frequency of

hyperglycaemia‰ scores improved significantly more in SAP group as compared to

the MDI group. For the SF-36v2 questionnaire, only the change in the General

Health and Social Functioning subscales differed significantly, both in favour of the

SAP group.

Contact Time and Sensor Usage

The median total contact time during the trial was 240 minutes (IQR 195 to 353) in

the MDI group and 690 minutes (526 to 1028) in the SAP group (p<0.001). The

median contact time in the SAP and MDI group before week 13 was 553 minutes

(423-825) and 135 minutes (108 to 218) respectively (p=0.001). Between week 13

and the end of the trial this was 75 minutes (60 to 120) in the SAP group and 60

minutes (40 to 75) in the MDI group (p=0.001).

The mean sensor use in the SAP group was 4.5 (SD 1.0) days/per week over the

whole trial period and 79% of the patients using the sensor more than 60% of the

time. The Bolus Wizard® was used by 86% of the SAP group patients at the end of

the trial. There was no evident relation between sensor use and HbA1c decrease

within the SAP group, when adjusted for baseline HbA1c, with a regression

coefficient of 0.006, p=0.20. Furthermore, when examining contact time, sensor use,

age, sex and baseline HbA1c as predictors for HbA1c decrease after 26 weeks in a

multivariate linear regression model, only baseline HbA1c was a significant

predictor (�= -0.80, 95% CI -1.06 to -0.53, p<0.001).

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Table 4- patient reported outcomes

SAP mean (SD)

MDI mean (SD)

Difference (95 % CI) p value*

Problem Areas In Diabetes scale N=30 N=24 Baseline 32.4 (18.8) 26.5 (18.4) 5.8 (N/A) 26 weeks 21.0 (19.3) 23.7 (19.4) 2.7 (-7.9 to 13.4) 0.61 LSM � Baseline-26 weeks† -7.9 (-15.1 to -0.61) 0.03 Hypoglycaemia Fear Survey N=35 N=28 Baseline 29.8 (19.2) 21.0 (17.7) 8.8 (N/A) 26 weeks 24.1 (20.2) 20.3 (16.9) 3.9 (-5.7 to 13.4) 0.42 LSM � Baseline-26 weeks† -3.2 (-10.0 to 3.7) 0.36

Diabetes Treatment Satisfaction Questionnaire

N=41 N=35

Baseline 21.6 (5.5) 22.5 (6.3) 1.0 (N/A) 26 weeks 32.4 (3.5) 23.8 (6.2) 8.6 (6.2 to 11.0) <0.001 LSM � Baseline-26 weeks† 9.3 (7.3 to 11.3) <0.001 Perceived frequency of hyperglycaemia Baseline 4.4 (1.2) 4.3 (1.3) 0.1 (N/A) 26 weeks 2.4 (1.2) 3.9 (1.2) 1.5 (1.0 to 2.1) <0.001 LSM � Baseline-26 weeks† -1.6 (-2.1 to -1.1) <0.001 Perceived frequency of hypoglycaemia Baseline 2.3 (1.3) 2.3 (1.4) 0.1 26 weeks 2.4 (1.2) 2.2 (1.3) 0.2 (-0.4 to 0.8) 0.51 LSM � Baseline-26 weeks† -0.3 (-0.8 to 0.3) 0.34

SF-36 N=42 N=33 Baseline Physical Functioning Role-Physical Bodily Pain General Health Vitality Social Functioning Role-Emotional Mental Health

89.4 (14.5) 76.8 (23.8) 78.9 (25.4) 55.5 (20.3) 53.9 (20.0) 81.5 (20.3) 84.9 (20.4) 72.6 (14.8)

90.5 (14.3) 84.4 (19.3) 78.7 (23.0) 59.8 (22.3) 61.0 (23.7) 86.4 (21.0) 89.6 (16.7) 77.9 (20.2)

1.0 (N/A) 7.6 (N/A) 0.2 (N/A) 4.3 (N/A) 7.1 (N/A) 4.8 (N/A) 4.4 (N/A) 5.3 (N/A)

26 weeks Physical Functioning Role-Physical Bodily Pain General Health Vitality Social Functioning Role-Emotional Mental Health

92.7 (11.2) 85.7 (20.7) 79.9 (24.4) 67.7 (21.6) 66.7 (20.2) 89.3 (16.0) 87.1 (19.6) 79.2 (12.5)

91.4 (12.7) 87.3 (20.4) 78.7 (22.6) 63.1 (19.1) 65.2 (19.3) 82.2 (25.2) 88.0 (16.0) 76.8 (16.5)

1.4 (-4.1 to 6.9) 1.6 (-11.2 to 8.0) 1.3 (-9.7 to 12.2) 4.5 (-5.0 to 14.1) 1.5 (-7.7 to 10.7) 7.1 (-3.0 to 17.2) 0.9 (-7.6 to 9.4) 2.3 (-4.3 to 9.0)

0.62 0.74 0.82 0.35 0.74 0.17 0.83 0.49

LSM � Baseline-26 weeks† Physical Functioning Role-Physical Bodily Pain General Health Vitality Social Functioning Role-Emotional Mental Health

2.2 (-0.7 to 5.1) 2.1 (-6.1 to 10.3) 1.7 (-7.6 to 11.0) 7.9 (0.5 to 15.3) 6.4 (-1.2 to 14.1) 9.8 (1.3 to 18.3) 0.9 (-6.6 to 8.4) 4.8 (-1.6 to 11.1)

0.13 0.62 0.71 0.04 0.10 0.02 0.81 0.14

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Hypoglycaemia and Adverse Events

There was a non-significant difference in the occurrence of severe hypoglycaemia,

with 4 episodes in the SAP group (9%) and 1 episode in the MDI group (3%),

p=0.21. This is 19 episodes /100 person years in the SAP group and 6/100 person

years in the MDI group. All patients in the SAP group were wearing the device

during the severe hypoglycaemia. In total, 7 SAEÊs were reported, of which 2

occurred in the SAP group and 5 in the MDI group. Only one SAE was reported as

being related to the device in the SAP group, where the patient was admitted to the

hospital for ketoacidosis because of pump-failure. Other SAEs were: surgery for

aorta bifurcation prosthesis, hemianopsia, respiratory tract infection, and

ketoacidosis (x2) in the MDI group and acute gastritis in the SAP group. Twenty

patients reported 26 probable or possible device related adverse events. Of these, 17

patients reported skin related problems (itch/exanthema/infection/redness/plaster

allergy/bruising/hematoma) at the sensor or insulin infusion site.

DISCUSSION

Sensor augmented pump therapy (SAP) significantly lowered HbA1c after 26 weeks

by -1.21% as compared to multiple daily injection therapy (MDI) without a

significant increase in hypoglycaemia. SAP therapy was associated with an

improvement in treatment satisfaction and diabetes related distress. These results

were observed in patients failing on optimal MDI and SMBG therapy in specialized

CSII centres.

This is the first trial with adequate duration and sample size comparing SAP to

MDI therapy in type 1 diabetes patients with suboptimal control. We thought it to

be appropriate to compare SAP therapy to MDI, as the number of patients using

insulin pump therapy in stead of MDI is relatively small6 Therefore, MDI reflects

the current standard of care. Furthermore, previous trials have already

investigated the addition of CGM to MDI or pump therapy and the initiation of

Table 4 (Left page)- patient reported outcomes Abbreviations: SAP=Sensor Augmented Pump group, MDI=Multiple Daily Injections group, N/A= not applicable, LSM= least square mean, CI=confidence interval *p value for comparison between the SAP and MDI group †Difference in change between the SAP and MDI group after 26 weeks; LSM from the ANCOVA model, adjusted for investigating centre and baseline values

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SAP therapy vs. the initiation of insulin pump therapy.11;19;20 The efficacy of CSII

therapy with short-acting analogues alone is about -0.3% as compared to MDI

therapy.10 This is however highly dependent on baseline HbA1c10, The JDRF trial

demonstrated the efficacy of sensor use as compared to MDI therapy in adult type 1

diabetes patients, lowering HbA1c by -0.5% from 7.6% at baseline.11 Other trials

investigating the efficacy of sensor use in poorly regulated type 1 diabetes patients

have found effects on HbA1c decrease ranging from 0 to -0.6%.21 Thus, the

magnitude of change of -1.21% in the Eurythmics trial seems at least a summation

of the effects of CSII and CGM therapy. This supports the notion that integration of

CSII and CGM provides a new treatment platform. This is further substantiated by

the use of the mealtime insulin dose advisor by 86% of the SAP group, establishing

active communication between the insulin pump, CGM and the patient.

A limitation of the study was the by necessity unblinded character. Moreover, the

patients in the SAP group received more attention from the study staff especially in

the first part of the trial, because of the device specific training phase. This

difference in attention, to the best of our knowledge now quantified for the first

time in a diabetes device trial, may contribute to HbA1c lowering.20;22 We believe

however that a temporary increase in time spent with the patient reflects the real-

life situation and can not be set apart when starting SAP therapy in patients using

MDI. Furthermore, in multivariate analysis, the duration of contact time was not a

significant predictor of HbA1c decrease and the HbA1c lowering reached in the first

part of the trial in the SAP group was sustained between 13 and 26 weeks, when

the difference in attention given to both study groups was only 15 minutes.

Notably, in the MDI group there was a minimal HbA1c decrease and no study effect

was observed. Perhaps this is not so surprising when considering that all other

attempts to optimise their MDI/SMBG treatment failed, and the limited additional

attention given to these patients.

Prior to randomisation, 4 patients dropped out because they did not tolerate the

CGM device, but hereafter only 1 patient randomised to SAP did not complete the

trial. In the JDRF trial, patients had to complete a run-in phase before they could

be randomised.11 Thus in both trials the efficacy of sensor or SAP therapy was

investigated in patients with type 1 diabetes, who proved to be tolerating the

device.

The rate of severe hypoglycaemia at 26 weeks was low in both groups, with a non-

significantly higher rate in the SAP group (9% vs. 3%, p=0.21), that seemed to be

more susceptible to severe hypoglycaemia at baseline as compared to the MDI

group. Thus, with a more pronounced HbA1c lowering, the severe hypoglycaemia

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frequencies are similar to those found in the JDRF CGM trial and much lower than

observed in the DCCT.1;11 The apparent lack of decrease in hypoglycaemia in the

SAP group can have several explanations. First, due to the decline of cognitive

functions during hypoglycaemia patients are less adequate in responding to

acoustic or vibration alarms.23 Also the CGM device is more likely to be put aside

during high risk activities such as intensive sports. Finally, the sensor inaccuracy

can contribute to missing episodes of hypoglycaemia. We did not specifically assess

sensor performance in this trial. The accuracy of glucose sensors has however been

investigated extensively and is known to be deteriorating in the hypoglycaemic

range.7

The analyses of the sensor derived data also show that a significant decrease in

hyperglycaemia AUC in the SAP group as compared to the MDI group is not

accompanied by an increase in time spent in hypoglycaemia. Also the number of

hypoglycaemic events did not differ between both groups. What did change was the

percentage of time spent in hyperglycaemia, which was significantly lower in the

SAP group after 26 weeks. The number of hyperglycaemic events did not differ

significantly between the SAP and MDI group, suggesting that the beneficial effect

of the SAP treatment results from reducing the duration (and not the number) of

hyperglycaemic events as they occur.

We found an improvement in change in PRO scores concerning diabetes related

distress, perceived frequency of hyperglycaemia, general health, social functioning,

and treatment satisfaction. Furthermore, the use of SAP therapy was not

accompanied by an increase the fear- or perceived frequency of hypoglycaemia.

These PRO outcomes do not confirm historical fears of data overload due to CGM.

Sensor use in the trial was on average 4.5 days/week and most patients used the

sensor >60% of the time. In contrast with previous CGM trials, we could not find a

relation between CGM use and change in HbA1c.11;19;20 This trial was however not

designed to investigate this relationship. It is noteworthy that the substantial

improvement in glycaemic control with frequent sensor use was seen in a patient

group not easy to reach, under treatment in experienced pump centres and failing

on optimised MDI/SMBG therapy.

In conclusion, these study results show that, as compared to MDI treatment, SAP

treatment in motivated but suboptimally controlled type 1 diabetes patients results

in a considerable HbA1c reduction and improvement in quality of life, without

increasing hypoglycaemia. The magnitude of the difference in change in HbA1c of -

1.21% may be due to the combined effect of pump, sensor, Bolus Wizard® and the

process of starting SAP therapy in this hard to reach population.

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Acknowledgements

This trial was financially supported by Medtronic International Trading Sàrl. This

was an investigator-initiated trial. The funding source had an advising role in trial

design details and drafting of the report and was only involved in the collection of

the sensor data. The funding source had no role in the conduct of the analyses,

interpretation of the data or in the decision to approve publication. Chantal

Mathieu is a clinical researcher of the FWO (Fund for Research Flanders). We

further acknowledge the following people involved in the execution of the trial:

Hilde Morobé, Peggy Callewaert, Marijke Carpentier, Conny Peeters, Kate Green,

Dr. Danijela Tatovic, Dr. Silvana Costa, Dr. Andrea Egger, Dr. Andrea Lanker,

Peggy Wentzel, Dorien Dragt, Dr. C.B. Brouwer, Merete Meldgaard, Ingrid

Wetterholtz, and Hanna Söderling.

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REFERENCES

(1) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med 1993; 329(14):977-986.

(2) American Diabetes Association. Standards of Medical Care in Diabetes--2008. Diabetes Care 2008; 31(Supplement_1):S12-S54.

(3) Bode BW, Schwartz S, Stubbs HA, Block JE. Glycemic Characteristics in Continuously Monitored Patients With Type 1 and Type 2 Diabetes: Normative values. Diabetes Care 2005; 28(10):2361-2366.

(4) Cryer PE. The Barrier of Hypoglycemia in Diabetes. Diabetes 2008; 57(12):3169-3176.

(5) Wentholt IM, Maran A, Masurel N, Heine RJ, Hoekstra JB, DeVries JH. Nocturnal hypoglycaemia in Type 1 diabetic patients, assessed with continuous glucose monitoring: frequency, duration and associations. Diabet Med 2007; 24(5):527-532.

(6) Pickup J, Keen H. Continuous subcutaneous insulin infusion at 25 years: evidence base for the expanding use of insulin pump therapy in type 1 diabetes. Diabetes Care 2002; 25(3):593-598.


(8) Pickup JC, Sutton AJ. Severe hypoglycaemia and glycaemic control in Type 1 diabetes: meta-analysis of multiple daily insulin injections compared with continuous subcutaneous insulin infusion. Diabet Med 2008; 25(7):765-774.

(9) Jeitler K, Horvath K, Berghold A, Gratzer TW, Neeser K, Pieber TR et al. Continuous subcutaneous insulin infusion versus multiple daily insulin injections in patients with diabetes mellitus: systematic review and meta-analysis. Diabetologia 2008; 51(6):941-951.

(10) Retnakaran R, Hochman J, DeVries JH, Hanaire-Broutin H, Heine RJ, Melki V et al. Continuous Subcutaneous Insulin Infusion Versus Multiple Daily Injections: The impact of baseline A1c. Diabetes Care 2004; 27(11):2590-2596.




(14) Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Sustained Effect of Intensive Treatment of Type 1 Diabetes Mellitus on Development and Progression of Diabetic Nephropathy: The Epidemiology of Diabetes Interventions and Complications (EDIC) Study. JAMA 2003; 290(16):2159-2167.

(15) Ware JEJP. SF-36 Health Survey Update. Spine 2000; 25(24):3130-3139.

(16) Polonsky WH, Anderson BJ, Lohrer PA, Welch G, Jacobson AM, Aponte JE et al. Assessment of diabetes-related distress. Diabetes Care 1995; 18(6):754-760.

(17) Bradley C. The Diabetes Treatment Satisfaction Questionnaire:DTSQ. Handbook of Psychology and Diabetes: A Guide to Psychological Measurement in Diabetes Research and Practice. Chur, Switzerland: Harwood Academic Publishers; 1994. 111-132.

(18) Cox DJ, Irvine A, Gonder-Frederick L, Nowacek G, Butterfield J. Fear of hypoglycemia: quantification, validation, and utilization. Diabetes Care 1987; 10(5):617-621.

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(19) Hirsch IB, Abelseth J, Bode BW, Fischer JS, Kaufman FR, Mastrototaro J et al. Sensor-Augmented Insulin Pump Therapy: Results of the First Randomized Treat-to-Target Study. Diabetes Technology & Therapeutics 2008; 10(5):377-383.


(21) Hermanides J, DeVries JH. Sense and nonsense in sensors. Diabetologia 2010; Apr;53:593-596..

(22) O'Connell M, Donath S, O'Neal D, Colman P, Ambler G, Jones T et al. Glycaemic impact of patient-led use of sensor-guided pump therapy in type 1 diabetes: a randomised controlled trial. Diabetologia 2009; 52(7):1250-1257.

(23) Cryer PE, Davis SN, Shamoon H. Hypoglycemia in Diabetes. Diabetes Care 2003; 26(6):1902-1912.

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Sensor augmented insulin pump therapy to treat

hyperglycaemia at the coronary care unit; a

randomised controlled pilot trial

J Hermanides, AE Engström, IME Wentholt, KD Sjauw,

JBL Hoekstra, JPS Henriques, JH DeVries

Diabetes Technology & Therapeutics, In Press

ABSTRACT

Background The relationship between admission hyperglycaemia and adverse

outcome in myocardial infarction has been shown consistently. However, achieving

and maintaining normoglycaemia in STEMI-patients has proven difficult. This

study aimed to investigate the efficacy of sensor augmented insulin pump therapy

(SAP) to treat hyperglycaemia.

Methods In a randomised controlled pilot trial, we assigned 20 patients, aged 30-80

years, admitted with STEMI and hyperglycaemia (>=7.8 mmol/l) to receive either

48 hours of strict glycaemic control with an subcutaneous insulin pump augmented

with a continuous glucose monitor (SAP group) or to treatment according to

standard practice (Control group) with glucose measured by blinded CGM. The

main outcome measure proportion of time spent in hyperglycaemia.

Results The median treatment time was 47.0 hours (IQR 46.2 to 48.0) in the SAP

group and 44.6 (IQR 22.0 to 48.6) in the Control group. The median proportion of

time >=7.8 mmol/l was 14.6% (IQR 10.5 to 18.5) in the SAP group and 36.3% in the

control group (IQR 26.0 to 80.4), p=0.006. The proportion of time <=3.9 mmol/l was

8.9% (IQR 8.3 to 12.5) in the SAP group vs. 0% (IQR 0 to 2) in the Control group,

p<0.001. Plasma glucose decreased significantly in SAP group as compared to the

Control group, p=0.025.

Conclusions SAP therapy is effective in reducing hyperglycaemia in STEMI

patients on the coronary care unit. This is accompanied by a small but significant

increase in hypoglycaemia. Although a promising tool for in-hospital

hyperglycaemia therapy, SAP needs improvement before continuing to large scale

randomised controlled trials.

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INTRODUCTION

In patients admitted for acute myocardial infarction (AMI), elevated admission

glucose levels are strongly predictive of heart failure, shock and mortality. 1-4 In the

first randomised controlled study to treat Diabetic Patients with Acute Myocardial

Infarction (DIGAMI), intensive glucose lowering therapy significantly reduced both

short- and long term mortality. 5;6 Two following trials aiming to treat

hyperglycaemia in patients with- and without diabetes mellitus and AMI (DIGAMI

2 and HI-5) could not reproduce earlier results.7;8 The authors attributed this to

disappointing recruitment rates, but also to the lack of significant difference in

glucose control achieved between the intervention and control groups. Indeed, post-

hoc analyses showed that hyperglycaemia remained an important predictor of

mortality.7;8 Recently, Kosiborod and colleagues showed that glucose normalisation

after AMI is associated with improved survival. 9 It thus seems important but

difficult to lower glucose in the coronary care setting. An important barrier to

implementation of intensive glucose control in the coronary care unit may be the

fear for hypoglycaemia that invariably accompanies glucose lowering therapy.10;11

Furthermore, it has proven hard to handle postprandial glucose excursions in

hospitalised patients who resume normal eating.12 The use of insulin pumps

augmented with a continuous subcutaneous glucose monitor (CGM) could improve

glucose lowering therapy in the coronary care unit (CCU). Using the continuous

stream of glucose data with preset alarms for hypo- and hyperglycaemia, the

subcutaneous insulin infusion rates can be adjusted to maintain euglycaemia.

Furthermore, a mealbolus advisor can be used to limit postprandial

hyperglycaemia.13 The system has already proven beneficial in treating patients

with type 1 diabetes in outpatient settings.14-16 In this study, we investigated the

efficacy of sensor augmented pump therapy to treat hyperglycaemia in patients

admitted to the CCU for AMI.

METHODS

Design and patients

We performed a randomised controlled pilot trial, including patients admitted to

the CCU of the Academic Medical Centre (Amsterdam, The Netherlands), a

university hospital. Patients with an admission glucose of >=7.8 mmol/l (measured

from a venous blood sample using the Accu-Check Inform®, Roche Diagnostics,

Basel Switserland), aged 30-80 years, and ST-elevation myocardial infarction

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(STEMI), treated by primary percutaneous coronary intervention (PCI), were

included. Exclusion criteria were known diabetes mellitus type 1, abdominal

abnormalities that might hinder either glucose measurement by the sensor or the

continuous subcutaneous insulin infusion and simultaneous participation in other

studies. The study was approved by the ethic committee of the Academic Medical

Centre and performed in accordance with the declaration of Helsinki. All patients

provided written informed consent. The study is registered with controlled-

trials.com, ISRCTN55085730.

Procedures

After inclusion, patients were randomised to either 48 hours of glucose control with

sensor augmented pump therapy (SAP group) or blinded continuous monitoring

only with treatment according to standard practice (Control group). A flow chart is

provided in Figure 1.

In the SAP group, preceding PCI, an intravenous starting insulin bolus was

injected based on the admission glucose, using an adapted standardised algorithm

(Appendix 1) from Bode et al and adjusted by Nobels et al. 17;18 The Paradigm®

Real-Time system (Medtronic MiniMed, Inc., Northridge, CA), combines a

continuous glucose sensor and insulin pump. Both need insertion subcutaneously in

the abdominal periumbilical area at different sites. Two hours after insertion and

Figure 1- study flow-chart, SAP=Sensor Augmented Pump, CGM= Continuous subcutaneous Glucose Monitor

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initiation (inherent to the system), the system could be calibrated and the CGM

and insulin pump were started. The CGM provides a glucose value every 5 minutes.

The alarm thresholds were set at 4.7 mmol/l for hypo- and 6.1 mmol/l for

hyperglycaemia. Because the frequency of the alarms requesting a blood glucose

check and recalibration was too time consuming for the nursing personnel we

changed the upper alarm threshold to 7.1 mmol/l after the first 5 patients in the

SAP group. On request of the nursing staff, the lower alarm threshold was set at

4.4 mmol/l and the upper at 7.5 mmol/l for the last two patients.

The nursing staff was instructed to adjust the basal insulin rates responding to

alarms according to the algorithm (Appendix 1). Using the BolusWizard® the study

staff calculated the mealtime insulin bolus. The BolusWizard® program provides a

mealtime insulin bolus advice taking into account the current glucose value, insulin

administration rate, patient specific insulin/carbohydrate sensitivity and the

amount of carbohydrates to be ingested. The insulin/carbohydrate sensitivity was

based on the total daily insulin need of the patient, which was calculated from the

amount of insulin needed to achieve normoglycaemia in each particular patient.

In the control group the CGM of the Paradigm® Real-Time system was inserted

before or shortly after PCI. The CGM readings were not made available to the

treating physician and the patient. If patients had glucose levels above 15 mmol/l

during 3 consecutive routine glucose measurements, standard practice at our

institution indicated that therapy should be started. To minimize bias,

responsibility for the initiation or adjustment of insulin remained with the treating

physician or consulting internal medicine physician. All patients were instructed

not to consume snacks in between the regular meals provided by the hospital.

In both groups the CGM was calibrated according to manufacturersÊ instructions

using venous blood samples every 6 hours. The glucose values for calibration were

determined using the Accu-Check Inform® (Roche Diagnostics, Basel Switserland).

In the SAP group, additional calibrations were performed if there was a false alarm

(Appendix 1). Routine venous blood samples for glucose determination were taken

before PCI and at 24 and 48 hours after admission. Blood was collected in heparine

gel tubes and centrifuged for 10 min at 1600 x g and 18À C for immediate glucose

determination.

Outcome measures and statistical analyses

The main outcome measure was the proportion of time spent hyperglycaemia

(>=7.8 mmol/l) as measured by continuous subcutaneous glucose monitor (CGM)

during 48 hours. Also we calculated the proportion of time spent in hypoglycaemia

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(<=3.9 mmol/l), spent within the initial alarm target range (4.7-6.1 mmol/l) and the

Area Under the Curve (AUC) for hypo- and hyperglycaemia.

Differences in plasma glucose values during admittance were assessed.

Furthermore we calculated CGM accuracy with the mean absolute difference

(MAD). The standardised venous recalibration samples taken every 6 hours were

compared with the last sensor value before recalibration (MAD = |CGM value -

blood glucose| / blood glucose). Thus, we did not use the additional calibration

values performed in the SAP group in case of a false alarm. Finally, the number of

alarms to which the nursing staff had to respond in the SAP group was assessed as

well as the number of hypoglycaemic episodes (plasma glucose <=3.9 mmol/l) that

were followed by oral or intravenous glucose administration.

A formal power analysis was not possible, because this is the first study applying

SAP in the CCU. Therefore, we aimed to include 20 patients to be randomised in a

1:1 ratio. Results were presented as mean � SD or median with IQR were

appropriate. For the between group differences from the sensor derived endpoints,

we used the Mann-Whitney-U test or the student T test. The between group

difference for the plasma glucose values were tested using an ANOVA model for

repeated measurements. All analyses were performed in SPSS 16.0 (SPSS Inc.,

Chicago, IL, USA).

RESULTS

After screening 45 patients we randomised 20 patients to either 48 hours of SAP

therapy or standard care. In both the SAP group and Control group one patient

dropped-out because of transferral to another hospital. In the Control group, one

patient withdrew informed consent. Thus 9 and 8 patients were eligible for

analyses in the SAP- and Control group, respectively. All patients resumed eating

regular meals after admission. The baseline characteristics are provided in Table 1.

The median amount of IV insulin given as a bolus before PCI in the SAP group was

3.6 IU (IQR 3.3-4.8). Hereafter, 102.9 IU (IQR 67.6 to 147.3) were given

subcutaneously with the insulin pump, of which 82% (IQR 69-92) was basal insulin

In the Control group no insulin therapy was initiated by the treating physician.

The median treatment time the SAP group was 47.0 hours (IQR 46.2 to 48.0) and in

the Control group 44.6 (IQR 22.0 to 48.6), p=0.67. Figure 2 shows the median

glucose values for both groups as measured every 5 minutes by the CGM system (2

hours after initiation).

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0 10 20 30 40 50 60

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Time (hours after randomisation)

SAP groupControl groupUpper Line 7.7 mmolLower line 3.9 mmol/l

48 hours

SAP group (n=9)

Control group (n=8)

Age, years (mean ± SD) 58 ± 8 62 ± 7 Male gender, n (%) 8 (89) 5 (63) Diabetes mellitus, n (%) Insulin treatment on admission, n (%)

4 (44) 1 (11)

2 (25) 0 (0)

Smoking, n (%) 5 (56) 5 (63) Hypertension, n (%) 3 (33) 2 (25) Hypercholesterolemia, n (%) 3 (33) 1 (13) Previous MI, n (%) 3 (33) 1 (13) Family history of CAD, n (%) 5 (56) 1 (13) Culprit vessel -LAD, n (%) 3 (33) 3 (38) -RCA, n (%) 5 (56) 4 (50) -LCX, n (%) 1 (11) 1 (13) HbA1c (median, IQR) 6.5% (6.0-7.5) 5.7% (5.5-6.5)

Table 1- Cohort characteristics, SAP=Sensor Augmented Pump, MI= Myocardial Infarction, CAD= Coronary Artery Disease, LAD= Left Anterior Descending, RCA= Right Coronary Artery, LCX= Left Circumflex

Figure 2- median sensor glucose values every 5 minutes after start measurements sensor for both groups. SAP= Sensor Augmented Pump

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The percentage of time spent in hyperglycaemia during the 48 hours after PCI was

significantly smaller in the SAP group: 14.6% (IQR 10.5 to 18.5) vs. 36.3% (IQR

26.0 to 80.4), p<0.01. The proportion of time spent in hypoglycaemia was

significantly greater in the SAP group (8.9%, IQR 8.3 to 12.5) as compared to the

Control group (0%, IQR 0 to 1.5), p<0.001 and the percentage of time spent in the

initial alarm target range of 4.7 to 6.1 mmol/l was 32.7% (IQR 28.4 to 36.0) in the

SAP group and 2.2% (IQR 0.2 to 27.7) in the control group, p=0.036. The AUC for

hyperglycaemia was significantly smaller in the SAP group (0.1 mmol/l/min, IQR

0.0 to 0.1) as compared to the Control group (0.2 mmol/l/min, IQR 0.1 to 0.4),

p=0.015. When comparing the AUC for hypoglycaemia this was 0.02 mmol/l/min

(IQR 0 to 0.02) in the SAP group and 0 mmol/l/min (IQR 0 to 0) in the Control

group, p<0.01.

The median plasma glucose in the SAP group decreased from 10.3 mmol/l (IQR 9.0

to 13.1) pre-PCI to 4.8 mmol/l (IQR 4.0 to 7.4) after 24 hours and 4.6 mmol/l (IQR

4.2 to 7.1) after 48 hours (Figure 3). In the Control group, the median glucose pre-

PCI was 9.1 mmol/l (IQR 8.6 to 11.4), which decreased to 7.7 mmol/l (IQR 6.3 to

10.5) after 24 hours and 6.9 mmol/l (IQR 6.1 to 11.5) after 48 hours. The plasma

glucose in the SAP group decreased significantly more as compared to the Control

group, p=0.025.

Figure 3- median plasma glucose values for both groups. The plasma glucose decreased significantly more in the SAP group, P=0.025. PCI= Primary percutaneous Coronary Intervention SAP= Sensor Augmented Pump

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In total we measured 7620 valid sensor values during the study. In 6% of the

monitoring time the sensor did not generate a signal. On four occasions a sensor

had to be replaced because of sensor failure. The mean number of calibrations per

24 hours was 9 (�4) in the SAP group and 4 (�1) in the Control group and the

median number of paired regular calibration values (every 6 hours) per patient was

5 (IQR 4-6.5). The accuracy of the CGM based on the regular calibration values did

not differ significantly between both groups with an MAD of 21.6% (�9.6) in the

SAP group and 18.5% (�8.8) in the control group (p=0.50).

In the SAP group, the nursing staff responded 11 times (�6) per 24 hours to an

alarm, with an equal distribution between the hypo- and hyperglycaemic alarms. In

the majority of the cases, the patient needed to indicate that the alarm was going

off because the acoustic signal was too weak to be heard by the nurse on the

coronary care unit. The basal rate was adjusted 9 times (�5, mean � SD) per 24

hours. The BolusWizard® advice was followed 75% of the time. In 25% of the time

the advised amount of insulin was lowered by the study staff. In the SAP group,

hypoglycaemia (plasma glucose <=3.9 mmol/l) followed by glucose administration

occurred 11 times in 6 patients. Of these, 3 patients had known diabetes. All events

were without clinical sequelae and occurred at least 23 hours after admission with

a median temporary distribution of 31.4 hours (IQR 26.7-36.6) after admission. In

the Control group, no hypoglycaemia was observed. No other device related adverse

events occurred.

DISCUSSION

In this randomised controlled trial we showed that sensor augmented insulin pump

therapy in patients with STEMI and admission hyperglycaemia lowered the

proportion of time spent in hyperglycaemia. This was accompanied by a significant

increase in the percentage of time in normoglycaemia, but also an increase in

hypoglycaemia and workload.

The (CREATE)-ECLA, Pol-GIK and GIK trials all aimed to deliver high

concentrations of insulin in glucose-insulin-potassium solution to patients with

AMI, without lowering the mean glucose levels.19-21 All of these trials yielded

negative results. In patients with diabetes mellitus, the first DIGAMI trial

successfully lowered the glucose during admittance and thereafter, thereby

improving survival.6 Targeting the mean glucose in the CCU seems more important

than the administration of insulin, as also indicated by Kosiborod and colleagues.9

In contrast with the DIGAMI 2 and HI-5 trial, we were able to lower median

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glucose values during CCU admittance after STEMI.7;8 The reduction of the

proportion of time spent in hyperglycaemia that we found was also shown in the

trials investigating SAP therapy in diabetes mellitus type 1 patients.14-16;22 An

increase in hypoglycaemia is not a consistent finding in these trials; however they

all concern intensively insulin treated type 1 patients, with hypoglycaemia

occurring in both arms of the studies. In our study, only the SAP group received

strict glucose control with ensuing hypoglycaemia.

The use of a continuous glucose sensor in a CCU setting was investigated before in

an exploratory study by Rowen and colleagues.23 They concluded that the use of

CGM was helpful to the nursing personnel, without an increase in workload. This is

in contrast with our findings, as most of the personnel indicated that the workload

substantially increased due to SAP treatment. Besides paying attention to CGM

alarms and checking the plasma glucose, the nursing staff had to follow the

treatment algorithm and perform calibrations at set times. The number of alarms

to which the nursing staff had to respond was experienced as high (11 times (�6)

per 24 hours).To decrease the workload, we changed the alarm thresholds for the

hyperglycaemic alarm during the trial. One important explanation for the false

positive or negative alarms is the inaccuracy of the interstitial glucose

measurements as compared to the plasma glucose. In our trial we found the MAD

to be 18.5 to 21.5%. It should be noted that we used venous plasma samples to

determine the glucose calibration values, whereas the usual outpatient practice is

to calibrate against capillary blood. However, our results are in line with previous

studies investigating the CGM in the ICU.24;25 The somewhat higher calibration

rate in the SAP group did not lead to a different accuracy of CGM measurements in

both groups, which may be due to the fact that sensor accuracy is more influenced

by the timing rather than the frequency of the calibration.26 We only calibrated the

sensor when glucose values were stable and the patient was not eating.

On many occasions, the patient had to alert the nurse that an alarm was signalling.

It is therefore likely that not all alarms were addressed immediately and this could

have contributed to the hypoglycaemia developed in 6 patients. The observation

that the acoustic signal of the SAP is not loud enough in the hospital setting has

also been made by Logtenberg et al.24 We have however used a device designed for

treatment in the outpatient setting and necessary adjustments for the in-hospital

setting were to be expected.

Due to logistic reasons we were not always able to complete the 48 hours of

treatment time. Figure 3 clearly shows that the median sensor value lines meet

before completion of the preset 48 hours of intervention. Although this is a

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limitation of the trial, plasma glucose in SAP was lower as compared to the Control

group up to 48 hours.

Because this was a pilot trial, it was limited by its relative small sample size.

Therefore, despite appropriate randomisation procedures, the percentage of

patients with diabetes mellitus was almost twice as high in the SAP group (44%) as

compared to the Control group (25%), which was also reflected in a higher

admission glucose and HbA1c in the SAP group. However, despite this baseline

difference putting the SAP group at the disadvantage, glucose in the SAP group

was significantly lower as compared to the Control group in the first 48 hours.

Notably, there was also a spontaneous glucose decrease in the Control group from

baseline to 48 hours.

This is the first randomised controlled pilot trial showing that in hyperglycaemic

STEMI patients, SAP clearly significantly reduces the duration of hyperglycaemia

as well as plasma glucose levels, when compared with hyperglycaemic control

patients. This is however accompanied by a small but significant increase of

hypoglycaemia and considerable workload for the nursing staff. Accuracy and

alarm functions need improvement in this setting. Although a promising tool for in-

hospital hyperglycaemia therapy SAP needs improvement, especially with regard

to sensor accuracy and the alarm function of the device, before continuing to large

scale randomised controlled trials.

ACKNOWLEDGEMENTS

We thank Medtronic International Trading Sàrl for providing the sensors for this

investigation free of charge.

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REFERENCES

(1) Capes SE, Hunt D, Malmberg K, Gerstein HC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet 2000; 355(9206):773-778.

(2) Hsu CW, Chen HH, Sheu WHH, Chu SJ, Shen YS, Wu CP et al. Initial Serum Glucose Level as a Prognostic Factor in the First Acute Myocardial Infarction. Annals of Emergency Medicine 2007; 49(5):618-626.


(4) Kosiborod M, Rathore SS, Inzucchi SE, Masoudi FA, Wang Y, Havranek EP et al. Admission Glucose and Mortality in Elderly Patients Hospitalized With Acute Myocardial Infarction: Implications for Patients With and Without Recognized Diabetes. Circulation 2005; 111(23):3078-3086.

(5) Malmberg K, Ryden L, Efendic S, Herlitz J, Nicol P, Waldenstrom A et al. Randomized trial of insulin-glucose infusion followed by subcutaneous insulin treatment in diabetic patients with acute myocardial infarction (DIGAMI study): effects on mortality at 1 year. J Am Coll Cardiol 1995; 26(1):57-65.

(6) Malmberg K. Prospective randomised study of intensive insulin treatment on long term survival after acute myocardial infarction in patients with diabetes mellitus. DIGAMI (Diabetes Mellitus, Insulin Glucose Infusion in Acute Myocardial Infarction) Study Group. BMJ 1997; 314(7093):1512-1515.

(7) Cheung NW, Wong VW, McLean M. The Hyperglycemia: Intensive Insulin Infusion In Infarction (HI-5) Study: A randomized controlled trial of insulin infusion therapy for myocardial infarction. Diabetes Care 2006; 29(4):765-770.

(8) Malmberg K, Ryden L, Wedel H, Birkeland K, Bootsma A, Dickstein K et al. Intense metabolic control by means of insulin in patients with diabetes mellitus and acute myocardial infarction (DIGAMI 2): effects on mortality and morbidity. Eur Heart J 2005; 26(7):650-661.

(9) Kosiborod M, Inzucchi SE, Krumholz HM, Masoudi FA, Goyal A, Xiao L et al. Glucose Normalization and Outcomes in Patients With Acute Myocardial Infarction. Arch Intern Med 2009; 169(5):438-446.

(10) Kosiborod M, Inzucchi SE, Goyal A, Krumholz HM, Masoudi FA, Xiao L et al. Relationship Between Spontaneous and Iatrogenic Hypoglycemia and Mortality in Patients Hospitalized With Acute Myocardial Infarction. JAMA 2009; 301(15):1556-1564.

(11) Mellbin LG, Malmberg K, Waldenstrom A, Wedel H, Ryden L, for the DIGAMI. Prognostic implications of hypoglycaemic episodes during hospitalisation for myocardial infarction in patients with type 2 diabetes: a report from the DIGAMI 2 trial. Heart 2009; 95(9):721-727.

(12) Vriesendorp TM, Roos YB, Kruyt ND, Biessels GJ, Kappelle LJ, Vermeulen M et al. Efficacy and safety of two 5 day insulin dosing regimens to achieve strict glycaemic control in patients with acute ischaemic stroke. J Neurol Neurosurg Psychiatry 2009; 80(9):1040-1043.


(14) Hermanides J., NŒrgaard K., Bruttomesso D., Mathieu C., Frid A., Dayan C.M. et al. Sensor augmented pump therapy substantially lowers HbA1c. Diabetologia 52[Supplement I], S43. 2009.

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(17) Bode BW, Braithwaite SS, Steed RD, Davidson PC. Intravenous insulin infusion therapy: indications, methods, and transition to subcutaneous insulin therapy. Endocr Pract 2004; 10 Suppl 2:71-80.

(18) Nobels F, Van Pottelbergh I, Van Crombrugge P. Bloedglucosecontrole tijdens opname in het ziekenhuis. Essentials of insulin therapy in diabetes mellitus 2006; 6:4-18.

(19) Ceremuzynski L, Budaj A, Czepiel A, Burzykowski T, Achremczyk P, Smielak-Korombel W et al. Low-dose glucose-insulin-potassium is ineffective in acute myocardial infarction: results of a randomized multicenter Pol-GIK trial. Cardiovasc Drugs Ther 1999; 13(3):191-200.


(21) van der Horst I, Zijlstra F, van 't Hof AW, Doggen CJ, de Boer MJ, Suryapranata H et al. Glucose-insulin-potassium infusion inpatients treated with primary angioplasty for acute myocardial infarction: the glucose-insulin-potassium study: a randomized trial. J Am Coll Cardiol 2003; 42(5):784-791.

(22) Hirsch A, Windhausen F, Tijssen JG, Verheugt FW, Cornel JH, de Winter RJ. Long-term outcome after an early invasive versus selective invasive treatment strategy in patients with non-ST-elevation acute coronary syndrome and elevated cardiac troponin T (the ICTUS trial): a follow-up study. The Lancet 369(9564):827-835.

(23) Rowen M, Schneider DJ, Pratley RE, Sobel BE. On rendering continuous glucose monitoring ready for prime time in the cardiac care unit. Coron Artery Dis 2007; 18(5):405-409.

(24) Logtenberg SJ, Kleefstra N, Snellen FT, Groenier KH, Slingerland RJ, Nierich AP et al. Pre- and postoperative accuracy and safety of a real-time continuous glucose monitoring system in cardiac surgical patients: a randomized pilot study. Diabetes Technol Ther 2009; 11(1):31-37.

(25) Piper HG, Alexander JL, Shukla A, Pigula F, Costello JM, Laussen PC et al. Real-Time Continuous Glucose Monitoring in Pediatric Patients During and After Cardiac Surgery. Pediatrics 2006; 118(3):1176-1184.

(26) King C, Anderson SM, Breton M, Clarke WL, Kovatchev BP. Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp. J Diabetes Sci Technol 2007; 1(3):317-322.

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Check the plasm

a blood glucose

1.Snooze the alarm

2. U

se the plasma

glucose value to adjust the basal insulin rate 3.C

alibrate the device*

*Only perform

calibrations when the patient is not eating and w

hen the sensor indicates stable glucose values. B

G= blood glucose

BG

<4.7 B

G 4.7-6.1

Alarm

High

BG

>6.1

1.Snooze the alarm

2. U

se the plasma

glucose value to adjust the basal insulin rate

1.Snooze the alarm

2. U

se the plasma



Check the plasm

a blood glucose

1.Snooze the alarm

2. U

se the plasma



BG

<4.7B

G 4.7-6.1

Alarm

Low

BG

>6.1

1.Snooze the alarm

2. U

se the plasma

glucose value to adjust the basal insulin rate

1.Snooze the alarm

2. U

se the plasma



AP

PE

ND

IX 1- In

sulin

algorithm

(Ch

apter 9)

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E/u=number of insulin units per hour

Intravenous starting insulin bolus pre-PCI:

Only for patients randomised to the sensor augmented pump group - Determine the starting rate in K2, -Multiply by 1.5, - Give I.V.

Glycaemia

(mmol/l)

K1 (E/u)

K2 (E/u)

START

K3 (E/u)

K4 (E/u)

K5 (E/u)

K6 (E/u)

K7 (E/u)

K8 (E/u)

K9 (E/u)

K10 (E/u)

> 25 4.4 8.8 13.2 17.6 22.0 26.4 30.8 35.2 39.6 44.0 21.4 - 25 3.6 7.2 10.8 14.4 18.0 21.6 25.2 28.8 32.4 36.0 18.5 - 21.3 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 27.0 30.0 16.1 - 18.4 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 13.9 - 16.0 2.1 4.2 6.3 8.4 10.5 12.6 14.7 16.8 18.9 21.0 12.1 - 13.8 1.7 3.4 5.1 7.2 8.5 10.2 11.9 13.6 15.3 17.0 10.4 - 12.0 1.4 2.8 4.2 5.6 7.0 8.4 9.8 11.2 12.6 14.0 9.1 - 10.3 1.2 2.4 3.6 4.8 6.0 7.2 8.4 9.6 10.8 12.0 8.4 - 9.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 7.8 - 8.3 0.9 1.8 2.7 3.6 4.5 5.4 6.3 7.2 8.1 9.0 7.3 - 7.7 0.8 1.6 2.4 3.2 4.0 4.8 5.6 6.4 7.2 8.0 6.7 - 7.2 0.7 1.4 2.1 2.8 3.5 4.2 4.9 5.6 6.3 7.0 6.2 - 6.6 0.6 1.2 1.8 2.4 3.0 3.6 4.2 4.8 5.4 6.0 5.9 - 6.1 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.6 - 5.8 0.4 0.9 1.3 1.8 2.2 2.7 3.0 3.6 4.0 4.5 5.3 - 5.5 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 5.0 - 5.2 0.3 0.7 1.0 1.4 1.7 2.1 2.4 2.8 3.2 3.5 4.7 - 5.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 4.4 – 4.6 0.2 0.5 0.7 1.0 1.2 1.5 1.7 2.0 2.3 2.5 4.2 - 4.3 0.2 0.4 0.6 0.8 1.0 1.2 1..4 1.6 1.8 2.0 3.9 - 4.1 0.1 0.3 0.4 0.6 0.7 0.9 1.0 1.2 1.3 1.5 3.3 - 3.8 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 < 3.3 0 0 0 0 0 0 0 0 0 0

Advice Glucose range (mmol/L)

Specific General

16.1 - > 25

Check ketones at glucose > 16.7 mmol/L

Consult doctor if E/u=> 24.0

6.2 – 16.0 -

If glycaemic range has been lowered: remain in same column If glycaemic range unaltered: move 1 column to the right

4.7 – 6.1 - If glycaemia remains within this zone:

stay in same column < 3.3. – 4.6

-

Consult doctor if E/u =<0.2 and glucose=<4.1

If glycaemia < 4.7 mmol/L: move 1 column to the left

Glycaemia (mmol/l) Glucose 30% Action 3.3 - 3.8 20 ml 2.8 - 3.3 30 ml < 2.8 40 ml

o Move 1 column to the left o Re-check BG after 15 min o BG 2x < 3.3 mmol/L: consult doctor

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No apparent local effect of insulin on

microdialysis continuous glucose monitoring

measurements

J Hermanides, IME Wentholt, AA Hart, JBL Hoekstra,

JH DeVries

Diabetes Care, 2008 Jun;31(6):1120-2

ABSTRACT

Background Data investigating the possible disturbing influence of insulin in the

vicinity of continuous glucose monitoring (CGM) is lacking. We investigated the

hypothesis that high local insulin concentrations would interfere with sensor

readings.

Methods Two microdialysis sensors were inserted in the periumbilical region of 10

continuous subcutaneous insulin infusion (CSII) treated type 1 diabetes patients. A

test sensor was inserted as close as possible to the insulin catheter and compared

with a control sensor. Glucose peak and nadir were induced. Horizontal and

vertical shifts were assessed using curve fitting and mean absolute difference

(MAD) between paired blood and sensor values were calculated.

Results Curve fitting showed no significant differences between the two sensors.

MAD � SD was 8.50 � 3.47% for the test sensor and 9.21 � 3.17% for the control

sensor, p=0.72.

Conclusions Microdialysis CGM can be accurately performed in the proximity of

CSII systems.

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INTRODUCTION

With ongoing technology development, continuous glucose monitoring (CGM) is

expected to work together with continuous subcutaneous insulin infusion systems

(CSII), evolving towards closed-loop insulin delivery.1 According to manufacturersÊ

instructions, the distance between the insertion points of the pump catheter and

the CGM catheter should be more than 2-3 inches.2 However, no clear evidence

supports this. We therefore investigated the hypothesis that high local insulin

concentrations would interfere with sensor readings.

RESEARCH AND DESIGN METHODS

Patients with at least 6 months of CSII experience were included. Exclusion

criteria were BMI >30 kg/m²; heparin, oral anticoagulant, or corticosteroid use;

pregnancy or breastfeeding; and skin problems prohibiting needle insertion. After

local ethics committee approval, participants gave written informed consent. A

GlucoDay® S (A. Menarini Diagnostics, Firenze, Italy) microdialysis CGM sensor3

was inserted in the periumbilical region, and a control sensor was inserted at least

10 cm away from the test sensor. The insulin catheter was inserted parallel and as

close as possible to the test sensor. The patient returned after overnight fasting.

Blood was sampled in sodium fluoride tubes through an intravenous catheter in the

forearm for immediate glucose determination at the laboratory (HK/G-6PD method,

Roche/Hitachi). Blood glucose levels were increased by withholding the usual rapid-

acting insulin injection, and patients then consumed a standard breakfast, as

described previously.4 Forty minutes after breakfast, an augmented bolus of insulin

(mean � SD 10.7 � 4.5 IU/kg) was administered. During the whole experiment,

basal CSII rates were maintained. Calibration was performed retrospectively, using

two venous glucose values from periods with minimal glucose rate of change, before

breakfast and at the end of the experiment.

Statistical analyses were performed using SPSS version 14.0.0 and package S+ 6.2

was used for curve fitting as previously described.4 It results in a horizontal and

vertical shift for both sensors, indicating sensor delay and drift respectively. For

each subject, blood glucose values were paired with concomitant interpolated

sensor values from the test and the control sensor. The mean absolute difference

(MAD) for each sensor was calculated per subject [|sensor value -blood

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glucose|/blood glucose], averaging the paired data per experiment per sensor.

Comparisons of the MADs were made using the WilcoxonÊs signed-rank test.

RESULTS

Out of fifteen performed experiments, five were unsuccessful because of pressure or

leakage problems. Results of three female and seven male diabetes type 1 subjects

were analysed. Mean age was 50.1 � 7.9 years (mean � SD), BMI 24.2 � 2.6 kg/m²,

diabetes duration 26.8 � 9.9 year and HbA1c 8.4 � 1.4%. The mean insertion

distance between the insulin catheter and the closest insertion point of the

microfiber was 0.9 � 0.2 cm. There was no significant correlation between the

insertion distance and the MAD of the test sensor (r =-0.12). The curves from the

ten successful experiments are shown in Figure 1. Bland-Altman plots are provided

in Figure 2.

After curve fitting, there is evidence that the test sensor values trail those of the

blood glucose by an average -10.8 � 4.0 min (range of –18.2 to -6.0 min, one-sample t test: p<0.001). A minus sign indicates a delay with respect to the blood glucose

curve. For the control series, the average delay was: -5.7 � 8.1 min (–11.8 to 14.8

min, one-sample t test: p=0.053). However, the difference in horizontal shift

between both sensors did not reach statistical significance (p=0.13). No significant

vertical shifts were detected.

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Figure 1- The results of all ten patients are displayed. The continuous line represents the blood glucose values, the dotted line represents the control sensor and the long dashed line represents the test sensor. The tim

e of breakfast is indicated by the arrow.

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In total, 374 blood glucose values were paired with the concomitant values from

both sensors, with 37 � 2 paired values per patient. The average MAD calculated

from the ten MADs was 9.21 � 3.17% for the control sensor and 8.50 � 3.47% for the

test sensor (WilcoxonÊs signed-rank test p=0.72).

Figure 2- Bland-Altman plots for the control sensor (A) and the test sensor (B). The x-axis represents the average of blood and sensor glucose measurements, and the y-axis represents the difference between sensor and paired blood glucose (BG) measurements as proportion of the average BG measurement. The difference between BG and sensor readings is 0 at the horizontal solid line. The long dashed line represents the mean difference (–0.0014 for the control and –0.024 for the test sensor); dotted lines are drawn at the mean difference ± 1.96 times the SD of the differences.

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CONCLUSIONS

We investigated the hypothesis that high local insulin concentrations would

interfere with sensor readings by infusion of insulin in the proximity of a

microdialysis type glucose sensor. No difference in MAD was detected when

comparing the test and control sensors. When applying curve fitting, there was a

significant delay (mean � SD 10.8 � 4.0 min) of the test sensor compared to the

blood glucose values. A non-significant delay was found for the control sensor (5.7 �

8.1 min). This cannot be fully explained by instrumental delay.4;5 One could

hypothesise that insulin induced saturation of the low number of GLUT4s present

at the adipocyte cell surface could explain how glucose can reliably be measured in

the vicinity of relatively high insulin concentrations and possibly delay glucose-

transporting capacity.6 This speculation merits further investigation. However, to

minimize variation, we have used the same devices for the test and control sensors

and bias created due to constant intersensor variability can therefore not be

excluded.7 A preliminary human pilot study also suggested no local influence of

insulin on sensor readings in humans.8

Furthermore, the relatively low number of 23 hypoglycaemic paired readings is a

limitation of this study and the efficacy of the test sensor in the hypoglycaemic

range needs further investigation.

We aimed to position the tip of the injection catheter as close to the microdialysis

catheter as possible. Using ultrasound, we were not able to visualize the microfiber

subcutaneously. Therefore we can not tell the exact distance between the injection

and microdialysis sites, but is seems likely that the actual distance was smaller

than the surface distance of the two insertions.

We conclude that microdialysis CGM can be accurately performed at a mean

distance of 0.9 cm from a CSII system in the normo- and hyperglycaemic ranges

and probably the hypoglycaemic range during rapid rise and fall of blood glucose.

This has important consequences for the use of CGM in type 1 diabetes and the

development of a closed-loop system. Further studies should focus on a possible

additional sensor delay caused by insulin and evaluate sensor accuracy in the

proximity of insulin during hypoglycaemia.

ACKNOWLEDGEMENTS

We acknowledge Nico J. Smits for his expert ultrasound help.

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REFERENCES


(2) Medtronic MiniMed. Important safety information. http://www minimed com/about/safety [accessed 2008 Feb. 12]; Available at: http://www.minimed.com/about/safety.html

(3) Kubiak T, Worle B, Kuhr B, Nied I, Glasner G, Hermanns N et al. Microdialysis-Based 48-Hour Continuous Glucose Monitoring with GlucoDay: Clinical Performance and Patients' Acceptance. Diabetes Technology & Therapeutics 2006; 8(5):570-575.

(4) Wentholt IM, Vollebregt MA, Hart AA, Hoekstra JB, Devries JH. Comparison of a needle-type and a microdialysis continuous glucose monitor in type 1 diabetic patients. Diabetes Care 2005; 28(12):2871-2876.

(5) Wentholt IM, Hart AA, Hoekstra JB, Devries JH. Relationship between interstitial and blood glucose in type 1 diabetes patients: delay and the push-pull phenomenon revisited. Diabetes Technol Ther 2007; 9(2):169-175.

(6) Kozka IJ, Clark AE, Reckless JPD, Cushman SW, Gould GW, Holman GD. The effects of insulin on the level and activity of the GLUT4 present in human adipose cells. Diabetologia 1995; 38(6):661-666.

(7) Boyne MS, Silver DM, Kaplan J, Saudek CD. Timing of changes in interstitial and venous blood glucose measured with a continuous subcutaneous glucose sensor. Diabetes 2003; 52(11):2790-2794.

(8) Regittnig W, Koehler G, Bodenlenz M, Schaller HC, Koehler H, Ellmerer M et al. Coupling of Subcutaneous Insulin Delivery and Subcutaneous Glucose Sensing by Means of a Microperfusion or Microdialysis Probe. 5th Annual Diabetes Technology Meeting , A118. 2005

.

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Postprandial glycaemic excursions with the use

of a closed-loop platform in diabetes type 1, a

pilot study

AC van Bon, J Hermanides, R Koops, JBL Hoekstra, JH

DeVries


�

ABSTRACT

Background To evaluate the efficacy of a proportional derivative algorithm closed-

loop system to control postprandial glucose concentrations in diabetes type 1.

Methods Six patients treated with CSII received a standardised meal on three

days. The first day served as control, the second day as learning experiment for the

algorithm and the third day to compare the closed-loop to the control day. Venous

glucose was measured as reference until 300 minutes postprandially. The artificial

pancreas platform consisted of a subcutaneous continuous glucose monitor (CGM),

the GlucoDay® S (Menarini Diagnostics), two D-Tron+ pumps (Disetronic Medical

Systems) for subcutaneous insulin and glucagon administration connected to a

personal computer.

Results One patient was excluded due to technical failure of the CGM. Two of five

patients were male; mean age was 50.8 years (range 38 to 60) and mean HbA1c

8.7% (range 7.0 to 12.2). The median postprandial venous glucose concentration of

day 1 was 11.4 mmol/l (range 5.2 to 14.7 mmol/l) compared to 8.7 mmol/l (7.1 to 8.8

mmol/l) on day 3 (p=0.14). Percentage of time spent in postprandial euglycaemia on

day 1 was 31% versus 60% on day 3 (p=0.08). Time spent below 3.9 mmol/l was 19%

on day 1 compared to 11% on day 3 (p=1.0). Time above 10 mmol/l on day 1 was

60% versus 29% on day 3 (p=0.22).

Conclusion The artificial pancreas provided comparable postprandial glycaemic

control as usual care.

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INTRODUCTION

Continuous subcutaneous insulin infusion (CSII) combined with continuous

glucose monitoring (CGM) and a glucose control algorithm would result in an

artificial pancreas. Artificial pancreas or closed-loop systems are capable to

achieve automated glucose control in ICU or CCU settings.1;2

Steil3 and Weinzimer4 tested a subcutaneous artificial pancreas based on a

proportional integrated derivative (PID) model in a clinical research unit. Mean

glucose concentrations were not different from usual care; however postprandial

glucose excursions were higher than with usual care. A manually administered

premeal priming bolus could partially overcome this problem. Schaller et al.5

evaluated an algorithm based on model predictive control (MPC) in six patients

with diabetes type 1 during eight hours in the fasting condition. The model

normalised glucose concentrations if the glucose level was above 6 mmol/l and

then maintained normoglycaemia. Hovorka et al.6 evaluated glucose control with

an algorithm based on a MPC model in patients with diabetes type 1 compared to

usual care with an insulin pump in three different settings: overnight glucose

control starting two hours after dinner following a pre-meal with self-determined

manual insulin bolus until the next morning, postprandial glucose control 30

minutes after dinner comparing rapidly absorbed carbohydrates to slowly

absorbed carbohydrates until the next morning and glucose control starting two

hours after 45 minutes of exercise until the next morning. In all these studies

closed-loop glucose control resulted in more time in target range as compared to

conventional care. Recent data showed similar results for an MPC closed loop

artificial pancreas compared to usual care in fourteen patients.7;8

The aim of this study was to test the feasibility of a control algorithm to control

postprandial glucose concentrations after a single meal in patients with diabetes

type 1.

METHODS

Subjects

Six patients with type 1 diabetes treated with CSII for more than six months, aged

18-70 years were recruited for the study. All patients gave written informed

consent. The ethics committee of the Academic Medical Centre at the University of

Amsterdam approved the study.

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Study Procedures

In the afternoon before the study visit, a microdialysis glucose sensor (GlucoDay® S

Menarini Diagnostics, Firenze, Italy) was inserted. Patients were admitted to the

clinical research unit of the Academic Medical Centre the following morning in

fasting condition. An intravenous catheter was inserted into an antecubital vein for

blood sampling. During the first test day, the patients administered a self-

determined insulin bolus before a standardised meal of 40 gram carbohydrates.

During the second and third test day, patients wore two D-Tron+ pumps

(Disetronic Medical Systems, St. Paul, MN) for subcutaneous insulin and glucagon

administration, respectively. The CGM sensor, insulin pump and the glucagon

pump were connected to a personal computer containing the algorithm. The test

started after calibration of the CGM. No premeal insulin bolus was administered

when the standardised meal was served. The sensor glucose values were read out

every ten seconds. Every five minutes an average glucose level was calculated. The

second day was a so-called learning day of the algorithm to determine an individual

insulin sensitivity factor. This factor was initially calculated on the basis of total

daily insulin need and adjusted as needed during the day 2 experiment. During all

three test days, venous glucose was measured at baseline and every 30 minutes

until five hours postprandially.

Calibration procedure

The calibration procedure was performed before starting the artificial pancreas and

repeated in case of a difference between sensor glucose and self-monitored glucose

level above 1.5 mmol/l. The calibration procedure consisted of three measurements

of the sensor glucose and the corresponding concomitant self-monitored glucose

values with an interval of ten minutes. The average difference of the three

measurements between the sensor glucose level and the self-monitored glucose was

calculated. This average was the correction factor for the sensor glucose.

Algorithm

The control algorithm, employed in this study was designed by Inreda BV (Goor,

the Netherlands) and is patented (NL C 1032756; WO 2007/049961 A3). The

algorithm can be characterised as a self learning individualised proportional

derivative controller. Insulin delivery is determined by the difference between

current and target glucose and the rate of change of glucose levels. Furthermore,

insulin delivery is adjusted for individual insulin sensitivity. Target blood glucose

values can be programmed for a lower and an upper limit. The upper limit was set

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at 7 mmol/l and the lower limit at 5 mmol/l. Every five minutes, the glucose levels

are compared to the target range. There are three operating ranges. First, if glucose

values are below the lower limit, a sound signal is generated. The patient can

correct the low glucose value by eating. In case of a glucose fall below a next limit,

glucagon is injected according to a formula taking into account the rate of glucose

fall. During the experiment, this was set at 3.2 mmol/l. If glucose levels are

between the lower and upper limit, no insulin is administered by the algorithm. If

glucose levels are above the upper limit, insulin is injected according to a formula

taking into account the rate of glucose rise. Every seven minutes, insulin is

administered if the glucose levels are above the limit and not falling. If glucose

concentration is above 20 mmol/l, the maximum insulin administration rate is

reached i.e. 10 units per seven minutes.

Statistics

All postprandial venous glucose concentrations on the first day and on the third day

were averaged per patient and were compared as paired measurements with the

Wilcoxon Signed Ranks test. Median venous glucose concentrations are given with

minimum and maximum value. Sensor glucose concentrations were calculated

every three minutes. The median sensor glucose concentration is expressed as area

under the curve (AUC). Demographic features are given as mean with range of

minimum and maximum. Time spent in euglycaemia was defined as the percentage

of time the glucose concentrations were between 3.9 mmol/l and 10.0 mmol/l.

RESULTS

Due to failure of the microdialysis filament on day 3, one male patient was

excluded. Two of five patients were male; mean age was 50.8 years (range 38 to 60)

and mean HbA1c 8.7% (range 7.0 to 12.2). The mean diabetes duration was 30.3

years (range 14 to 45) and the mean CSII duration was 6.7 years (range 2 to 14,

Table 1).

Table 1 Baseline characteristics (n=5)

Male / Female 2 / 3 Age, years (mean, range) 50.8 (38 – 60) HbA1c, % (mean, range) 8.7 (7 – 12.2) Diabetes duration, years (mean, range) 30.3 (14 – 45) CSII duration, years (mean, range) 6.7 (2 – 14)

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The median venous glucose concentration on day 1 was 11.4 mmol/l (range 5.2 to

14.7 mmol/l) compared to a median venous glucose concentration in day 3 of 8.7

mmol/l (range 7.1 to 8.8 mmol/l), p=0.14 (see Figure 1 and Table 2). For those with

HbA1c >7%, n=4, the median glucose concentration on day 1 was 12.6 mmol/l

(range 9.2 to 14.7 mmol/l) compared to 8.7 mmol/l (range 8.5 to 8.8 mmol/l) on day

3, p=0.07. The AUC of the sensor glucose concentration of day 1 (2993 mmol/l x

minutes, range 1900 to 4581) did not differ from day 3 (2746 mmol/l x minutes

range 2426 to 3330), p=0.5. The venous peak and nadir glucose concentration on

day 1 and day 3 were not different (Table 2). Figure 1-median venous glucose concentrations day 1 compared to day 3

Table 2-postprandial glucose values day 1 compared to day 3, n=5 Plasma glucose, mmol/l (median, range) Day 1 Day 3 p-value

Overall 11.4 (5.2 to 14.7) 8.7 (7.1 to 8.8) 0.14 t=120 min 12.3 (4.2 to 17.4) 8.1 (6.8 to 9.6) 0.23 t=180 min 13.4 (3.3 to 15.6) 4.5 (3.3 to 9.0) 0.08 Peak 14.1 (7.9 to 17.7) 14.5 (10.8 to 17.7) 0.5 Nadir 10.4 (3.0 to 10.1) 3.8 (3.2 to 4.4) 0.08

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On day three, the patients tended to have a higher percentage of time spent in

euglycaemia measured by venous glucose measurements compared to day 1 (31% vs

60%, p=0.08). No significant differences were seen in time spent in hypoglycaemia

or hyperglycaemia between day 1 and day 3.

On day 3, three hypoglycaemic episodes occurred in two patients lasting 6, 9 and 30

minutes. During the period of 30 minutes, the corresponding venous blood glucose

level was 3.2 mmol/l. No venous measurements were taken during the other two

periods. In addition, venous glucose measurements detected five other episodes of

glucose levels below 3.9 mmol/l, in three patients, with values of 3.2, 3.6 (twice) and

3.8 mmol/l (twice). All hypoglycaemic periods occurred late postprandially, after

150 minutes or more.

On day 3, the algorithm was enabled to give alarms. The number of sound alarms

and number of the glucagon responses are shown in table 3. In total the sound

alarm went off 14 times, range 1-4 per patient. Glucagon boluses were given in two

patients. No nausea was noted.

The cumulative mealtime related insulin requirements on day 1 and day 3 are

shown in table 3. No significant differences were seen, p=0.14.

Table 3a-insulin need during the study, given as international units Day 1 Day 3 Patient Bolus Basal Total Total 1 14 6.1 20.1 41 2 4 3 7 26.5 3 4 5 9 9 4 4.5 5.8 10.3 40.5 6 5 6.1 11.1 9

Table 3b- number of sound alarms (glucose<5 mmol/l) and the number of glucagon responses (glucose<3.2 mmol/l) Day 3 Hypoglycaemic alarm Glucagon need (IU)

Patient 1 3 0 Patient 2 4 10 Patient 3 1 0 Patient 4 4 0 Patient 6 2 1

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�

DISCUSSION

In this small pilot study, the feasibility of a closed-loop artificial pancreas based on

a PID algorithm was tested. Postprandial venous glucose control was comparable to

usual care, with a tendency to a higher percentage of time spent in euglycaemia.

We donÊt want to overemphasise this, as in some of our patients with relatively

high HbA1cÊs control could have been optimised using conventional methods, but for

a first pilot trial we chose to take these real-life patients as a starting point.

Preliminary results of closed-loop artificial pancreas experiments based on Model

Predictive Control (MPC)7;8 also showed comparable control during 22 hours closed-

loop versus usual care. Postprandial hyperglycaemia did not significantly differ

between closed-loop and usual care, but the maximum glucose levels were above

the postprandial targets advised by the American Diabetes Association (ADA).

Weinzimer et al.9 also reported high postprandial levels that could partially

overcome by a manual premeal insulin bolus. In our pilot study the postprandial

glucose values achieved by the AP were almost within the postprandial range of the

ADA (<10 mmol/l). The median venous glucose on day 1 time point 120 minutes

postprandial was 12.3 mmol/l (range 4.2 to 17.4 mmol/l) versus median glucose 8.1

mmol/l on day 3 (range 6.8 to 9.6 mmol/l), p=0.23. At time point 180 minutes

postprandial, the median venous glucose concentrations was 13.4 mmol/l on day 1

(range 3.3 to 15.6 mmol/l) and 4.5 mmol/l on day 3 (range 3.3 to 9 mmol/l), p=0.08

(Table 2).

After the learning day, the individual sensitivity was adjusted for the following test

day. Probably, the individually adjusted insulin sensitivity factor is an explanation

for the tendency towards lower glucoses on day three compared to day one. The

research groups of Padova, Montpellier and Virginia7;8 also used an individual

factor. This factor named ÂaggressivenessÊ or ÂqÊ was dependent of clinical

parameters of the patient. The research groups did not have a learning day, so q

was not adjusted during the experiment.

To prevent hypoglycaemias, the hypoglycaemic alarm was set at 5 mmol/l. If the

glucose level was below 5 mmol/l a hypoglycaemic alarm went off and patients had

to eat 25 gram carbohydrates. All patients had to eat carbohydrates, but the alarm

was not followed by oral carbohydrates if the alarm went off within 5 until 10

minutes after eating.

A hypoglycaemia was defined as a glucose concentration below 3.9 mmol/l. On day

3, five episodes of hypoglycaemia were measured by venous glucose concentrations

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in three patients, one patient encountering a single value of 3.8 mmol/l only, of

arguable significance. All events occurred late postprandial, between 150 and 300

minutes. The lowest measured venous blood glucose concentration was 3.2 mmol/l.

This occurred in two patients and glucagon injections were administered. In one of

these two patients the amount of insulin administered on day 3 was substantially

larger than on day 1. So, in these two patients the individual sensitivity factor was

set too aggressive. One other patient received four times more insulin than usual.

Taking her HbA1c of 10% into account, her daily insulin doses was probably too

low.

Glucagon boluses were given in two patients in order to prevent severe

hypoglycaemia. The efficacy of low dose glucagon as a rescue to prevent severe

hypoglycaemia remains to be shown in subsequent experiments. Damiano et al 10

also used subcutaneous glucagon injections in their closed-loop glucose control, but

glucagon was used to control the glucose concentration within the normal range

with frequent injection of small doses, while it was used as rescue compound in our

system.

In the near future, the algorithm will undergo revisions, mainly at the glucose level

of the sound alarm. The slope of the glucose drop has to be taken in account if the

carbohydrate alarm will go off, and the level will be set lower, probably at 4 or 4.5

mmol/l. Also a needle type sensor will replace the microdialysis sensor because of

the technical problem of the microdialysis filament. Furthermore, the examination

time and number of patients will be extended. Hereafter, a wireless connection

between the components would enable outpatient testing.

In conclusion, postprandial glucose control by this algorithm was feasible, and

comparable to non-optimised usual care.

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REFERENCES

(1) Hovorka R, Kremen J, Blaha J, Matias M, Anderlova K, Bosanska L et al. Blood Glucose Control by a Model Predictive Control Algorithm with Variable Sampling Rate Versus a Routine Glucose Management Protocol in Cardiac Surgery Patients: A Randomized Controlled Trial. J Clin Endocrinol Metab 2007; 92(8):2960-2964.

(2) Plank J, Blaha J, Cordingley J, Wilinska ME, Chassin LJ, Morgan C et al. Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. diabetes Care 2006; 29:272-276.

(3) Steil GM, Rebrin K, Darwin C, Hariri F, Saad MF. Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes. Diabetes 2006; 55(12):3344-3350.

(4) Weinzimer SA, Steil GM, Swan KL, Dziura J, Kurtz N, Tamborlane WV. Fully Automated Closed-Loop Insulin Delivery Versus Semiautomated Hybrid Control in Pediatric Patients With Type 1 Diabetes Using an Artificial Pancreas. diabetes Care 2008; 31(5):934-939.

(5) Schaller HC, Schaupp L, Bodenlenz M, Wilinksa ME, Chassin LJ, Wach P et al. On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with diabetes type 1. Diabetic medicine 2006; 23:-90.

(6) Hovorka R, Acerini CL, Allen JM, Chassin LJ, Kollman C, Elleri D et al. Overnight closed-loop insulin delivery in children and adolescents with type 1 diabetes: towards home testing. Diabetes 2009; 58(Supl):A54-206-OR.

(7) Bruttomesso D, Farret A, Costa S, Marescotti MC, Vettore M, Avogaro A et al. Closed –loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: preliminary studies in Padova and Montpellier. Journal of Diabetes Science and Technology 2009; 3:1014-1021.

(8) Clarke WL, Anderson S, Breton M, Patek S, Kashmar L, Kovatchev B. Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience. Journal of Diabetes Science and Technology 2009; 3:1031-1038.

(9) Weinzimer SA, Steil GM, Swan KL, Dziura J, Kurtz N, Tamborlane WV. Fully Automated Closed-Loop Insulin Delivery Versus Semiautomated Hybrid Control in Pediatric Patients With Type 1 Diabetes Using an Artificial Pancreas. diabetes Care 2008; 31(5):934-939.

(10) Model predictive closed loop control with insulin and glucagon. EASD / JDRF symposium EASD 2009 S13; 2009.

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Glucose variability is associated with ICU

mortality

J Hermanides, TM Vriesendorp, RJ Bosman,

DF Zandstra, JBL Hoekstra, JH DeVries

Critical Care Medicine, 2010 Mar;38(3):838-42

ABSTRACT

Objective Mounting evidence suggests a role for glucose variability in predicting

intensive care unit (ICU) mortality. We investigated the association between

glucose variability and intensive care unit and in-hospital deaths across several

ranges of mean glucose.

Design Retrospective cohort study.

Setting An 18-bed medical/surgical ICU in a teaching hospital.

Patients All patients admitted to the ICU from January 2004 through December

2007.

Measurements and Main Results Two measures of variability, mean absolute

glucose change per hour and SD, were calculated as measures of glucose variability

from 5728 patients and were related to ICU and in-hospital death using logistic

regression analysis. Mortality rates and adjusted odds ratios for ICU death per

mean absolute glucose change per hour quartile across quartiles of mean glucose

were calculated. Patients were treated with a computerized insulin algorithm

(target glucose 4.0 to 7.0 mmol/l). Mean age was 65 � 13 years, 34% were female,

and 6.3% of patients died in the ICU. The odds ratios for ICU death were higher for

quartiles of mean absolute glucose change per hour compared with quartiles of

mean glucose or SD. The highest odds ratio for ICU death was found in patients

with the highest mean absolute glucose change per hour in the upper glucose

quartile: odds ratio 12.4 (95% confidence interval, 3.2 to 47.9; p<0.001). Mortality

rates were lowest in the lowest mean absolute glucose change per hour quartiles.

Conclusions High glucose variability is firmly associated with ICU and in-hospital

death. High glucose variability combined with high mean glucose values is

associated with highest ICU mortality. In patients treated with strict glycaemic

control, low glucose variability seemed protective, even when mean glucose levels

remained elevated.

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Time (hours)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Glu

cose

(mm

ol/l)

4

5

6

7

8

9

10

11

Patient 1, mean glucose 7.5 mmol/l, SD = 2.5, MAG = 0.25 mmol/l/hr

INTRODUCTION

The results of the Leuven studies have led to a worldwide increase in the

implementation of strict glycaemic control in the intensive care unit (ICU).1;2 The

mortality reduction in these landmark trials was attributed to the strict lowering of

mean glucose (target 4.4-6.1 mmol/l) during admission in the intervention group.3

Interestingly, the positive results of the Leuven studies have not been reproduced

in later studies4-8 It is therefore of importance to examine whether factors other

than mean glucose are of influence. As mean glucose is lowered and glycaemic

excursions are targeted, glucose variability (GV) is likely to be reduced as well.

Several studies have shown that GV is strongly associated with short-term ICU

mortality.9;10 This can be understood from a pathophysiological viewpoint, because

hyperglycaemia, as well as GV, can contribute to ICU mortality by increasing

oxidative stress, neuronal damage, mitochondrial damage and coagulation

activation.11-15 In a study by Krinsley in an ICU population, the SD as marker of

GV was a predictor of mortality within different ranges of mean glucose and a

stronger predictor than mean glucose itself.16 However, the SD is not the most

appropriate method for defining GV of repeated glucose measurements.17 Two

patients with the same mean glucose and SD can express completely different

patterns of variability (Figure 1). Furthermore, the majority of the results gathered

so far on the predictive value of GV come from populations that are not treated or

are partly treated with strict glycaemic control.9;10;16

The aims of this study were to measure GV over time in a large strict glucose

control-treated ICU population across several ranges of mean glucose, and to

investigate the association of GV and mean glucose values with ICU- and in-

hospital mortality.

Figure 1- two fictive patients with identical mean glucose and SD, but different patterns of variability, expressed by the mean absolute glucose change (MAG)

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Setting

We performed a retrospective cohort study in an 18-bed medical/surgical ICU in a

teaching hospital (Onze Lieve Vrouwe Gasthuis Hospital, Amsterdam, The

Netherlands). On average, a nurse takes care of two patients, depending on the

severity of disease. All beds were equipped with a clinical information system (iMD-

Soft; MetaVision, Tel Aviv, Israel) and all clinical and laboratory data was stored

here. The glucose regulation protocol was implemented in April 2001 and set at a

target glucose range of 4.0 to 7.0 mmol/l. Using a sliding scale computerised

algorithm, the nursing staff was instructed to adjust the insulin infusion rate

depending on the current glucose value and the rate of glucose change (based on

the previous five measurements). The software also provided the time the next

glucose measurement was due, which could vary from 15 minutes up to 4 hours.

Routinely, enteral feeding was started within 24 hours after admission, aiming at

2000 ml within 48 hour or 1500 ml within 24 hours. A duodenal feeding tube was

inserted in case of persistent gastric retention. When patients resumed normal

eating, the tight glucose algorithm was deactivated. The successful implementation

of the algorithm has been reported previously.18 An integrated decision support

module controlled the algorithm and was connected to the clinical information

system. Because a retrospective analysis of anonimysed data was performed,

informed consent was not required according to Dutch Ethical Review Board

regulations.

Cohort and Data collection

We extracted data from the clinical information system concerning patients

admitted between January 2004 and December 2007. Readmissions, patients with

a withholding care policy and patients with <3 glucose values measured during

admission were excluded. We assessed demographic variables, medical history and

mortality in the ICU and in-hospital mortality. Furthermore we collected

information on severity of disease, the occurrence of severe hypoglycaemia (glucose

<=ª2.5 mmol/l) and diabetes mellitus, because these variables are associated with

GV.16;19 As measures for severity of disease we evaluated both the maximal

Sequential Organ Failure Assessment (SOFA) score during admission and the

Acute Physiology and Chronic Health Evaluation II (APACHE II) score on

admission.20;21

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Measures of glucose

Glucose was measured from blood samples obtained from an arterial catheter using

the Accu-check (Roche/Hitachi®, Basel, Switzerland), a handheld glucose

measurement device. Results were automatically stored in the clinical information

system. We collected all glucose values measured for every patient and calculated

the mean glucose during admission and the SD. To obtain a measure of GV that

was less dependent on the mean glucose and took into account all variability over

time, we calculated the mean absolute glucose (MAG) change per patient per hour

(formula 1). This is done by taking the sum of all absolute glucose changes during

admission and dividing this by the total time spent in the ICU in hours.

MAG = |�BG|/�time


Results are presented as mean � SD or median with interquartile range (IQR),

depending on the distribution of the data. Mean glucose, the glucose SD and MAG

change were divided into quartiles. The area under the receiver operating

characteristic curve of the MAG change and the SD were calculated and any

difference in predictive value was examined using parametrical bootstrapping with

1000 replicates. To select the best measure for disease severity we compared the

relations of the APACHE II and maximal SOFA score with ICU mortality in

univariate regression analysis. Using multivariate logistic regression, we

calculated odds ratios (ORs) for ICU- and in-hospital death for each MAG quartile

and corrected for clinical relevant confounders: sex, age, diabetes mellitus, severity

of disease, severe hypoglycaemia and mean glucose. In addition, length of stay in

the ICU was included as a possible confounder because of its relation to mortality

and likely relation to variability, because variability may change over time and the

precision of variability assessment depends on the observation time. Also

cardiothoracic surgery was included, because mortality in this group is generally

lower compared with other surgical patients. Furthermore, several demographic

and physiological characteristics of this group differed from the general ICU

population, which could be reflected in differences in mean glucose levels and

glucose variability.22 Furthermore, we explored differences in predictive value of

the MAG in surgical and medical patients, using an interaction term in the

multivariate model. Hereafter, we calculated ORÊs for ICU death and in-hospital

death for different ranges of GV, subdivided into quartiles of mean glucose. All

Formula 1- mean Absolute Glucose (MAG) change is the absolute glucose change (�BG) per hour spent in the ICU (�time)

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statistical analyses were performed in SPSS 16.0 (SPSS Inc.,Chicago, IL) and SAS

9.1 (SAS Institute, Cary, NC).

RESULTS

Between January 2004 and December 2007, there were 6725 ICU admissions. We

excluded 656 readmissions, 86 patients with a withholding care policy and 255

patients with <3 glucose value measured during admission, leaving 5728 patients

for our analyses. The characteristics of the studied cohort are displayed in Tables

1A and 1B. In total 154189 glucose values were collected, a median of 12 values per

patient (IQR 6 to 23) and 11 values per day (IQR 7 to 14).

Cardiothoracic surgery (n=3560)

Medical and Other (n=2168)

Total cohort (n=5728)

Age, years (mean ± SD) 67 ± 11 63 ± 17 65 ± 13 Gender, female n (%) 1106 (31.1%) 865 (39.9%) 1971 (34%) APACHE II score on admission (median, IQR) 15 (12 to 17) 20 (15 to 27) 16 (13 to 20) Max SOFA score during admission (median, IQR) 5 (4 to 6) 7 (5 to 10) 6 (5 to 7) ICU stay, days (median (IQR) 0.9 (0.8 to 1.1) 1.9 (0.8 to 4.4) 1.0 (0.8 to 2.0) Diabetes mellitus, n (%) 681 (19.1%) 18 (0.8%) 699 (12.2%) Died ICU, n (%) 27 (0.8%) 334 (15.4%) 361 (6.3%) Died hospital, n (%) 93 (2.6%) 516 (23.8%) 609 (10.6%) Severe hypoglycaemia (� 1 event), n (%) 66 (1.9%) 223 (10.3%) 299 (5.2%)

Surgical (n=4409) Medical (n=1319)

Cardiovascular Sepsis After cardiac arrest

3895 52 10

235 220 287

Gastrointestinal 235 57 Haematological 7 7 Renal 12 26 Metabolic 7 48 Neurological 40 150 Respiratory 151 289

Table 1A- characteristics of the studied cohort; APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment

Table 1B- number of patients per APACHE II admission category (surgical/medical)

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0

5

10

15

20

25

30

35

ICU

mor

talit

y (%

)

Mean glucose quartiles

MAG chan

ge q

uarti

les

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21

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2

3

4

0

5

10

15

20

25

30

35In

-hos

pita

l mor

talit

y (%

)

Mean glucose quartilesMAG ch

ange

qua

rtiles

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2

3

4

Measures of glucose and ICU mortality

For the entire cohort, the median glucose SD was 1.8 mmol/l (IQR 1.4 to 2.4 mmol/l)

and the median MAG was 0.6 mmol/l/hr (IQR 0.4 to 0.9 mmol/l). The MAG was

significantly stronger associated with ICU death compared with the SD in

univariate analysis, a difference in area under the receiver operating curve of 5.4%

(95% confidence interval [CI], 3.0 to 7.7, p<0.001). The ranges of mean glucose

quartiles and MAG quartiles with the number of patients per stratum and ICU

mortality are shown in Table 2. The highest ICU- and in-hospital mortality was

seen in the upper MAG and upper mean glucose quartile with mortality rates of

24.5% and 28.7% respectively (Figure 2). ICU mortality rates in the lowest MAG

quartile ranged from 0.7% to 5.2%.

Severity of disease and glucose variability The OR for death in the highest quartile of the APACHE II score was 33.8 (95% CI

17.9 to 63.8) and 64.8 (95% CI 28.8 to 145.9) for the maximal SOFA score. Thus we

corrected for quartiles of maximal SOFA score instead of APACHE II score as a

measure of disease severity in our multiple regression model.20 The median MAG

change increased from 0.6 mmol/l/hr (0.3 to 0.9 mmol/l/hr) in the lowest quartiles

SOFA quartile to 0.7 mmol/l/hr (IQR 0.4 to 1.0 mmol/l/hr) in the highest SOFA

quartile (p<0.001). The association of the MAG and ICU mortality did not

significantly differ between surgical and medical patients, p=0.74.

Figure 2A- intensive care unit (ICU) mortality (%) Figure 2B- in-hospital mortality (%) All mortality rates were calculated per quartiles of mean absolute glucose change (MAG), divided by quartiles of mean glucose. Quartile Range mean glucose (mmol/l) Range MAG (mmol/l/hour) 1 <6.92 <0.39 2 6.92 to 7.60 0.39 to 0.60 3 7.60 to 8.89 0.60 to 0.88 4 >8.89 >0.88

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M

AG

qua

rtile

s (m

mol

/l/hr

)

<0

.39

(n=1

432)

0.

39 to

0.6

0 (n

=143

2)

0.60

to 0

.88

(n=1

432)

>0

.88

(n

=143

2)

18/3

46 (5

.2%

) 41

/485

(8.5

%)

41/3

85 (1

0.6%

) 44

/216

(20.

4%)

4/20

4 (2

.0%

) 14

/402

(3.5

%)

24/4

65 (5

.2%

) 24

/365

(6.6

%)

2/34

4 (0

.6%

) 4/

236

(1.7

%)

13/3

53 (3

.7%

) 32

/496

(6.5

%)

Mea

n gl

ucos

e qu

artil

es (m

mol

/l)

<6.

92 (n

=143

2)

6.

92 to

7.6

0 (n

=143

6)

7.60

to 8

.89

(n=1

429)

>8

.89

(n=1

431)

4/

538

(0.7

%)

1/30

9 (0

.3%

) 8/

229

(3.5

%)

87/3

55 (2

4.5%

)

Qua

rtile

s of

MA

G c

hang

e O

R IC

U d

eath

(95%

CI)

OR

in-h

ospi

tal d

eath

(95

% C

I)

1st : <

0.3

9 m

mol

/l/hr

re

fere

nce

refe

renc

e

2nd : 0

.39

to 0

.60

mm

ol/l/

hr

1.5

(0.9

to 2

.5)

1.5

(1.0

to 2

.1)

3rd :

0.60

to 0

.88

mm

ol/l/

hr

1.8

(1.1

to 2

.9)

1.6

(1.2

to 2

.3)

4th :

> 0.

88 m

mol

/l/hr

3.

3 (2

.1 to

5.4

) 2.

8 (2

.0 to

3.9

)

p

for t

rend

<0.

001

p fo

r tre

nd <

0.00

1

Tabl

e 2-

the

num

ber o

f pat

ient

s pe

r stra

tum

are

dep

icte

d w

ith th

e IC

U m

orta

lity

per s

tratu

m (n

and

%).

Ran

ges

of q

uarti

les

of m

ean

abso

lute

gl

ucos

e ch

ange

(MA

G) a

nd m

ean

gluc

ose

are

give

n.

Tabl

e 3-

odd

s ra

tio (O

R) f

or IC

U a

nd in

-hos

pita

l dea

th p

er M

AG

cha

nge

quar

tile,

adj

uste

d fo

r age

, sex

, dia

bete

s m

ellit

us, m

axim

al S

OFA

sco

re, m

ean

gluc

ose,

sev

ere

hypo

glyc

aem

ia a

nd c

ardi

otho

raci

c su

rger

y

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Multivariate analyses

The ORs for ICU death and in-hospital death, adjusted for mean glucose and other

confounders are displayed in Table 3: in the highest MAG quartile, the OR for ICU

death was 3.3 (95% CI 2.1 to 5.4) and for in-hospital death 2.8 (95% CI 2.0 to 3.9).

The risk of death increased per MAG quartile (p for trend <0.001). We performed

logistic regression, with ICU death as dependent variable and MAG quartiles as

the covariate. Per quartile of mean glucose, we calculated ORs for ICU death. We

corrected for age, sex, diabetes mellitus, cardiothoracic surgery, severe

hypoglycaemia, length of ICU stay and maximal SOFA score during admission. The

highest OR for ICU death was found in patients with the highest MAG in the upper

glucose quartile: OR 12.4 (95% CI 3.2 to 47.9, p<0.001). Also in the first quartile of

mean glucose, high MAG change in the fourth quartile was associated with ICU

death, with an OR of 4.1 (95 % CI 1.9 to 9.1, p<0.001). Finally, we performed

analyses for in-hospital death. In each quartile of mean glucose, the upper MAG

quartile seemed predictive of in-hospital death compared to the first quartile, with

ORs for in-hospital death of 2.7 (95% CI 1.6 to 5.2), 1.9 (95% CI 0.9 to 4.2), 2.5 (95%

CI 0.9 to 6.8) and 6.4 (95% CI 2.7 to 15.0) across the first, to the fourth mean

glucose quartile, respectively.

DISCUSSION

In this retrospective cohort study we have shown that GV, expressed as MAG, is

highly associated with ICU death in both high and low ranges of mean glucose. In

combination with a high mean glucose, GV seems most detrimental. It can be

debated whether GV reflected by MAG change is a causative harmful phenomenon,

or whether it is an epiphenomenon, resulting from metabolic deterioration during

severe illness and dying.23 We have attempted to elucidate this by excluding

patients who were on a withholding care policy and correcting for the maximal

SOFA score during admission. From a pathophysiological viewpoint a causal

relationship can be substantiated: in vitro, varying glucose levels have been shown

to enhance cell apoptosis.24 In rats, glycaemic reperfusion after hypoglycaemia

caused neuronal death25 and altering glucose levels were impairing endothelial

function in healthy volunteers.26 Even more, tubulointerstitial cells exposed to

intermittent high glucose concentrations showed enhanced cell growth and collagen

syntheses compared with stable high glucose concentrations.27 Possibly, the

adaptive cell mechanisms that are initiated in case of constant hyperglycaemia are

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ineffective when the hyperglycaemia is not constant but varying, explaining the

toxicity of GV.28

Our findings are in accordance with previous studies that have investigated the

relationship between GV and mortality. In the studies of Krinsley and Egi the

glucose SD in the ICU was a predictor of mortality, independent of mean

glucose10;16 In the study form Bagshaw et al, GV, defined as the occurrence of both

hypoglycaemia and hyperglycaemia within 24 hours of ICU admission, was related

to ICU mortality. Although these studies reported data from large multicentre

cohorts, a strict glycaemic control algorithm was not fully implemented, limiting

external validity for the ICU populations treated with strict glycaemic control.

Furthermore, Krinsley and Egi used the SD as a measure of variability. Because

the blood glucose scale is not normally distributed and asymmetric, he assumptions

of parametric statistics do not apply and the SD was therefore not the optimal

measure of GV.17 In addition, the SD does not take the change per time into

account. Indeed, in our cohort, the MAG change was a stronger predictor of ICU

death than SD. Similar results were reported by Dosset et al, who investigated a

surgical ICU population and found that the largest absolute change in successive

blood glucose measurements during an ICU admission was a strong predictor of

ICU death, whereas the SD was not.29

The APACHE II model is the most widely used parameter when correcting for

disease severity in GV studies in the ICU.9;10;16 However, the APACHE II model

was originally designed to predict mortality in the first 24 hours of ICU

admission.21 Indeed we found disease severity to be a confounder with a relation to

both glycaemic variability and mortality. Correction for disease severity using

maximum SOFA score during admission seemed superior to using APACHE scores

at admission.

Our findings suggest that high mean glucose is less harmful when GV is low and

patients with identical mean glucose can have different mortality rates, depending

on their MAG change. GV was not reported as an outcome measure in the Leuven

studies, the GluControl study, the VISEP study and the NICE-SUGAR study.1;2;4;7;8

Differences in these study outcomes may be attributed to several factors.30 It can be

hypothesised that more stable glucose profiles, with less GV, can be reached when

insulin therapy is combined with a constant administration of glucose-containing

fluids, analogous to the insulin clamp technique.31 Because we have found that GV

is such a strong predictor of ICU death, there is the possibility that differences in

GV can be part of the explanation of differences in mortality reduction in these

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trials. It would be interesting to examine the results of these previous trials in the

context of glycaemic variability.

Of note, ICU mortality in the mean glucose quartiles in our study decreased from

10.1% (<6.92 mmol/l) to 3.6% (7.60 to 8.89 mmol/l) and rose hereafter to 7.0% (>8.89

mmol/l). Such a J-shaped mortality curve has been seen before in patients with

myocardial infarction32-34 and in a large cohort of 66184 ICU patients in whom

glucose values during the first 24 hours of admission were taken into account.35 We

cannot explain the increased mortality in the lower glucose quartile solely by the

occurrence of severe hypoglycaemia (as defined by glucose <=2.5 mmol/l). Previous

results regarding the relation between severe hypoglycaemia and ICU mortality are

conflicting.4;36-38 In our study, the association between ICU mortality in the lowest

mean glucose quartile and highest MAG quartile holds after correcting for severe

hypoglycaemia in the multivariate analysis. This is in concordance with the study

from Bagshaw et al, who found that glucose variability was a stronger predictor of

ICU death than hypoglycaemia alone.9

An important limitation of this study is that was performed in one centre only and

is retrospective in nature. Most patients were admitted for cardiothoracic surgery;

we have however corrected for this potential confounder. A strong point is that we

studied a large and representative group of 5728 mixed ICU patients who were

treated with strict glycaemic control using a computerised algorithm, which

brought a high number of patients in target.18 Furthermore, we used a novel

indicator for GV.

CONCLUSIONS

We have confirmed findings of previous studies that GV is related to ICU and in-

hospital death. Additionally, we have shown that, also for those with persistently

high mean glucose values during admission, low GV seems protective. There

appears to be a synergistic negative effect of high mean glucose in combination with

high GV. To elucidate whether GV is a treatable risk factor in the ICU or a risk

marker only, the effect of lowering GV on ICU survival is to be investigated, for

example by designing an algorithm which aims at lowering variability rather than

the mean glucose and comparing this with an algorithm that lowers the mean

glucose.

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REFERENCES


(2) Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I et al. Intensive insulin therapy in the medical ICU. N Engl J Med 2006; 354(5):449-461.

(3) Van den Berghe GMP, Wouters PJM, Bouillon RM, Weekers FM, Verwaest CM, Schetz MM et al. Outcome benefit of intensive insulin therapy in the critically ill: Insulin dose versus glycemic control. Critical Care Medicine 2003; 31(2):359-366.

(4) Brunkhorst FM, Engel C, Bloos F, Meier-Hellmann A, Ragaller M, Weiler N et al. Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N Engl J Med 2008; 358(2):125-139.

(5) Wiener RS, Wiener DC, Larson RJ. Benefits and risks of tight glucose control in critically ill adults: a meta-analysis. JAMA 2008; 300(8):933-944.

(6) Arabi YMM, Dabbagh OCM, Tamim HMM, Al-Shimemeri AAM, Memish ZAM, Haddad SHM et al. Intensive versus conventional insulin therapy: A randomized controlled trial in medical and surgical critically ill patients. Critical Care Medicine 2008; 36(12):3190-3197.


(8) Preiser JC, Devos P, Ruiz-Santana S, M+®lot C, Annane D, Groeneveld J et al. A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study. Intensive Care Medicine.

(9) Bagshaw SM, Bellomo R, Jacka MJ, Egi M, Hart GK, George C et al. The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit Care 2009; 13(3):R91.

(10) Egi M, Bellomo R, Stachowski E, French CJ, Hart G. Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 2006; 105(2):244-252.

(11) Brownlee M. The Pathobiology of Diabetic Complications: A Unifying Mechanism. Diabetes 2005; 54(6):1615-1625.


(13) Stegenga ME, van der Crabben SN, Levi M, de Vos AF, Tanck MW, Sauerwein HP et al. Hyperglycemia Stimulates Coagulation, Whereas Hyperinsulinemia Impairs Fibrinolysis in Healthy Humans. Diabetes 2006; 55(6):1807-1812.

(14) Van den Berghe G, Schoonheydt K, Becx P, Bruyninckx F, Wouters PJ. Insulin therapy protects the central and peripheral nervous system of intensive care patients. Neurology 2005; 64(8):1348-1353.

(15) Weekers F, Giulietti AP, Michalaki M, Coopmans W, Van Herck E, Mathieu C et al. Metabolic, Endocrine, and Immune Effects of Stress Hyperglycemia in a Rabbit Model of Prolonged Critical Illness. Endocrinology 2003; 144(12):5329-5338.

(16) Krinsley JS. Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit Care Med 2008; 36(11):3008-3013.

(17) Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W. Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care 1997; 20(11):1655-1658.

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(18) Rood E, Bosman RJ, van der Spoel JI, Taylor P, Zandstra DF. Use of a computerized guideline for glucose regulation in the intensive care unit improved both guideline adherence and glucose regulation. J Am Med Inform Assoc 2005; 12(2):172-180.

(19) Waeschle RM, Moerer O, Hilgers R, Herrmann P, Neumann P, Quintel M. The impact of the severity of sepsis on the risk of hypoglycaemia and glycaemic variability. Crit Care 2008; 12(5):R129.

(20) Ferreira FL, Bota DP, Bross A, Melot C, Vincent JL. Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients. JAMA 2001; 286(14):1754-1758.

(21) Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13(10):818-829.

(22) Kramer AA, Zimmerman JE. Predicting outcomes for cardiac surgery patients after intensive care unit admission. Semin Cardiothorac Vasc Anesth 2008; 12(3):175-183.

(23) Mizock BA. Alterations in fuel metabolism in critical illness: hyperglycaemia. Best Pract Res Clin Endocrinol Metab 2001; 15(4):533-551.

(24) Quagliaro L, Piconi L, Assaloni R, Martinelli L, Motz E, Ceriello A. Intermittent high glucose enhances apoptosis related to oxidative stress in human umbilical vein endothelial cells: the role of protein kinase C and NAD(P)H-oxidase activation. Diabetes 2003; 52(11):2795-2804.

(25) Suh SW, Gum ET, Hamby AM, Chan PH, Swanson RA. Hypoglycemic neuronal death is triggered by glucose reperfusion and activation of neuronal NADPH oxidase. J Clin Invest 2007; 117(4):910-918.

(26) Ceriello A, Esposito K, Piconi L, Ihnat MA, Thorpe JE, Testa R et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 2008; 57(5):1349-1354.

(27) Jones SC, Saunders HJ, Qi W, Pollock CA. Intermittent high glucose enhances cell growth and collagen synthesis in cultured human tubulointerstitial cells. Diabetologia 1999; 42(9):1113-1119.

(28) Hirsch IB, Brownlee M. Should minimal blood glucose variability become the gold standard of glycemic control? Journal of Diabetes and its Complications 2005; 19(3):178-181.

(29) Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris JM, Jr., May AK. Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg 2008; 74(8):679-685.

(30) Vanhorebeek IP, Langouche LP, Van den Berghe GMP. Tight blood glucose control: What is the evidence? Critical Care Medicine, Metabolic Support in Sepsis and Multiple Organ Failure: Proceedings of a Roundtable Conference in Brussels, Belgium, 2007; 35(9):S496-S502.

(31) DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979; 237(3):E214-E223.

(32) Pinto DS, Skolnick AH, Kirtane AJ, Murphy SA, Barron HV, Giugliano RP et al. U-shaped relationship of blood glucose with adverse outcomes among patients with ST-segment elevation myocardial infarction. J Am Coll Cardiol 2005; 46(1):178-180.

(33) Pinto DS, Kirtane AJ, Pride YB, Murphy SA, Sabatine MS, Cannon CP et al. Association of blood glucose with angiographic and clinical outcomes among patients with ST-segment elevation myocardial infarction (from the CLARITY-TIMI-28 study). Am J Cardiol 2008; 101(3):303-307.

(34) Kosiborod M, Inzucchi SE, Krumholz HM, Xiao L, Jones PG, Fiske S et al. Glucometrics in patients hospitalized with acute myocardial infarction: defining the optimal outcomes-based measure of risk. Circulation 2008; 117(8):1018-1027.

(35) Bagshaw SMM, Egi MM, George CM, Bellomo RM, for the ANZICS Database Management Committee. Early blood glucose control and mortality in critically ill patients in Australia. Critical Care Medicine 2009; 37(2):463-470.

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(36) Krinsley JS, Grover A. Severe hypoglycemia in critically ill patients: risk factors and outcomes. Crit Care Med 2007; 35(10):2262-2267.

(37) Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F et al. Intensive Insulin Therapy in Mixed Medical/Surgical Intensive Care Units: Benefit Versus Harm. Diabetes 2006; 55(11):3151-3159.

(38) Vriesendorp TM, DeVries JH, van SS, Moeniralam HS, de JE, Roos YB et al. Evaluation of short-term consequences of hypoglycemia in an intensive care unit. Crit Care Med 2006; 34(11):2714-2718.

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Hypoglycaemia is associated with ICU mortality

J Hermanides, RJ Bosman, TM Vriesendorp, R Dotsch,

FR Rosendaal, DF Zandstra, JBL Hoekstra, JH DeVries

Critical Care Medicine, In Press

ABSTRACT

Objective The implementation of intensive insulin therapy in the intensive care

unit (ICU) is accompanied by an increase in hypoglycaemia. We studied the

relation between hypoglycaemia on ICU mortality, since the evidence on this

subject is conflicting.

Design Retrospective database cohort study.

Setting An 18-bed medical/surgical ICU in a teaching hospital (Onze Lieve Vrouwe

Gasthuis Hospital, Amsterdam, the Netherlands).

Patients 5961 patients admitted to from 2004 to 2007 were analysed. Readmissions

and patients with a withholding care policy or with hypoglycaemia on the first

glucose measurement were excluded. Patients were treated with a computerised

insulin algorithm (target glucose range 4.0 to 7.0 mmol/l).

Measurements All first episodes of hypoglycaemia (glucose <=2.5 mmol/l) were

derived from 154015 glucose values. Using Poisson regression, the incidence rates

(IR) for ICU death and incidence rate ratio (IRR) comparing exposure and non-

exposure to hypoglycaemia were calculated. Patients were considered to be exposed

to hypoglycaemia from the event until the end of ICU admittance. We corrected for

severity of disease using the daily Sequential Organ Failure Assessment score. Age,

sex, cardiothoracic surgery, sepsis and diabetes mellitus were also included as

possible confounders.

Main results 288 (4.8%) patients experienced at least one episode of hypoglycaemia.

Median age was 68 (58 to 75) years, 66% was male and 6.4% died in the ICU. The

IR of death in patients exposed to hypoglycaemia was 40/1000 ICU days, as

compared to 17/1000 ICU days in patients without exposure. The adjusted IRR for

ICU death was 2.1 (95% CI 1.6-2.8, p<0.001).

Conclusions Hypoglycaemia is related to ICU mortality, also when adjusted for a

daily adjudicated measure of disease severity, indicating the possibility of a causal

relationship.

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INTRODUCTION

Hyperglycaemia in the ICU is common, also in patients without known diabetes,

and is related to poor outcome.1 The implementation of strict glucose control with

intensive insulin therapy (IIT) in the Intensive Care Unit (ICU), targeting for a

fasting morning glucose of 4.4-6.1 mmol/l was proven to be beneficial with regard to

mortality in the two „Leuven‰ trials, if ICU treatment exceeded five and three days,

respectively. 2;3 Two large multicentre randomised controlled trials were carried

out to confirm these results but were terminated prematurely, because of high rates

of sever hypoglycaemia or because the target glucose range was not reached.4;5 The

recently published NICE-SUGAR trial showed that mortality was increased, when

the investigators aimed for a blood glucose 4.5 to 6.0 mmol/l in the IIT group as

compared to <=10.0 mmol/l in the control group (27.5% vs. 24.9%, p=0.02).6 Several

explanations have already been proposed to explain the conflicting results

regarding strict glucose control, including the increased risk of (severe)

hypoglycaemia that accompanies IIT.7-9 A meta-analysis including the NICE-

SUGAR data indicated that IIT was associated with a six-fold increased risk for

hypoglycaemic events (95% CI 4.5 to 8.0). However, the causal relation with

mortality in the ICU remains unclear. Several studies have specifically

investigated outcomes after hypoglycaemia in the ICU and yielded conflicting

results.4;10-13 Because it is possible that hypoglycaemia is a risk marker for severe

illness or dying, rather than a risk factor for mortality, it is important to correct for

severity of disease. All previous studies used the APACHE II score to this purpose,

a validated score designed to predict mortality in the first 24 hours of ICU

admittance. To discriminate between hypoglycaemia as a risk marker and a risk

factor, we reasoned the optimal correction is for severity of disease on the day of the

hypoglycaemic event. Therefore in this study we investigated the relation between

hypoglycaemia in the ICU and mortality, corrected for a measure of severity of

disease taken on the day of the hypoglycaemic event.


Design and setting

We performed a cohort study in an 18-bed mixed surgical/medical ICU in a

teaching hospital (Onze Lieve Vrouwe Gasthuis Hospital, Amsterdam, the

Netherlands). The nurse-to-patient ratio was on average 1:2 and all beds were

equipped with a clinical information system (iMD-Soft; MetaVision, Tel Aviv,

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Israel). Informed consent was not required according to Dutch Ethical Review

Board regulations, as it concerned analysis of anonimysed data.

Glucose regulation protocol

The glucose regulation algorithm (target range 4.0 to 7.0 mmol/l) was fully

computerised and connected to the clinical information system. The software

suggested an insulin infusion rate based on the current glucose value, the rate of

glucose change over the previous 5 measurements, previous insulin drip rates and

given insulin boluses. The software also provided the timing of the next glucose

measurement which could vary between 15 minutes and 4 hours. In case of a

glucose measurement below <3.5 mmol/l the insulin pump was stopped and 20 ml

of 30% glucose administered. Glucose was measured 15 minutes hereafter and if

euglycaemia was reached, the insulin pump was restarted at 50% of the previous

infusion rate. If still below 3.5 mmol/l, glucose was administered again and the

insulin pump was not restarted for at least two hours. In case of a glucose

measurement between 3.5 and 4.5 mmol/l, the insulin pump infusion rate was

decreased with the percentage of decrease between the current and the last glucose

value. Within 24 hours after admission, enteral feeding was started, targeting for

2000 kcal/24h within 48 hour or 1500 kcal/24h within 24 hours. In case of gastric

retention, a feeding tube was inserted in the duodenum. When normal eating was

resumed, the glucose regulation algorithm was stopped. The protocol was

implemented in 2001.14

Hypoglycaemia

Glucose was measured from blood samples obtained from an arterial catheter using

the AccuCheck (Roche/Hitachi®, Basel, Switzerland). We obtained all

measurements from the clinical information system. Hypoglycaemia was defined as

one or more glucose measurements <=2.5 mmol/l. This is using the same cut-off

value as we used earlier and similar to the cut-off values used by van den Berghe

and the NICE-SUGAR investigators (<=2.2 mmol/l).6;13;15

Because the cut-off value for (severe) hypoglycaemia in the ICU in different studies

ranges from 2.2 to 4.5 mmol/l, we also assessed the risk for ICU death associated

with different cut-off values for hypoglycaemia; <=1.4, 1.9, 3.1, 3.6, 4.2, 4.7, 5.3 and

5.8 mmol/l respectively.

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Data collection

All data was extracted from the clinical information system. Patients admitted

between January 2004 and January 1st 2008 were included. No changes in the

glucose regulation protocol were applied in this period. Readmissions, patients with

a withholding care policy and patients who had hypoglycaemia at admission were

excluded. We collected information on the medical history, demographic variables

and admission diagnosis. For each day of ICU admittance, we calculated the

Sequential Organ Failure Assessment (SOFA) Score, as a measure of severity of

disease on that particular day.16 If the SOFA score was not available on the day of

ICU discharge, we imputed the SOFA score of the preceding day.


Results are presented as median with interquartile range (IQR). We calculated the

incidence rate of ICU death per 1000 ICU days for patients with- and without

hypoglycaemia and calculated the crude incidence rate ratio (IRR), thereby

assuming a more or less constant hazard for ICU death. Every calendar day of ICU

admittance was counted as 1 day of ICU admittance. When a patient experienced

hypoglycaemia, we considered the patient to be exposed all subsequent days of the

ICU admittance. To correct for severity of disease per day, we divided the daily

SOFA scores into tertiles and compared mortality in the exposed to the non-

exposed within each SOFA tertile. We created tertiles to maintain sufficient power

for multivariate analyses. Using Poisson regression, we adjusted the IRR for the

altering SOFA-tertile. We also included the interaction between SOFA-tertile and

ICU admission days in the model (SOFA tertile*ICU admission days), as the

predictive value of the SOFA score differs when patients are admitted longer.

Furthermore, we adjusted for admission diagnosis of sepsis, according to the

APACHE II diagnostic criteria, as this can cause hypoglycaemia and is also related

to ICU mortality.17 Diabetes mellitus is known to predispose patients to

hypoglycaemia in the ICU17 however might affect outcome positively18 and was

therefore included in the analyses. Cardiothoracic surgery was also included as a

potential confounder, because mortality among cardiothoracic patients is generally

lower and they differ with respect to several demographic and physiological

characteristics.19

Finally, the analyses were adjusted for age and sex. The same analysis was

performed for different cut-off values for hypoglycaemia. All statistical analyses

were performed in SPSS 16.0 (SPSS Inc.,Chicago, IL, USA).

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RESULTS

From 6725 admissions, 5961 patients were eligible for analyses after excluding 656

readmissions and patients with a withholding care policy (n=86) or glucose <=2.5

mmol/l according to their first glucose measurement (n=22). The patient

characteristics are displayed in Table 1 and Table 2. In total 154015 glucose values

were collected, a median of 11 values per day per patient (IQR 6 to 14). Median age

was 68 years (IQR 58 to 75), 66% of the population was male and 6.4% of the

patients died in the ICU. Patients were in target range a median of 42% of the time

(IQR 17 to 56) and 288 (4.8 %) of patients encountered one or more episodes of

hypoglycaemia (<=2.5 mmol/l) and of these 113 patients experienced more than 1

episode.In total we collected 20737 SOFA scores (1152 missing scores (5.6%),

mainly SOFA scores of discharge day), of which the tertiles ranged from 0-4, 5-6,

>6.

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Surgical (n=4582)

Medical (n=1379)

Total cohort (n=5961)

Age, years (median, IQR) 68 (59 to 78) 64 (52 to 76) 68 (58 to 75) Gender, male n (%) 3058 (67) 863 (62) 3907 (66) APACHE II score on admission (median, IQR) 15 (12 to 17) 24 (18 to 31) 16 (13 to 20) Max SOFA score during admission* (median, IQR) 5 (4 to 6) 8 (5 to 11) 6 (4 to 7) ICU stay, days (median (IQR) 0.9 (0.8 to 1.1) 2.6 (1.1 to 5.3) 1.0 (0.8 to 2.0) Diabetes mellitus, n (%) 691 (15.1) 8 (0.6) 699 (11.7%) Died ICU, n (%) 81 (1.8) 305 (21.8) 380 (6.4%) Died hospital, n (%) 211 (4.6) 445 (31.9) 648 (10.9%) Severe hypoglycaemia (>= 1 event), n (%) 111 (2.4) 177 (12.8) 288 (4.8%) Admission Category n (%) -Cardiovascular 4012 (87.6) 253 (18.3) 4265 (71.5) -After cardiac arrest 10 (0.2) 298 (21.6) 308 (5.2) -Sepsis 53 (1.2) 221 (16.0) 274 (4.6) -Gastrointestinal 255 (5.6) 57 (4.1) 312 (5.2) -Haematological 8 (0.2) 9 (0.7) 17 (0.3) -Renal 12 (0.3) 26 (1.9) 38 (0.6) -Metabolic 7 (0.2) 45 (3.3) 52 (0.9) -Neurological 47 (1.0) 168 (12.2) 215 (3.6) -Respiratory 178 (3.9) 302 (21.9) 476 (8.0)

No severe hypoglycaemic episode (n=5673)

>=1 severe hypoglycaemic episode (n=288)

Age, years (median, IQR) 68 (58 to 75) 68 (57 to 77) Gender, male n (%) 3740 (65.9) 167 (58.0) APACHE II score on admission (median, IQR) 15 (12 to 19) 23 (17 to 30) SOFA score* (median, IQR) 5 (4-7) 9 (6-13) ICU stay, days (median (IQR) 1.0 (0.8 to 1.9) 5.8 (2.0 to 12.9) Diabetes mellitus, n (%) 666 (11.7) 33 (11.5) Died ICU, n (%) 312 (5.5) 68 (23.6) Died hospital, n (%) 549 (9.7) 99 (34.4) Medical admission, n (%) 1202 (21.2) 177 (61.5) Surgical admission, n (%) 4471 (78.8) 111 (38.5)

Table 1- population characteristics; APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment ICU Intensive Care Unit *Maximum score of the total scores calculated each ICU day

Table 2- population characteristics for patients with- and without severe hypoglycaemia; APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment ICU Intensive Care Unit *Maximum score during admission, calculated from the total scores calculated each ICU day

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SOFA score

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ICU mortality and hypoglycaemia

The IR of ICU death was 19/1000 ICU days. The overall IR for ICU death after

experiencing a hypoglycaemic event was 40/1000 ICU days, as compared to 17/1000

ICU days without a hypoglycaemic episode, crude IRR 2.3 (95% CI 1.8 to 3.1,

p<0.001). The IRs after experiencing hypoglycaemia were higher across all SOFA

score ranges (Figure 1). After adjusting for age, sex, admission for cardiothoracic

surgery, sepsis, SOFA score, ICU days and the interaction between SOFA score and

ICU days, the adjusted IRR was to 2.1 (95% CI 1.6 to 2.8, p<0.001). The crude IR

for patients experiencing only one episode of hypoglycaemia was 36/1000 ICU days

as compared to 42/1000 ICU days for patients with more than one episode, IRR 1.2

(95% CI 0.7 to 2.0, p=0.58). When the APACHE II score was used to correct for

severity of disease, instead of the daily SOFA score, the IRR for ICU death was 1.6

(95% CI 1.2 to 2.1).

Figure 1- the incidence of intensive care unit (ICU) death per 1000 ICU days with and without hypoglycaemia (<=2.5 mmol/l) for daily sequential organ failure scores (SOFA) 0 to 4, 5 to 6 and >6.

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Figure 2 shows the adjusted IRRs for the different cut-off values for hypoglycaemia

in the ICU. There was an increased risk for ICU death up to the cut-off value of 4.7

mmol/l, IRR 1.4 (95% CI 1.1 to 1.8, p=0.006). With cut-off values above 4.7 mmol/l,

no effect on IRR for ICU death was found, IRR 1.1 (0.9 to 1.4, p=0.44).

DISCUSSION

In this cohort study we showed that hypoglycaemia (<=2.5 mmol/l) in the ICU is

accompanied by an increase in ICU death 2.1 (95% CI 1.6 to 2.8, p<0.001) after

adjusting for the daily SOFA score over time.

In a previous nested case-control study we did not find an association between

hypoglycaemia and in-hospital death (hazard ratio 1.03, 95% CI 0.68 to 1.56) in a

relative small sample of 156 hypoglycaemic events.13 Arabi and coworkers also

found no significant relation between hypoglycaemia and ICU mortality.12 In

contrast with these findings, Krinsley et al. found in a small retrospective case-

control designed study that hypoglycaemia (<2.2 mmol/l) was associated with an

increased mortality risk (OR 2.28, 95% CI 1.41 to 3.70) in a population that was

only partly treated with IIT.11 Also, Bagshaw et al showed in a large multicentre

ICU cohort (n=66,184), that the OR for ICU mortality after exposure to a glucose

Figure 2- Adjusted incidence rate ratios (IRR) for intensive care unit (ICU) death when different cut-off values for hypoglycaemia are used.

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value <4.5 mmol/l was 1.41 (95% CI 1.31 to 1.54).10 However, this study was limited

to the first 24 hours of ICU admission and a relative small number of glucose

values per patient (~2) were collected.

All previous studies used the APACHE II score to correct for disease severity.20

We excluded patients with a severe hypoglycaemia as the first glucose

measurement. This was because these patients were not under strict glucose

control before the event and spontaneous hypoglycaemia can be a marker of severe

disease.21-23

A strength of the current study is that we attempted to adjust for severity of

disease as a changing variable over time in stead of using the APACHE II score.

Although APACHE II is validated to predict mortality, it is determined at ICU

admission and does not take any changes thereafter into account. The SOFA score

is a measure of disease severity that it is updated daily during admittance and

therefore seems to provide a better distinction between hypoglycaemia as a risk

factor or risk marker. The use of the SOFA score is limited by the interaction with

time spent in the ICU and different predictive power of identical SOFA scores

depending on the failing organ systems that contribute to the score.24 However, in

the current study it was the best available predictive score that could be assessed

on a daily basis and it is validated to discriminate between ICU survivors and non-

survivors.16;24 Furthermore, we have attempted to correct for the interaction with

time spent in ICU by including this in the model. After experiencing a

hypoglycaemic event, we considered patients to be exposed the remaining days of

the ICU admittance, because from a pathofysiological viewpoint hypoglycaemia

may cause damage that is, at least during ICU stay, likely to be sustained.

Hypoglycaemia may contribute to ICU mortality by inducing neuroglycopenia with

neuronal cell loss and hypoglycaemic coma, especially after hyperglycaemic

reperfusion.13;25;26 Furthermore, hypoglycaemia might provoke ischemia in pre-

existing vascular disease in diabetic patients27 and it has also been shown to induce

platelet activation and inflammation in experimental setting.28-30 Hypoglycaemia

could thus contribute to ICU mortality by aggravating overall illness.

Another strength of this study is that adequate glycaemic control was achieved

while the hypoglycaemia rate was low compared to other studies.31 Our study is

limited by its single-centre origin. However, there was a high quality of data

collection, because all clinical information and laboratory values were directly or

even automatically stored in the computerised clinical information system.

The additional analyses we performed investigating different cut-off values for

hypoglycaemia showed that up to 4.7 mmol/l, hypoglycaemia was associated with a

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significant increased risk for ICU death. This is in concordance with the study of

Bagshaw and colleagues, who found an increased mortality risk when defining

hypoglycaemia as <4.5 mmol/l. The recently published NICE-SUGAR study was the

start of a debate about the optimal blood glucose range in the ICU6 and a higher,

perhaps safer range than the tight range of the Leuven studies is proposed.2 The

present and previous studies thus suggest that hypoglycaemia might be harmful

when aiming for strict glycaemic control, targeting for a range between 4.0 to 7.0

mmol/l.5

Future investigations looking at strict glycaemic control in the ICU should consider

the possibility that even glucose values below 4.7 mmol/l are harmful. Higher

target ranges for glucose control will diminish the incidence hypoglycaemia and

seem justified.

CONCLUSIONS

Hypoglycaemia is related to ICU mortality, also when adjusted for a daily

adjudicated measure of disease severity. Although residual confounding can never

be ruled out, a causal relationship between hypoglycaemia and ICU mortality is a

likely possibility.

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REFERENCES



(3) Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I et al. Intensive insulin therapy in the medical ICU. N Engl J Med 2006; 354(5):449-461.

(4) Brunkhorst FM, Engel C, Bloos F, Meier-Hellmann A, Ragaller M, Weiler N et al. Intensive Insulin Therapy and Pentastarch Resuscitation in Severe Sepsis. N Engl J Med 2008; 358(2):125-139.

(5) Preiser JC, Devos P, Ruiz-Santana S, M��lot C, Annane D, Groeneveld J et al. A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study. Intensive Care Medicine.


(7) Preiser JC. NICE-SUGAR: the end of a sweet dream? Critical Care 2009; 13(3):143.

(8) Bellomo R, Egi M. What Is a NICE-SUGAR for Patients in the Intensive Care Unit? Mayo Clinic Proceedings 2009; 84(5):400-402.

(9) Van den Berghe G, Schetz M, Vlasselaers D, Hermans G, Wilmer A, Bouillon R et al. Intensive Insulin Therapy in Critically Ill Patients: NICE-SUGAR or Leuven Blood Glucose Target? J Clin Endocrinol Metab 2009; 94(9):3163-3170.

(10) Bagshaw SM, Bellomo R, Jacka MJ, Egi M, Hart GK, George C et al. The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit Care 2009; 13(3):R91.

(11) Krinsley JS, Grover A. Severe hypoglycemia in critically ill patients: risk factors and outcomes. Crit Care Med 2007; 35(10):2262-2267.

(12) Arabi YMM, Tamim HMM, Rishu AHM. Hypoglycemia with intensive insulin therapy in critically ill patients: Predisposing factors and association with mortality. Critical Care Medicine 2009; 37(9):2536-2544.

(13) Vriesendorp TM, DeVries JH, van SS, Moeniralam HS, de JE, Roos YB et al. Evaluation of short-term consequences of hypoglycemia in an intensive care unit. Crit Care Med 2006; 34(11):2714-2718.

(14) Rood E, Bosman RJ, van der Spoel JI, Taylor P, Zandstra DF. Use of a Computerized Guideline for Glucose Regulation in the Intensive Care Unit Improved Both Guideline Adherence and Glucose Regulation. J Am Med Inform Assoc 2005; 12(2):172-180.

(15) Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F et al. Intensive insulin therapy in mixed medical/surgical intensive care units: benefit versus harm. Diabetes 2006; 55(11):3151-3159.

(16) Ferreira FL, Bota DP, Bross A, Melot C, Vincent JL. Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients. JAMA 2001; 286(14):1754-1758.

(17) Vriesendorp TM, van SS, DeVries JH, de JE, Rosendaal FR, Schultz MJ et al. Predisposing factors for hypoglycemia in the intensive care unit. Crit Care Med 2006; 34(1):96-101.

(18) Graham BB, Keniston A, Gajic O, Trillo Alvarez CA, Medvedev S, Douglas IS. Diabetes mellitus does not adversely affect outcomes from a critical illness. Crit Care Med 2010; 38(1):16-24.

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(19) Kramer AA, Zimmerman JE. Predicting outcomes for cardiac surgery patients after intensive care unit admission. Semin Cardiothorac Vasc Anesth 2008; 12(3):175-183.

(20) Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13(10):818-829.

(21) Goyal A, Mehta SR, Diaz R, Gerstein HC, Afzal R, Xavier D et al. Differential clinical outcomes associated with hypoglycemia and hyperglycemia in acute myocardial infarction. Circulation 2009; 120(24):2429-2437.

(22) Kosiborod M, Inzucchi SE, Goyal A, Krumholz HM, Masoudi FA, Xiao L et al. Relationship between spontaneous and iatrogenic hypoglycemia and mortality in patients hospitalized with acute myocardial infarction. JAMA 2009; 301(15):1556-1564.

(23) Mendoza A, Kim YN, Chernoff A. Hypoglycemia in hospitalized adult patients without diabetes. Endocr Pract 2005; 11(2):91-96.

(24) Vincent JL, de MA, Cantraine F, Moreno R, Takala J, Suter PM et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. Crit Care Med 1998; 26(11):1793-1800.

(25) Fujioka M, Okuchi K, Hiramatsu KI, Sakaki T, Sakaguchi S, Ishii Y. Specific Changes in Human Brain After Hypoglycemic Injury. Stroke 1997; 28(3):584-587.

(26) Suh SW, Gum ET, Hamby AM, Chan PH, Swanson RA. Hypoglycemic neuronal death is triggered by glucose reperfusion and activation of neuronal NADPH oxidase. J Clin Invest 2007; 117(4):910-918.

(27) Wright RJ, Frier BM. Vascular disease and diabetes: is hypoglycaemia an aggravating factor? Diabetes Metab Res Rev 2008; 24(5):353-363.

(28) Hutton RA, Mikhailidis D, Dormandy KM, Ginsburg J. Platelet aggregation studies during transient hypoglycaemia: a potential method for evaluating platelet function. J Clin Pathol 1979; 32(5):434-438.

(29) Razavi Nematollahi L, Kitabchi AE, Stentz FB, Wan JY, Larijani BA, Tehrani MM et al. Proinflammatory cytokines in response to insulin-induced hypoglycemic stress in healthy subjects. Metabolism 2009; 58(4):443-448.

(30) Trovati M, Anfossi G, Cavalot F, Vitali S, Massucco P, Mularoni E et al. Studies on mechanisms involved in hypoglycemia-induced platelet activation. Diabetes 1986; 35(7):818-825.

(31) Vriesendorp TM, DeVries JH, Hoekstra JB. Hypoglycemia and strict glycemic control in critically ill patients. Current Opinion in Critical Care 2008; 14(4):397-402.

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Mean glucose during ICU admission is related to

mortality by a U-shaped curve; implications for

clinical care

SE Siegelaar, J Hermanides, HM Oudemans- van

Straaten, PHJ van der Voort, RJ Bosman, DF Zandstra,

JH DeVries


ABSTRACT

Purpose To determine in which way glucose regulation is associated with ICU

mortality thereby trying to reconcile the conflicting data from the Leuven and

NICE-SUGAR trials, since recently the optimal glucose target range has become

unclear.

Methods Retrospective database cohort study including patients admitted between

January 2004 and December 2007 in a 20-bed medical/surgical ICU in a teaching

hospital. Hyperglycaemia was treated using a fully computerised algorithm

targeting for glucose levels of 4.0 to 7.0 mmol/l. 5983 patients were eligible for

analyses. We randomly selected 2435 patients with a surgical/medical ICU

admission ratio of 55/45%, to enable comparison with the Leuven and NICE-

SUGAR populations.

Results The cohort was subdivided in deciles of increasing mean glucose. We

examined the relation between mean glucose strata and mortality. We observed the

highest mortality in the lower and higher strata. Logistic regression analysis

adjusted for age, sex, APACHE II score and admission duration showed that mean

glucose levels <7.0 mmol/l and >9.0 mmol/l were associated with significantly

increased ICU mortality (OR >=2.06 and >=2.33 respectively). Limitations of the

study were its retrospective design and possible incomplete correction for severity

of disease. Conclusions Mean glucose during ICU admission is related to mortality by a U-

shaped curve. A Âsafe rangeÊ of mean glucose regulation might be defined between

7.0 and 9.0 mmol/l. The U-shaped curve may help to explain the increased

mortality in the intensively treated group of the NICE-SUGAR study but is at odds

with the low mortality in the intensively treated groups of the Leuven studies.

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INTRODUCTION

Due to inflammatory and neuro-endocrine derangements in critically ill patients,

stress hyperglycaemia associated with high hepatic glucose output and insulin

resistance is common in the intensive care unit (ICU).1 This stress hyperglycaemia

is associated with poor outcome.2 Moreover, several studies report a deleterious

effect of glycaemic variability over and above mean glucose, after correction for

severity of disease.3-5

In 2001 van den Berghe and colleagues published the first randomised controlled

trial (RCT) comparing normalization of glycaemia by intensive insulin treatment

(IIT) with conventional glycaemic control at a surgical ICU (glucose target: 4.4 to

6.1 mmol/l versus 10.0 to 11.1 mmol/l).6 They reported an impressive reduction in

mortality with IIT. This same group failed to reproduce these findings in the entire

population of patients in their medical ICU,7 however mortality was lower in the

predefined subgroup of patients receiving IIT for more than three days. After

pooling of the data of both RCTÊs IIT seemed associated with a reduction in

mortality.8 Based on these ÂLeuven trialsÊ, many hospitals decided to implement

protocols and target normalisation of glucose levels to improve patient care.

Recently, after the publication of two inconclusive multicentre studies (the Volume

Substitution and Insulin Therapy in Severe Sepsis [VISEP]9 and the

GluControl10;11 studies) followed by the very recently reported Normoglycaemia in

Intensive Care Evaluation- Survival Using Glucose Algorithm Regulation (NICE-

SUGAR) trial12, confusion has been raised about the optimal tightness of control:

the NICE-SUGAR trial investigators reported an absolute increase in deaths at 90

days with IIT (glucose target: 4.5 to 6.0 mmol/l versus 8.0 to 10.0 mmol/l).

Additionally, a recently published meta-analysis including this latter trial showed

that intensive insulin therapy significantly increased the risk of hypoglycaemia and

conferred no overall mortality benefit among critically ill patients.13

The goal of this study is to report glucose and mortality data from a general ICU of

a teaching hospital in the Netherlands and to compare these data with the results

of the Leuven studies and NICE-SUGAR trial, with the goal to come to terms

regarding the cause of their conflicting results and to define the optimal tightness

of glycaemic control.

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Cohorts, setting and data collection

We collected information about patients admitted between January 2004 and

December 2007 in a 20-bed medical/surgical ICU in a teaching hospital (Onze Lieve

Vrouwe Gasthuis Hospital, Amsterdam, the Netherlands; the OLVG cohort). The

average patient-to-nurse ratio was 1:2. All beds were equipped with a clinical

information system (iMD-Soft; MetaVision, Tel Aviv, Israel) from which all clinical

and laboratory data was extracted. The glucose regulation algorithm was

implemented successfully in 200114, targeting for glucose values between 4.0 and

7.0 mmol/l. Using a sliding scale computerised algorithm, the nursing staff was

instructed to adjust the insulin infusion rate depending on the current glucose

value and the rate of glucose change (based on the previous five measurements).

The software also provided the time the next glucose measurement was due, which

could vary from 15 minutes up to 4 hours. Routinely, enteral feeding was started

within 24 hours after admission, aiming at 1500 ml within 24 hours, and

subsequently adjusted to the patientÊs requirements. A duodenal feeding tube was

inserted in case of persistent gastric retention. The tight glucose algorithm was

deactivated when patients resumed normal eating.

We excluded readmissions, patients with a withholding care policy and patients

with only one glucose value measured during admission. From these patients we

randomly allocated a definitive population in which 45% of the patients had a

medical ICU admission diagnosis and 55% of the patients a surgical ICU admission

diagnosis. This ratio lies in between the populations of the Leuven and NICE-

SUGAR studies. From the clinical information system we collected demographic

variables, mortality rates in the ICU and glucose values. As severity of disease

measures we used the maximal Sequential Organ Failure Assessment (SOFA) score,

which was determined daily during ICU admission15 and the Acute Physiology and

Chronic Health Evaluation II (APACHE II) score16. Informed consent is not

required according to Dutch Ethical Review Board regulations, because a

retrospective analysis of anonymous data was performed.

Glucose measurers

For each patient we calculated the mean overall glucose during admission from all

glucose values measured during admission and the mean morning glucose from the

first value available between 5.00h and 7.00h am per patient per day. Glucose was

obtained from arterial blood samples, with a handheld glucose measurement device

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(AccuCheck, Roche/Hitachi®, Basel, Switzerland). Results were automatically

stored in the clinical information system.

For comparison with the combined Leuven and NICE-SUGAR studies, we used the

mean glucose during admission and ICU mortality rates as published for both the

IIT and conventional group.

Data interpretation

The cohort characteristics are presented as mean � standard deviation (SD) or

median with interquartile range (IQR), depending on the distribution of the data.

The mean glucose values and standard deviations were divided into 10 strata with

equal numbers of patients per group. For each stratum, the ICU mortality was

calculated. Subsequently we performed a logistic regression analysis to calculate

the odds ratio (OR) for ICU mortality per glucose stratum. The stratum with the

lowest mortality incidence was used as reference. In this model we adjusted for age,

sex, severity of disease (APACHE II score) and admission duration (i.e. <= or

>24hr). The latter because glucose values are higher and have a wider range in the

first 24 hours of admission, biasing the patients with longer admission times and

corresponding lower mean glucose values. For the combined Leuven studies and the

NICE-SUGAR study, we assessed the 95% population range around the mean

glucose by taking the mean minus- and plus 1.96 times the standard deviation. We

plotted the mortality rate in the Leuven and NICE-SUGAR trials against the

mortality in the corresponding stratum of mean glucose in our cohort.

RESULTS

In total 5983 patients were eligible for analyses of the mean glucose for the OLVG

population after excluding 656 readmissions, 86 patients with a withholding care

policy and 155 patients with only one glucose value measured. From this

population we randomly selected the final cohort of 2435 patients with a

surgical/medical ICU admission ratio of 55/45%, to enable comparison with the

Leuven and NICE-SUGAR populations. In the final cohort a median (IQR) of 12 (8

to 14) glucose values per admission day per patient was collected. The total

population and the random sample are comparable regarding all baseline

characteristics (data not shown). The characteristics of our population and those in

the Leuven and NICE-SUGAR trials are displayed in Table 1.

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Mean glucose

The overall mean (SD) glucose of the OLVG population was 8.0 (2.3) mmol/l (Table

1). The mean glucose values of the first 24 hours of admission were larger and had

a wider range than mean glucose values after 24 hours (mean [SD] 8.3 [2.8] mmol/l,

range 0.6 to 40.2 mmol/l and 7.2 [1.6] mmol/l, range 3.2 to 31.1 mmol/l respectively).

The mean morning glucose was 7.5 [2.5] mmol/l.

After dividing the mean glucose into ten equally sized strata, the lowest mean

glucose stratum ranged 6.3 mmol/l and below. The highest stratum went up from

10.3 mmol/l. Mean glucose ranges per stratum and corresponding mortality rates

are displayed in Figure 1. This results in a U-shaped curve relation between

glucose and mortality, with high mortality in the lowest and highest glucose-

stratum, 21.3% and 27.6% respectively. The strata of the total population showed a

comparable U-curve with the random sample (data not shown). Logistic regression

analysis showed that glucose values below 7.0 mmol/l and above 9.1 mmol/l were

associated with a significant higher OR for ICU mortality compared to the stratum

with the lowest mortality (stratum 6). This results in a Âsafe rangeÊ of 7.0 up to 9.0

mmol/l (Figure 2). The mean glucose, SD and 95% population range of the combined

Leuven and NICE-SUGAR studies are depicted in Table 1.

In the NICE-SUGAR study, the mean glucose of the IIT group (6.4 mmol/l) falls

into the stratum with increased mortality compared to the conventional group (8.0

mmol/), which lies in the safe range of the OLVG population (Figure 1). The mean

of the IIT group of the combined Leuven studies (5.8 mmol/l) falls into the stratum

of 6.3 mmol/l and below, where mortality in the OLVG cohort is highest. The mean

of the conventional group in the combined Leuven studies (8.4 mmol/l) lies in the

safe range of the OLVG population (Figure 1).

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Table 1- characteristics of the studied cohorts; Hypoglycaem

ia was defined as at least one glucose value <=2.2 m

mol/l

¶ time-w

eighted blood glucose12

IIT, Intensive Insulin Therapy; AP

AC

HE

, Acute P

hysiology and Chronic H

ealth Evaluation; S

OFA

, Sequential O

rgan Failure Assessm

ent; ICU

, Intensive Care U

nit, n.a.; not available

O

LVG

Com

bined Leuven N

ICE-SU

GA

R

IIT (n=2435)

IIT (n=1388) C

onventional (n=1360) IIT (n=3016)

Conventional (n=3014)

Age, years (m

ean ± SD)

63.9 ± 15.1 63 ± 15

63 ± 15 60 ± 17

60 ± 17 G

ender, female n (%

) 857 (35.2)

460(34) 449 (32)

1128 (37) 1079 (36)

Diagnostic group, n (%

) -S

urgical -M

edical

1096 (45) 1339 (55)

765 (55) 595 (45)

783 (58) 605 (42)

1112 (37) 1903 (63)

1121 (37) 1893 (63)

APA

CH

E II score on admission (m

ean ± SD)

20.3 ± 8.6 15 ± 10

16 ± 9 21 ± 8

21 ± 8 D

iabetes Mellitus, n (%

) 180 (7.4)

207 (15) 200 (14)

615 (20) 596 (20)

BM

I (mean ± SD

) 27.2 ± 20.7

25.7 ± 4.9 25.4 ± 4.9

27.9 ± 7.7 28.0 ± 7.2

Died IC

U, n (%

) 302 (12.4)

179 (13.2) 225 (16.2)

546 (18.1) 498 (16.5)

Morning glucose, m

mol/l (m

ean ± SD)

7.5 (2.5) n.a.

n.a. 6.5 (1.4)

8.0 (1.4) O

verall glucose, mm

ol/l (mean ± SD

) 8.0 (2.3)

5.8 (1.3) 8.4 (1.8)

6.4 (1.0) ¶ 8.0 (1.3) ¶

95% population range (m

mol/l)

3.5 to 12.5 3.2 to 8.4

4.9 to 11.9 3.9 to 9.3

5.2 to 10.8 H

ypoglycaemia incidence (%

) 6.6

11.3 1.8

6.8 0.5

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ICU

mor

talit

y (%

)

0

5

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20

25

30

Mean glucose (mmol/l)

<= 6

,3

6,4-

6,7

6,8-

6,9

7,0-

7,2

7,3-

7,4

7,5-

7,7

7,8-

8,2

8,3-

9,0

9,1-

10,2

>= 1

0,3

Leuven IITLeuven CNICE SUGAR IITNICE SUGAR COLVG

**

* **

**

Mean glucose (mmol/l)

<= 6

,3

6,4-

6,7

6,8-

6,9

7,0-

7,2

7,3-

7,4

7,5-

7,7

7,8-

8,2

8,3-

9,0

9,1-

10,2

>=10

,3

OR

ICU

mor

talit

y

0,1

1

10

**

* *

**

*

Figure 1- ICU mortality per mean glucose stratum with the highest mortality in the lowest and highest strata. The mean glucose values and ICU mortality data of the Leuven and NICE-SUGAR IIT and C groups are plotted in the graph. ICU, Intensive Care Unit; SD, standard deviation; IIT, intensive insulin treatment; C, conventional treatment

Figure 2- odds ratio (OR) for mortality per glucose stratum with the highest OR in the lowest and highest strata. Logistic regression model adjusted for age, sex, APACHE II score and admission duration (� and > 24hr). *p<0.05, **p<0.001. ICU, Intensive Care Unit

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Mean glucose stratum (mmol/l)

<= 6

,3

6,4-

6,7

6,8-

6,9

7,0-

7,2

7,3-

7,4

7,5-

7,7

7,8-

8,2

8,3-

9,0

9,1-

10,2

>=10

,3

Hyp

ogly

cem

ia in

cide

nce

(%)

0

5

10

15

20

Hypoglycaemia

In total 161 (6.6%) patients of this cohort suffered from at least 1 hypoglycaemic

episode during ICU admission, defined as a glucose value <=2.2 mmol/l. 17.9% of all

deaths during ICU admission concerned patients who had experienced

hypoglycaemia. Regarding the lowest mean glucose stratum (mean glucose <=6.3

mmol/l), 34.6% of the patients that died at the ICU had experienced a

hypoglycaemic episode and 65.4% did not. The incidence of hypoglycaemia in the

different the mean glucose and variability cohorts is reported in Figure 3.

Figure 3-Hypoglycaemia incidence per mean glucose stratum

DISCUSSION

Mean glucose values between 7.0 and 9.0 mmol/l during ICU stay are associated

with the lowest OR for mortality at the ICU, while mean values below 7.0 and

higher than 9.0 mmol/l confer significantly higher ORÊs. These results were

attained while using a glucose algorithm that targeted for glucose values between

4.0 and 7.0 mmol/l. The finding that hyperglycaemia is associated with increased

mortality is in accordance with published literature.2 Also, the U-shaped curve we

found, with increased mortality in the lower and upper quadrants, is described

earlier in patients with myocardial infarction during admission17-19, in the ICU

setting for the first 24 hours of admission20 and more generally in patients with

type 2 diabetes mellitus21, corroborating this finding.

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Hypoglycaemia seems to be associated with increased mortality at the ICU.22-25 In

our population the incidence of hypoglycaemia was highest in the lowest mean

glucose cohorts in which mortality was higher as well. In addition, a significant

part of the patients who died had experienced a hypoglycaemic episode. However,

hypoglycaemia can only partially account for the considerable mortality rate in the

lowest mean glucose stratum since 65.4% of the non-survivors did not experience

hypoglycaemia. However, it might be possible that some hypoglycaemic episodes

were not recorded due to intermittent sampling or were underestimated due to the

AccuChek point-of-care meter used for glucose measurements, results of which tend

to be higher than those obtained from the laboratory.26;27 Also, low blood sugars are

perhaps already harmful above the current cut-off value of 2.2 mmol/l. Therefore

the contribution of hypoglycaemia to ICU death could be underestimated and needs

further research using continuous glucose measurement.

We plotted the reported NICE-SUGAR and combined Leuven mean glucose data in

Figure 1. This shows that the findings of the NICE-SUGAR trial are in accordance

with the mortality data from our cohort; the mean glucose of their IIT group is

according to our data also associated with a higher ICU mortality rate than the

conventionally treated group. This is in contrast with data of the combined Leuven

studies (Figure 1). According to our U-curve the mean glucose value achieved in the

combined Leuven conventionally treated group would correspond with a lower

mortality rate than the mean glucose achieved in their intensively treated group,

which is opposed to their findings. Since the mean mortality and mean glucose

values of the conventionally treated groups of both the combined Leuven and

NICE-SUGAR studies are roughly comparable, it seems that the mortality rate of

the combined Leuven IIT group is lower than expected. A possible explanation for

the low mortality of the Leuven IIT group might be lower glucose variability. In

addition to hypoglycaemia, glycaemic swings are a known risk factor of ICU

death.4;5 Also after an attempt for correction using severity of disease measures

glucose variability remains an independent predictor of mortality in the OLVG

population.28 In addition, other explanations have been proposed to explain the

diverging outcomes of Leuven and NICE-SUGAR.29

Baseline characteristics from our sample and the combined Leuven and NICE-

SUGAR studies were reasonably comparable, only the proportion of patients with

diabetes mellitus in our sample is smaller, 7.4% versus 14.5 and 20.0% in the

combined Leuven and NICE-SUGAR studies respectively). This may be due to a

difference in definition since in our population diabetes mellitus was only scored

when the patient used anti-hyperglycaemic drugs, probably leading to an

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underestimation of the proportion of patients with diabetes mellitus in our

population. The APACHE II score at admission was lower in the Leuven studies,

which might be due to a larger proportion of (cardiothoracic) surgery patients, of

whom on average APACHE II score and mortality rate were lower than in medical

patients.6;7

The mean glucose of the OLVG population (8.0 mmol/l) was higher than the target

range, between 4.0 and 7.0 mmol/l. Other studies of intensive insulin therapy also

did not reach their target range, illustrating the difficult implementation of this

therapy.9;11;12 Also short ICU admission duration in the OLVG population makes it

more difficult to reach the glucose target because glucose values are higher and

have a wider range in the first 24 hours of admission (median duration of stay 1.5

days in our cohort compared to 3 days in the Leuven cohort and 4.2 days 'on

algorithm' in the NICE-SUGAR study). Furthermore, our patients were treated in a

normal-care setting without the extra stimuli of a trial setting to achieve the target.

Mean glucose does not equal time in target range, as the protocol requires more

frequent sampling when not in target, thus falsely inflating the mean.

The rate of hypoglycaemia in our population was comparable with the NICE-

SUGAR study intensively treated group despite a higher mean glucose in our

population. Lack of a study effect and perhaps a different sampling frequency

might also have contributed to this observation30. Moreover, patients with a

medical admission diagnosis experience more hypoglycaemic events.25 Taking that

into account, other studies implementing intensive insulin therapy observed

comparable rates of severe hypoglycaemia in their intensively treated groups

compared to our population.9;11

In our logistic regression model we adjusted for severity of disease and admission

duration less or more than 24 hours, since both high and low glucose levels could be

a manifestation of severe disease rather than a cause. Glucose values are higher

and have a wider range in the first 24 hours of admission, biasing the patients with

longer admission times and corresponding lower mean glucose values. A limitation

of our correction for severity of disease is the use of the APACHE II score, because

the use of APACHE II to predict mortality is not validated for cardiothoracic

surgery patients. However, this adjustment is the best available method.31

CONCLUSIONS

According to our data the mean glucose during ICU stay is related to mortality by a

U-shaped curve; a Âsafe rangeÊ for mean glucose can be defined between 7.0 and 9.0

mmol/l, while both higher and lower mean values are associated with higher

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mortality. Secondly, comparison of the combined Leuven, NICE SUGAR and our

cohorts learns that the increased mortality in the IIT group of NICE-SUGAR is in

line with our U-shaped curve. Low glucose variability may have contributed to their

corresponding low mortality. According to these findings and awaiting further

studies we recommend treating hyperglycaemia at the ICU in a moderately

intensive way, targeting for mean glucose values between 7.0 and 9.0 mmol/l and

avoiding hypoglycaemia. This Âsafe rangeÊ should be studied prospectively in

randomised clinical trials.

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REFERENCES

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An Independent Marker of In-Hospital Mortality in Patients with Undiagnosed Diabetes.

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(3) Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris J, May AK. Blood Glucose Variability

Is Associated with Mortality in the Surgical Intensive Care Unit. American Surgeon 2008;

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(4) Egi M, Bellomo R, Stachowski E, French C, Hart G. Variability of blood glucose

concentration and short-term mortality in critically ill patients. Anesthesiology 2006;

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(5) Krinsley J. Glycemic variability: A strong independent predictor of mortality in critical ill

patients. Crit Care Med 2008; 36(11):3008-3013.

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(8) Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F et al.

Intensive Insulin Therapy in Mixed Medical/Surgical Intensive Care Units. Diabetes

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(10) Devos P, Preiser JC, Melot C. Impact of tight glucose control by intensive insulin therapy

on ICU mortality and the rate of hypoglycemia: final results of the Glucontrol study.

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(11) Preiser JC, Devos P, Ruiz-Santana S, Melot C, Annane D, Groeneveld J et al. A

prospective randomised multi-centre controlled trial on tight glucose control by intensive

insulin therapy in adult intensive care units: the Glucontrol study . Intensive Care

Medicine 2009; E-pub 28 july.

(12) The NICE-SUGAR Study Investigators. Intensive versus Conventional Glucose Control

in Critically Ill Patients. N Engl J Med 2009; 360(13):1283-1297.

(13) Griesdale DEG, de Souza RJ, van Dam RM, Heyland DK, Cook DJ, Malhotra A et al.

Intensive insulin therapy and mortality among critically ill patients: a meta-analysis

including NICE-SUGAR study data. CMAJ: Canadian Medical Association Journal 2009;

180(8):821-827.

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(14) Rood E, Bosman R, van der Spoel J, Taylor P, Zandstra D. Use of a computerized

guideline for glucose regulation in the Intensive Care Unit improved both guideline

adherence and glucose regulation. Journal of the American Medical Informatics

Association 2005; 12(2):172-180.

(15) Ferreira FL, Bota DP, Bross A, Melot C, Vincent JL. Serial Evaluation of the SOFA Score

to Predict Outcome in Critically Ill Patients. JAMA 2001; 286(14):1754-1758.

(16) Knaus W, Draper E, Wagner D, Zimmerman J. APACHE II: A severity of disease

classification system. Critical Care Medicine 1985; 13(10):818-829.

(17) Kosiborod M, Inzucchi SE, Krumholz HM, Xiao L, Jones PG, Fiske S et al. Glucometrics

in Patients Hospitalized With Acute Myocardial Infarction: Defining the Optimal

Outcomes-Based Measure of Risk. Circulation 2008; 117(8):1018-1027.

(18) Pinto DS, Skolnick AH, Kirtane AJ, Murphy SA, Barron HV, Giugliano RP et al. U-

Shaped Relationship of Blood Glucose With Adverse Outcomes Among Patients With ST-

Segment Elevation Myocardial Infarction. Journal of the American College of Cardiology

2005; 46(1):178-180.

(19) Pinto DS, Kirtane AJ, Pride YB, Murphy SA, Sabatine MS, Cannon CP et al. Association

of Blood Glucose With Angiographic and Clinical Outcomes Among Patients With ST-

Segment Elevation Myocardial Infarction (from the CLARITY-TIMI-28 Study). The

American Journal of Cardiology 2008; 101(3):303-307.

(20) Bagshaw S, Egi M, George C, Bellomo R. Early blood glucose control and mortality in

critically ill patients in Australia. Crit Care Med 2009; 37(2):463-470.

(21) Currie C, Peters J, Tynan A, Evans M, Heine R, Bracco O et al. Survival as a function of

HbA1c in people with type 2 diabetes: a retrospective cohort study. The Lancet 2010;

375:481-489.

(22) Bagshaw S, Bellomo R, Jacka M, Egi M, Hart G, George C et al. The impact of early

hypoglycemia and blood glucose variability on outcome in critical illness. Critical Care

2009; 13(3):R91.

(23) Krinsley JS, Grover A. Severe hypoglycemia in critically ill patients: Risk factors and

outcomes. Critical Care Medicine 2007; 35(10):2262-2267.

(24) Vriesendorp TM, Devries JH, van SS, Moeniralam HS, de JE, Roos YB et al. Evaluation

of short-term consequences of hypoglycemia in an intensive care unit. Crit Care Med

2006; 34(11):2714-2718.

(25) Hoekstra J, Hermanides J, Bosman R, Vriesendorp T, Dotsch R, Rosendaal FR et al.

Hypoglycaemia is related to mortality in the ICU. Diabetologia 2009; 52: Suppl1:S237

(40-PS), Abstract.

(26) Hoedemaekers CWE, Klein Gunnewiek JMT, Prinsen MA, Willems JL, Van der Hoeven

JG. Accuracy of bedside glucose measurement from three glucometers in critically ill

patients. Critical Care Medicine 2008; 36(11).

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of Roche Accu-Chek Inform Whole Blood Capillary, Arterial, and Venous Glucose Values

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in Patients Receiving Intensive Intravenous Insulin Therapy After Cardiac Surgery.

American Journal of Clinical Pathology 2007; 127(6):919-926.

(28) Hermanides J, Vriesendorp T, Bosman R, Zandstra D, Hoekstra J, DeVries J. Glucose

variability predicts mortality in the ICU. Crit Care Med 2010 Mar;38(3):838-42.

(29) Van den Berghe G, Schetz M, Vlasselaers D, Hermans G, Wilmer A, Bouillon R et al.

Intensive insulin therapy in critically ill patients: NICE-SUGAR or Leuven blood glucose

target? J Clin Endocrinol Metab 2009; 94(9):3163-3170.

(30) Van den Berghe G. How to compare adequacy of algorithms to control blood glucose in the

intensive care unit? Critical Care 2004; 8(3):151-152.

(31) Kramer AA, Zimmerman JE. Predicting Outcomes for Cardiac Surgery Patients After

Intensive Care Unit Admission. Seminars in Cardiothoracic and Vascular Anesthesia

2008; 12(3):175-183.

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Summary and future considerations

Samenvatting en toekomstperspectief

List of Publications

Biografie

Co-authors

Dankwoord

J Hermanides

Summary and future considerations

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What are the consequences of hyperglycaemia, how do we measure it and what

measures do we need to take? These questions were the base of this thesis. In

PART I, the consequences of hyperglycaemia are investigated in relation to the

coagulation system and thromboembolism. We especially focused on the effects of

acute hyperglycaemia resulting from surgery in patients with and without diabetes

mellitus. PART II of the thesis is about the detection and treatment of

hyperglycaemia. The sensor augmented pump therapy as a new detection and

treatment tool is the subject of several chapters. The final chapters concern the

treatment of hyperglycaemia in the Intensive Care Unit (ICU).

PART I

Chapter 2 gives an overview of the current evidence of the role of hyperglycaemia

in activating the coagulation system. Evidence is mounting that both chronic and

acute hyperglycaemia contribute to coagulation activation and hypofibrinolysis,

resulting in a procoagulant state that predisposes patients to thrombotic events.

Intensive glycaemic control in patients with diabetes mellitus reduces the incidence

of thrombotic diseases such as myocardial infarction and stroke. Whether intensive

glucose control during acute hyperglycaemia will be beneficial needs further

investigation in randomised controlled trials.

Chapter 3 confirms the association between hyperglycaemia and coagulation: a

cohort of consecutive patients referred for suspected deep vein thrombosis was

investigated. 188 cases with confirmed venous thromboembolism (VTE) were

compared to 370 controls. Increased glucose levels at presentation in the outpatient

clinic were associated with increased odds ratios for actually having VTE. This is

the first report on this association and now confirmation in other studies is

required.

In CChapter 4 the effect of hip surgery on glucose metabolism and coagulation

activation was studied in 9 patients without diabetes mellitus undergoing elective

hip surgery. Hip surgery clearly induced a rise in glucose levels, which preceded a

proportional rise of factor VIII clotting activity, von Willebrand ristocetin cofactor

activity, von Willebrand factor antigen and prothrombin fragment 1+2. These

results suggests a possible role of hyperglycaemia in activating the coagulation

system.

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Chapter 5 supports the suggestion we made in chapter 4: increased glucose levels

after elective hip surgery were predictive of later development of venous

thromboembolism. We performed post-hoc analyses of four previously performed

studies investigating patients undergoing total hip or knee replacement. Our

outcome measures were symptomatic VTE and „total VTE‰ (defined as the

composite of symptomatic VTE, asymptomatic DVT and all cause mortality). We

analysed 12383 patients who underwent elective hip- and knee-surgery.

Postoperative glucose levels measured at day 1 were associated with total VTE in

patients undergoing hip surgery. Also, the increase of glucose levels during hip

surgery was associated with the development of symptomatic and total VTE. This

was not demonstrated in patients undergoing total knee replacement, which is

likely due to the surgical procedure. This indicates that hyperglycaemia develops as

a consequence of hip surgery and contributes to the development of post-surgical

VTE.

In CChapter 6 a different patient group was studied: 330 consecutive patients

undergoing pancreatoduodenectomy. Glucose was measured before-, during-, and

after surgery. In concordance with previous studies, early postoperative

hyperglycaemia was induced by the procedure and appears to be a strong predictor

of postoperative complications.

All of the previous studies show that acute hyperglycaemia occurs even without

known diabetes preticipated by a pathophysiological stressor, such as surgery or

illness. Postoperative hyperglycaemia is associated with VTE after hip surgery and

with complications after pancreatodoudenectomy. Future studies should focus on

preventing the development of perioperative hyperglycaemia and study the effect in

randomised controlled setting. As intensive insulin therapy can be time consuming

and carries the risk of hypoglycaemia, the use other glucose lowering agents should

be considered, such as oral antidiabetics or GLP-1 analogs.

PART II

Chapter 7 provides a view on the current evidence of the clinical application of

subcutaneous glucose sensors (CGM), alone or combined with subcutaneous insulin

pumps. In several studies, CGM lowered HbA1c in adult patients with poorly

controlled type 1 diabetes mellitus, when selecting compliant patients who tolerate

the device. However, as a preventive tool for hypoglycaemia, CGM is not yet

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effective. Increasing reimbursement of CGM is expected in the near future and

studies on cost-effectiveness are awaited.

Chapter 8 describes the EURYTHMICS trial (EURopean trial (Y) To lower HbA1c

by Means of an Insulin pump augmented by a Continuous glucose Sensor). In a

randomised controlled trial in 8 European countries, the efficacy of sensor

augmented pump therapy was investigated in 83 patients with type 1 diabetes

mellitus and HbA1c >= 8.2%. Patients were randomised to either sensor augmented

pump with mealtime bolus advisor for 26 weeks or to the control group using

multiple daily injection therapy. We showed that sensor augmented pump therapy

effectively lowered HbA1c by 1.21% in suboptimally controlled type 1 diabetes

patients. The magnitude of the improvement in glycaemic control suggests an

additive effect of the insulin pump, mealtime insulin dose advisor and CGM.

The use of sensor augmented pump therapy was also studied in CChapter 9.

However, in this randomised controlled pilot trial, we investigated the efficacy of

the therapy in patients with hyperglycaemia (>7.7 mmol/l) admitted to the coronary

care unit (CCU) for acute myocardial infarction. Hyperglycaemia is a known risk

factor for mortality after myocardial infarction, but previous trials were not able to

lower the glucose in this patient group. Therefore we tested the sensor augmented

pump therapy. 20 Patients were randomised to either sensor augmented pump

therapy for 48 hours or standard care. Although effective in normalising glucose

levels, sensor augmented pump therapy was associated with increased

hypoglycaemia and workload for the nursing staff due to false positive alarms.

Improvement of the device is awaited before continuing with large scale trials.

In CChapter 10 a part of the development of sensor augmented pump therapy

towards the artificial pancreas was studied. As two functions, insulin delivery and

glucose sensing, are combined, one would like to physically integrate insulin

delivery and glucose sensing as this would minimise the discomfort for the patient

and increase the usability. Therefore we investigated the hypothesis that high local

insulin concentrations would interfere with sensor readings in 10 type 1 diabetes

patients using microdialyses CGM. We concluded that microdialysis CGM could be

accurately performed at a mean distance of 0.9 cm from an insulin pump system in

the normo- and hyperglycaemic ranges, and probably the hypoglycaemic range,

during rapid rise and fall of blood glucose. This has important consequences for the

development of a closed-loop system or artificial pancreas.

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The development of the closed-loop system was further investigated in CChapter 11.

To optimally integrate insulin delivery and glucose sensing, communication via an

algorithm is essential. However, an effective algorithm has proven difficult to

develop. In this pilot study we have investigated the effectiveness of an algorithm

to treat postprandial glucose excursions in a closed-loop format. Indeed, the

algorithm is as effective as standard care, and may even become superior, which we

hope to demonstrate in a subsequent study.

In CChapter 12 hyperglycaemia is approached from a different perspective. Of

course, hyperglycaemia is not a constant phenomenon, but it will vary over time.

This will be even more pronounced when treatment of hyperglycaemia is initiated.

Earlier studies already suggested that this glucose variation may be harmful by

itself. Because variation of glucose is always associated with the mean glucose, it is

difficult to distinguish between these two. In this study, we investigated the

association of glucose variability, using a newly developed measure of glycaemic

variability, and mortality among 5728 patients admitted to the ICU. We found that

high glucose variability was strongly associated with ICU and in-hospital death.

High glucose variability combined with high mean glucose values was associated

with highest ICU mortality. Low glucose variability seemed protective, even when

mean glucose levels were elevated.

Every upside has its downside, as we show in CChapter 13. The downside of treating

patients for hyperglycaemia in the ICU is that you will increase the risk of

hypoglycaemia. Whether or not this is harmful is being debated and therefore we

investigated the association between hypoglycaemia and mortality in the ICU. All

first episodes of hypoglycaemia (glucose <=2.5 mmol/l) were derived from 154015

glucose values in 5961 patients admitted to the ICU. Patients were considered to be

exposed to hypoglycaemia from the event until the end of ICU admittance.

Hypoglycaemia was indeed related to ICU mortality, also when adjusted for a daily

adjudicated measure of disease severity, indicating a causal relationship.

Finally in CChapter 14 we try to put the most important recent evidence of the

NICE-SUGAR and Leuven studies in perspective, thereby including our data from

an ICU population treated according to the most recent guidelines. Mean glucose

during ICU admission turned out to be related to mortality by a U-shaped curve. A

Âsafe rangeÊ of glucose regulation in our population could be defined between 7.0

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and 9.0 mmol/l. The U-shaped curve may help to explain the increased mortality in

the intensively treated group of the NICE-SUGAR study but also the higher

mortality in the conventionally treated groups of the Leuven studies.

So, what should be done in the future? The application of sensor augmented pump

therapy for poorly regulated patients with diabetes mellitus type 1 has been proven

to be effective in lowering HbA1c, but not in preventing hypoglycaemia. However,

no trial so far has been designed for sensor augmented pump therapy with (severe)

hypoglycaemia as the primary outcome measure. Such a trial is expected and

warranted in the near future. The development of the closed-loop or artificial

pancreas is ongoing and future projects will hopefully bring this holy grail a little

bit closer by improving and designing algorithms for the communication between

glucose sensor and insulin pump. The use of sensor augmented pump therapy in a

(acute) clinical setting needs improvement, as shown in chapter 9. It could be a

most useful tool, as continuous glucose measurements, when accurate, should be

able to prevent hypoglycaemia, decrease nursing workload and capture glucose

variability. It should be realised that at a completely different patient group as

compared to patients with type 1 diabetes mellitus. Patients in an ICU or CCU

have other major problems next to their hyperglycaemia, which could influence the

accuracy of the glucose sensors. When writing this thesis, the TOPDOGS study is

being finalised. In this study factors influencing the effectiveness and accuracy of

glucose sensors in the ICU is being investigated in a large cohort and the results

are awaited eagerly. Finally, a pooled analysis of the NICE-SUGAR and Leuven

data is expected to provide treatment guidelines for treating hyperglycaemia,

glucose variability and avoiding hypoglycaemia in the ICU. For now, the avoidance

of hypoglycaemia by targeting for a safe-range seems advisable, considering the U-

shaped mortality curve.

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Het onderwerp van dit proefschrift is hyperglykemie: een verhoging van het

glucosegehalte in het bloed. De meest voorkomende oorzaak van hyperglykemie is

de ziekte diabetes mellitus. Sinds de ontdekking van insuline is de

levensverwachting van patiënten met diabetes mellitus enorm gestegen. Het blijft

echter een aandoening waarbij patiënten rekening moeten houden met ernstige

complicaties van de ziekte en een levenslange en hinderlijke behandeling.

Een minder bekende oorzaak van hyperglykemie is fysieke stress. Door

bijvoorbeeld een operatie of een hartinfarct kan een dusdanige acute metabole

ontregeling ontstaan dat het glucose stijgt. Waar men vroeger dacht dat deze

tijdelijke hyperglykemie gunstig zou kunnen zijn voor het herstel, wordt het steeds

duidelijker dat het eigenlijk zeer nadelig is voor de patiënt.

In DEEL I van dit proefschrift worden de gevolgen van hyperglykemie onderzocht,

vooral in relatie tot het stollingssyteem en het optreden van trombose. Daarbij

hebben we ons in het bijzonder gericht op de gevolgen van acute hyperglykemie,

uitgelokt door een operatie. DEEL II van dit proefschrift gaat over het detecteren

en behandelen van hyperglykemie. Tot op heden is het gebruikelijk dat de patiënt

met diabetes een aantal maal per dag met een vingerprik in het bloed de hoogte

van het glucose meet en daarop insuline spuit. Door de technische vooruitgang is

het inmiddels mogelijk om met behulp van sensoren in de buikhuid continu het

suikergehalte te meten en ook continu kleine hoeveelheden insuline middels een

pomp af te geven. De effectiviteit van deze nieuwe behandelingstechniek hebben we

onderzocht bij patiënten met diabetes mellitus en patiënten met hyperglykemie

tijdens een hartinfarct. Tevens wordt geanalyseerd hoe deze nieuwe

behandelingstechniek verder is te verbeteren. In de laatste hoofdstukken komt het

detecteren van hyperglykemie op de Intensive Care (IC) aan bod en de nadelen van

behandeling met insuline in deze situatie.

DEEL I

In HHoofdstuk 2 geeft een overzicht van de huidige literatuur met betrekking tot de

rol van hyperglykemie in het stollingssysteem. Er is overtuigend bewijs dat zowel

chronische als acute hyperglykemie het stollingssyteem activeren en de afbraak

van een stolsel vertragen. Dit leidt tot een verhoogd risico op trombose. Het

behandelen van hyperglykemie bij patiënten met diabetes mellitus blijkt het risico

op trombotische aandoeningen, zoals een hersen- of hartinfarct, te verlagen. Echter,

het effect van de behandeling van acute hyperglykemie is nog niet voldoende

onderzocht in interventie trials.

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Hoofdstuk 3 bevestigt het verband tussen hyperglykemie en stolling in een groep

patiënten die werd onderzocht op de polikliniek op verdenking van een diep

veneuze trombose in het been. Van deze personen hadden er 188 daadwerkelijk een

trombose in het been. De controlegroep bestond uit 370 mensen die geen trombose

bleken te hebben. Hoe hoger het glucose was bij presentatie op de polikliniek, hoe

groter de kans was dat de patiënt inderdaad een trombosebeen had. Dit is de eerste

keer dat de relatie tussen een trombosebeen en oplopend glucose is aangetoond en

vervolgstudies zijn nodig ter bevestiging.

Het gevolgen van een heupoperatie voor het glucose metabolisme en de

stollingsactivatie werden bestudeerd in HHoofdstuk 4. In een groep van 9 patiënten

zonder diabetes mellitus werd het glucose bepaald voor, tijdens en na de operatie.

Bovendien werden op dezelfde tijdstippen de stollingsfactoren factor VIII, von

Willibrand ristocetine cofactor activiteit, von Willibrand factor antigeen en

prothrombine fragment 1+2 bepaald. Door de heupoperatie steeg het

plasmaglucose, wat werd gevolgd door een vergelijkbare stijging van de

stollingsfactoren. Deze resultaten suggereren een mogelijke rol voor glucose bij het

activeren van het stollingssysteem.

Hoofdstuk 5 bevestigt de suggestie die we hebben gedaan in hoofdstuk 4: verhoogde

glucosewaarden na een heupoperatie voorspellen de latere ontwikkeling van

veneuze trombose. Dit bleek uit een post-hoc analyse van 4 grote studies die waren

uitgevoerd bij 12383 patiënten die electieve heup- of knieoperaties ondergingen. De

belangrijkste uitkomstmaat was symptomatische veneuze trombose en „totale

veneuze trombose‰ (gecombineerde uitkomstmaat van symptomatische en

asymptomatische trombose met overlijden door welke oorzaak dan ook). Op dag één

na de operatie bleken de gemiddelde glucosewaarden in de patiëntengroepen

gestegen in vergelijking met preoperatieve waarden. Bij patiënten die een

heupoperatie ondergingen was deze verhoging geassocieerd met een verhoogd risico

op het ontwikkelen van symptomatische en „totale veneuze trombose‰. Dit werd

niet aangetoond bij patiënten die een knieoperatie ondergingen, wat mogelijk

verklaard wordt door het verschil in de chirurgische procedure. Deze resultaten

geven aan dat hyperglykemie kan ontstaan als gevolg van orthopedische chirurgie

en bij heupoperaties ook bijdraagt aan de ontwikkeling van postoperatieve veneuze

trombose.

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In HHoofdstuk 6 werd een andere patiëntengroep bestudeerd: bij 330 patiënten die

een pancreatoduodenectomie ondergingen werd het glucose gemeten voor, tijdens

en na de operatie. Ten gevolge van de operatie ontstond hyperglykemie en deze

bleek een sterke voorspeller voor postoperatieve complicaties.

Uit al deze studies komt naar voren dat acute hyperglykemie kan worden

veroorzaakt door pathofysiologische stress, zoals een operatie, ook zonder dat de

patiënt diabetes mellitus heeft. Postoperatieve hyperglykemie blijkt geassocieerd

met veneuze trombose na heupoperaties en met complicaties na een

pancreatoduodenectomie. De logische volgende stap lijkt de preventie te zijn van

perioperatieve hyperglykemie en het effect daarvan bestuderen in gerandomiseerde

studies. Omdat de toepassing van intensieve insuline therapie peroperatief

tijdrovend is en gepaard gaat met een verhoogd risico op hypoglykemie, kan ook

gebruik van andere glucoseverlagende medicatie dan insuline worden overwogen.

Te denken valt aan bepaalde orale medicatie en GLP-1 analoga.

DEEL II

Hoofdstuk 7 wordt de huidige stand van zaken met betrekking tot de continue

subcutane glucose sensoren, al dan niet in combinatie met de insulinepomp

besproken. Uit meerdere studies blijkt inmiddels dat met behulp van continue

glucose sensoren het HbA1c verlaagd kan worden bij volwassen, slecht gereguleerde

maar gemotiveerde patiënten met type 1 diabetes, mits zij het apparaat verdragen.

Het nut van het gebruik van sensoren ter preventie van hypoglykemie is nog niet

bewezen. Hoewel een analyse van de kosteneffectiviteit nog moet worden

uitgevoerd, is de verwachting dat sensoren in toenemende mate zullen worden

vergoed door verzekeraars.

In Hoofdstuk 8 worden de resultaten van de EURYTHMICS trial (EURopean (Y)

trial To lower HbA1c by Means of an Insulin pump augmented by a Continuous

glucose Sensor) gepresenteerd. De effectiviteit van behandeling van type 1 diabetes

met een combinatie van glucose sensor en insulinepomp werd onderzocht in een

gerandomiseerde studie in 8 Europese landen bij 83 patiënten met een HbA1c van

8.2% of hoger. De patiënten werden gerandomiseerd naar 26 weken behandeling

met sensor gecombineerd met pomp voorzien van een maaltijd bolus-calculator of

naar continueren van de behandeling met meerdaagse insuline injecties. We

toonden aan dat therapie met sensor en pomp het HbA1c verlaagt met 1.21%. Deze

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mate van HbA1c verlaging suggereert een gecombineerd effect van de glucose

sensor, de insulinepomp en de bolus-calculator.

Ook in HHoofdstuk 9 werd pomp therapie in combinatie met glucose sensor

onderzocht in een gerandomiseerde studie. Echter, nu werden 20 patiënten die

werden opgenomen op de hartbewaking met een acuut hartinfarct en een verhoogd

glucose (>7.7 mmol/l) gerandomiseerd naar behandeling met sensor gecombineerde

pomptherapie of conventionele behandeling gedurende 48 uur. Eerdere

onderzoeken slaagden er niet in om glucose effectief te verlagen in deze

patiëntengroep, terwijl hyperglykemie een bekende risicofactor voor overlijden is.

Met therapie met sensor en pomp bleek het inderdaad wel mogelijk om het glucose

te verlagen, echter dit ging gepaard met een toename van hypoglykemie en

werkdruk voor het verpleegkundig personeel. Voordat grotere gerandomiseerde

studies worden opgezet, lijken verbeteringen aan de apparatuur gewenst.

Het verder integreren van de glucose sensor en de insulinepomp is onderzocht in

Hoofdstuk 10. Dit moet uiteindelijk leiden tot de ontwikkeling van een kunstmatige

pancreas. In dit hoofdstuk is onderzocht of het mogelijk is insuline afgifte en

glucosemetingen op dezelfde plaats in het buikvet te verrichten. Dit zou de weg

vrijmaken naar de ontwikkeling van één naald waardmee beide functies kunnen

worden uitgevoerd, wat het ongemak voor de patiënt belangrijk zou verminderen.

Bij 10 patiënten met type 1 diabetes onderzochten we de invloed van hoge

concentraties insuline zo dicht mogelijk bij een continue microdialyse glucose

sensor. Het bleek dat de sensor nauwkeurig blijft meten op een gemiddelde afstand

van 0.9 cm vanaf de injectieplaats ven de insulinepomp. Zowel bij hoge als normale

glucosewaarden bleven de metingen nauwkeurig en dit leek ook het geval in het

hypoglykeme gebied. Ook tijdens het snel stijgen en dalen van glucose waren de

metingen van de sensor niet minder nauwkeurig. Deze resultaten hebben

belangrijke consequenties voor het verder ontwikkelen van de kunstmatige

pancreas.

Ook Hoofdstuk 11 heeft betrekking op de ontwikkeling van een kunstmatige

pancreas. Om insuline afgifte te laten plaatsvinden op basis van gemeten continue

sensor waarden is communicatie tussen deze twee functies via een algoritme

essentieel. De ontwikkeling van een dergelijk algoritme is een uitdaging gebleken.

In dit onderzoek is de effectiviteit van een algoritme om postprandiale glucose

excursies te behandelen onderzocht in een gesloten systeem, dus zonder interventie

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van de arts of patiënt. Het algoritme bleek even effectief als de standaard

behandeling, wellicht zelfs iets beter, en een vervolgstudie is inmiddels gaande.

In HHoofdstuk 12 bekijken we hyperglykemie vanuit een andere invalshoek.

Hyperglykemie is geen constant fenomeen, maar de glucosewaarden zullen

variëren over de tijd. Ook behandeling van hyperglykemie heeft invloed op de

variabiliteit van glucose. Uit eerder onderzoek is gebleken dat deze glucose variatie

op zichzelf ook schadelijk kan zijn. Omdat de mate van variatie samenhangt met

het gemiddelde bloedglucose is het lastig om de gevolgen van beide fenomenen

apart te onderzoeken. In dit hoofdstuk hebben we daarom een nieuwe maat voor

glucose variabiliteit ontwikkeld. Bij 5728 patiënten is onderzocht wat de associatie

is tussen glucose variabiliteit en mortaliteit op de IC. Een verhoogde glucose

variabiliteit bleek duidelijk geassocieerd met een toename van het aantal patiënten

dat overleed. De combinatie van een hoog gemiddeld glucose tijdens opname en

hoge variabiliteit bracht het hoogste sterftecijfer met zich mee. Een lage glucose

variabiliteit leek echter te beschermen tegen overlijden.

Elke behandeling is geassocieerd met bijwerkingen, zoals is te lezen in HHoofdstuk

13. De standaard behandeling van hyperglykemie op de IC is insulinetherapie,

waarmee het risico op hypoglykemie toeneemt. In de huidige literatuur bestaat

verschil van mening over de vraag of deze hypoglykemie schadelijk is. Daarom

onderzochten wij de relatie tussen hypoglykemie op de IC en overlijden. Alle eerste

episodes van een glucose <= 2.5 mmol/l werden onderzocht in een groep van 5961

IC patiënten. Patiënten werden beschouwd als zijnde blootgesteld aan

hypoglykemie vanaf het moment van de episode tot aan het einde van de IC

opname. Er bleek inderdaad een duidelijke relatie te bestaan tussen hypoglykemie

en sterfte op de IC, ook na correctie voor ernst van ziekte van de patiënt. Dit

suggereert een causaal verband.

Tot slot hebben we in HHoofdstuk 14 geprobeerd de twee belangrijkste IC trials

(NICE-SUGAR en Leuven) naar de effectiviteit van insuline therapie op de IC in

perspectief te plaatsen met behulp van de data uit een IC populatie. Het

gemiddelde glucose tijdens IC opnames blijkt zich te verhouden tot sterfte op de IC

via een U-curve. De laagste sterfte, ofwel het veilige gebied, bevindt zich in de

bestudeerde populatie tussen de 7.0 en de 9.0 mmol/l. Deze U-curve kan wellicht de

interpretatie van zowel het hoge sterftecijfer in de intensief behandelde groep van

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de NICE-SUGAR studie, alsook de hogere sterfte in de conventioneel behandelde

groepen van de Leuven studies vergemakkelijken.

Wat kunnen we nu in de nabije toekomst verwachten aan verdere ontwikkelingen

en studies? De toepassing van pomptherapie gecombineerde met sensor bij slecht

gereguleerd patiënten met type 1 diabetes is bewezen effectief ter verlaging van het

HbA1c, maar niet bij het voorkomen van hypoglykemie. Er zijn echter nog geen

gerandomiseerde studies gepubliceerd waarin het voorkomen van hypoglykemie bij

het gebruik van glucose sensoren specifiek is onderzocht. Het is belangrijk dat een

dergelijke studie gaat worden uitgevoerd.

De ontwikkeling van de kunstmatige pancreas is nu volop gaande en vooral de

verbeterde algoritmes om de glucose sensor en insulinepomp met elkaar te laten

communiceren zijn veelbelovend. Bij het gebruik van de glucose sensor

gecombineerd met insulinepomp in de acute klinische setting is, zoals wij hebben

laten zien, nog veel ruimte voor verbetering. Het is in potentie een nuttig apparaat

aangezien de continue glucose metingen, indien voldoende nauwkeurig

geregistreerd, hypoglykemie en glucose variabiliteit kunnen helpen voorkomen met

daarbij een vermindering van de werkdruk van het verpleegkundig personeel. Men

moet zich echter wel realiseren dat het hier gaat om een totaal andere

patiëntengroep dan de patiëntengroep met type 1 diabetes. Patiënten op de

hartbewaking en de IC hebben naast hun hyperglykemie andere pathologie, die

direct de werking van de glucosesensoren kan beïnvloeden. Terwijl dit proefschrift

wordt geschreven, wordt ook de laatste hand gelegd aan een studie waarin een

aantal bijkomende factoren op de IC worden onderzocht, die de nauwkeurigheid en

effectiviteit van glucose sensoren zouden kunnen beïnvloeden, zoals verminderde

weefselpersfusie en temperatuurschommelingen (de TOPDOGS studie). Tenslotte

wordt een gezamenlijke analyse verwacht van de NICE-SUGAR en Leuven data,

Deze analyse kan vermoedelijk leiden tot het vaststellen van definitieve richtlijnen

voor de behandeling van hyperglykemie en de preventie van hypoglykemie en

glucose variabiliteit. Op dit moment lijkt het, gezien de U-vormige sterfte curve,

verstandig om hypoglykemie in ieder geval te vermijden en dus een veilige glucose

range aan te houden bij patiënten op de IC.

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List of Publications

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Hermanides J, Bosman RJ, Vriesendorp TM, Dotsch R, Rosendaal FR, Zandstra DF; Hoekstra JBL, DeVries JH. Hypoglycemia is associated with ICU mortality. Critical Care Medicine 2010 In Press Hermanides J¹, Engström AE², Wentholt IME¹, Sjauw KD², Hoekstra JBL¹, Henriques JPS², DeVries JH. Sensor augmented insulin pump therapy to treat hyperglycemia at the coronary care unit; a randomized clinical pilot trial. Diabetes Technoly & Therapeutics. 2010 In Press Hermanides J, Devries JH. Sense and nonsense in sensors. Diabetologia. 2010 Apr;53:593-596. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JBL, Devries JH. Glucose variability is associated with intensive care unit mortality. Critical Care Medicine. 2010 Mar;38(3):838-42. Hermanides J, Huijgen R, Henny CP, Mohammad NH, Hoekstra JBL, Levi MM, DeVries JH. Hip surgery sequentially induces stress hyperglycaemia and activates coagulation. The Netherlands Journal of Medicine. 2009 Jun;67(6):226-9. Hermanides J, Hoekstra JBL. Oral glucose tolerance test. Not useful in screening of type 2 diabetes. Nederlands Tijdschrift voor Geneeskunde. 2009 Apr 18;153(16):743. Hermanides J, Cohn DM, Devries JH, Kamphuisen PW, Huijgen R, Meijers JC, Hoekstra JBL, Büller HR. Venous thrombosis is associated with hyperglycemia at diagnosis: a case-control study. Journal of Thrombosis and Haemostasis. 2009 Jun;7(6):945-9. Verhagen DW, Hermanides J, Korevaar JC, Bossuyt PM, van den Brink RB, Speelman P, van der Meer JT. Health-related quality of life and posttraumatic stress disorder among survivors of left-sided native valve endocarditis. Clinical Infectiuos Diseases. 2009 Jun 1;48(11):1559-65. Hermanides J, Belqhazi L, Michels RP, Hoekstra JBL. Lower incidence of type 2 diabetes mellitus with changes in lifestyle: clues from World War II. Nederlands Tijdschrift voor Geneeskunde. 2008 Nov 1;152(44):2415-7. Hermanides J, Wentholt IME, Hart AA, Hoekstra JBL, DeVries JH. No apparent local effect of insulin on microdialysis continuous glucose- monitoring measurements. Diabetes Care. 2008 Jun;31(6):1120-2. Verhagen DW, Hermanides J, Korevaar JC, Bossuyt PM, van den Brink RB, Speelman P, van der Meer JT. Prognostic value of serial C-reactive protein measurements in left-sided native valve endocarditis. Archives of Internal Medicine. 2008 Feb 11;168(3):302-7. Verhagen DW, Hermanides J, Korevaar JC, Bossuyt PM, van den Brink RB, Speelman P, van der Meer JT. Extension of antimicrobial treatment in patients with left-sided native valve endocarditis based on elevated C-reactive protein values. European Journal of Clinical Microbioly & Infectious Diseases. 2007 Aug;26(8):587-90.

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Biografie

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Op 11 januari 1981 kwam Jeroen ter aarde. Aan het feit dat dit in het VUmc moest

gebeuren, kon hij niet zo veel doen. Als oudste van drie broers doorliep hij vlijtig de

Wilhelmina School, waar hij zijn basisonderwijs genoot. Tijdens zijn middelbare

schooltijd op het Hermann Wesselink College ontwikkelde hij zijn kunstzinnige

talenten in de muziek en op het toneel. Met halflang haar stal hij regelmatig de

show met vingervlugge gitaarsolo's en in de rol van Lysander in Shakespeare's 'Een

Midzomernachtsdroom' bewees hij zijn dramaturgisch talent. In 1999 behaalde hij

zijn gymnasiumdiploma, om direct door te stomen naar de Universiteit van

Amsterdam voor zijn opleiding geneeskunde. Het duurde niet lang voordat Jeroen

zijn ouderlijk huis verruilde voor een studentenkamer in de Amsterdamse Pijp.

Alhier verwierf hij faam met zijn koffie met een vleugje kaneel. Dit brouwsel hielp

hem zijn studie geneeskunde vlot te doorlopen, in combinatie met een muzikale

carrière in de bands Dunk en Ruis en een sportcarrière bij hockeyclub AMVJ. Na

een wetenschappelijke stage in Ethiopië ging Jeroen wetenschappelijk onderzoek

doen bij prof. dr. P. Speelman. Van het één kwam het ander, en uiteindelijk wist hij

na zijn co-schappen een promotieplaats te verkrijgen bij prof. dr. J.B.L. Hoekstra.

Tijdens zijn promotietijd werd hij gegrepen door de kunst van de pijnbestrijding, en

in april 2010 is hij begonnen met de opleiding tot anesthesioloog in het AMC onder

leiding van dr. C. Keijzer.

De Paranimfen

Bas van Geldere en Wietse Eshuis

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Co-authors

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� Anders Frid (MD, PhD), Division of Diabetes and Endocrinology, Malmö University

Hospital, Malmö, Sweden � Annemarie E Engström (MD), Department of Cardiology, Academic Medical Centre,

Amsterdam

� Anthonie W Lensing (MD, PhD), Bayer HealthCare AG, Wuppertal, Germany

� Arianne C van Bon (MD), Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� Augustinus AM Hart (PhD)

� Bregtje A Lemkes (MD), Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� C Pieter Henny (MD, PhD), Department of Anaesthesiology, Academic Medical Centre,

Amsterdam

� Catherine Fermon (MD, PhD), Centre dÊéducation pour le traitement de diabète et des

maladies de la nutrition, Centre Hospitalier de Roubaix, Roubaix, France

� Chantal Mathieu (MD, PhD), Department of Endocrinology, Catholic University Leuven,

Leuven, Belgium,

� Colin M Dayan (MD, PhD), Department of Medicine, University of Bristol, Bristol, United

Kingdom

� Daniela Bruttomesso (MD, PhD), Department of Clinical and Experimental Medicine,

Division of Metabolic Diseases, University of Padova, Padova, Italy

� Danny M Cohn (MD, PhD), Department of Vascular Medicine, Academic Medical Centre,

Amsterdam

� Dirk J Gouma (MD, PhD), Department of Surgery, Academic Medical Centre,

Amsterdam

� Durk F Zandstra (MD, PhD), Department of Intensive Care Medicine, Onze Lieve

Vrouwe Gasthuis, Amsterdam

� Frits Holleman (MD, PhD), Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� Frits R Rosendaal (MD, PhD), Department of Clinical Epidemiology, the Netherlands,

Leiden University Medical Center, Leiden

� Gan van Samkar (MD), Department of Anaesthesiology, Academic Medical Centre,

Amsterdam

� Harry R Büller (MD, PhD), Department of Vascular Medicine, Academic Medical Centre,

Amsterdam

� Heleen M Oudemans- van Straaten (MD, PhD), Department of Intensive Care Medicine,

Onze Lieve Vrouwe Gasthuis, Amsterdam

� Iris ME Wentholt (MD, PhD), Department of Internal Medicine, Academic Medical

Centre, Amsterdam

� J Hans DeVries (MD, PhD), Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� Jan W van Dalen, Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� Joost BL Hoekstra (MD, PhD), Department of Internal Medicine, Academic Medical

Centre, Amsterdam

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� Joost CM Meijers (PhD), Department of Vascular Medicine, Academic Medical Centre,

Amsterdam

� Jose PS Henriques (MD, PhD), Department of Cardiology, Academic Medical Centre,

Amsterdam

� Kirsten NŒrgaard (MD, PhD), Department of Endocrinology, Hvidovre University

Hospital, Hvidovre, Denmark

� Krishan D Sjauw (MD), Department of Cardiology, Academic Medical Centre,

Amsterdam

� Marcel M Levi (MD, PhD), Department of Vascular Medicine, Academic Medical Centre,

Amsterdam

� Martin Homering (PhD), Bayer HealthCare AG, Wuppertal, Germany

� Nadja Haj Mohammad (MD, PhD), Department of Internal Medicine, Academic Medical

Centre, Amsterdam

� Olivier RC Busch (MD, PhD), Department of Surgery, Academic Medical Centre,

Amsterdam

� Peter Diem (MD, PhD), Division of Endocrinology, Diabetes and Clinical Nutrition,

University Hospital and University of Bern, Bern, Switzerland

� Peter HJ van der Voort (MD, PhD), Department of Intensive Care Medicine, Onze Lieve


� Pieter W Kamphuisen (MD, PhD), Department of Vascular Medicine, Academic Medical

Centre, Amsterdam

� Robert J Bosman (MD, PhD), Department of Intensive Care Medicine, Onze Lieve


� Robin Koops, Inreda Diabetic BV, Goor

� Roeland Huijgen (MD), Department of Vascular Medicine, Academic Medical Centre,

Amsterdam

� Ron Dotsch (MSc), Behavioural Science Institute, Department of Social and Cultural

Psychology, Radboud University Nijmegen

� Sarah E Siegelaar (MD), Department of Internal Medicine, Academic Medical Centre,

Amsterdam

� Silvia Kuhls (PhD), Bayer HealthCare AG, Wuppertal, Germany

� Thomas M van Gulik (MD, PhD), Department of Surgery, Academic Medical Centre,

Amsterdam

� Titia M Vriesendorp (MD, PhD), Department of Internal Medicine, Academic Medical

Centre, Amsterdam

� Wietse J Eshuis (MD), Department of Surgery, Academic Medical Centre, Amsterdam

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Dankwoord

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Allereerst een woord van dank voor alle deelnemers aan de verschillende studies. Mijn promotor, prof.dr. J.B.L. Hoekstra. Beste Joost, enorm veel dank voor je enthousiaste, motiverende en sportieve begeleiding. In de loop van mijn promotietraject ben ik er steeds meer achter gekomen dat het helemaal niet zo vanzelfsprekend is dat een promotor zo betrokken is bij zijn (explosief groeiend aantal) promovendi als jij. Je stimuleert nieuwe initiatieven en ideeën, waardoor onderzoek doen veel interessanter wordt dan domweg „tellen‰. Bovendien heeft onder jouw supervisie (en soms lichte dwang) ook mijn hardloopcarrière een enorme vlucht genomen. Ondanks mijn afscheid van de interne verschijn ik nog graag aan de start van de volgende triatlon. Mijn co-promotor, dr. J.H. De Vries. Beste Hans, je hebt me de afgelopen jaren verlicht; figuurlijk met je enorme hoeveelheid parate kennis, maar ook letterlijk, want het enige daglicht waaraan ik op een reguliere werkdag werd blootgesteld, kwam via jouw raam binnen in mijn „aquariumÊ als je deur open stond. Gelukkig was dat zeer regelmatig, want je was bijna altijd bereikbaar voor een vraag, overleg of discussie. Ik heb ontzettend veel van je geleerd en dank je hartelijk voor je uitstekende en prettige begeleiding. Ik hoop dat deze samenwerking nog niet ten einde is. Prof.dr. Büller, prof.dr. Rosendaal, prof.dr. Schlack en prof.dr. Zandstra wil ik hartelijk danken voor het kritisch beoordelen van mijn proefschrift en hun bereidheid om zitting te nemen in de commissie. Dear Professor Pickup, I am very honoured that you are willing to come to Amsterdam and serve on my doctorate committee. Prof.dr. Peter Speelman en „baas‰ dr. Dominique Verhagen, dank voor de mogelijkheid om de eerste wetenschappelijke stappen te zetten. Mijn collegae van de diabetes groep: Bregtje, Sanne, Sarah, Anne, Arianne, Aline, Max, Iris, Titia en Frits, het was een mooie tijd, bedankt voor de gezelligheid en samenwerking! Het jonge aanstormende talent: Airin, Josefine, Wanda, Yoeri, Lonneke en Temo. De groep groeit en dat is goed. Ook dank aan de gang- en koffiegenoten van F4-Noord: Els, Anouk, Miriam en Maaike. En natuurlijk de diabetesverpleegkundigen: Peggy en Dorien, jullie hulp bij de Eurythmics trial was zeer welkom! Ook dank aan Hester, Ineke en Monique. Wendelien, Jan-Willem en Juliette, bedankt voor jullie inzet als student. Er was ook interdisciplinaire samenwerking, zoals de „Glucothrombotic Society‰: Danny, PW, Roeland en professor Joost Meijers. Maar ook dank aan de afdelingen cardiologie (Krischan en Annemarie), chirurgie en anesthesiologie van het AMC. De intensive care van het Onze Lieve Vrouwe Gasthuis (dr. Rob Bosman en professor Durk Zandstra). En natuurlijk de psychologische ondersteuning vanuit Nijmegen: Ron, wat mooi dat onze namen in hetzelfde rijtje boven een artikel staan, de meest „holistische‰ benadering van hypoglykemie op de IC ooit. Maarten, dank voor alle glucose gedachtewisselingen en obese mails, daar moet toch ooit een studie uit voortkomen!

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Mijn paranimfen en vrienden, Bas en Wietse. Het is goed te weten dat jullie er zijn, mocht het fysiek of verbaal uit de hand lopen tijdens de verdediging. Bas, wederom brothers in arms! Wietse, „De pancreas zorgt ervoor dat je met de mensen in je omgeving kunt omgaan�.‰ (www.kruidenvrouwtje.nl), dat geldt zeker voor ons! Vrienden: muziek, sport of gewoon naar de kroeg hebben helemaal niets met werk te maken en daarom zijn jullie zo belangrijk. Lieve opaÊs en omaÊs, wat fantastisch dat jullie er bij zijn en ik weet zeker dat oma Jannie dit heel mooi had gevonden. Jullie zijn de meest hippe grootouders ooit. Mijn BroÊs: Stephan en Michel, wat goed dat we met zÊn 3en zijn. Laten we vooral de „mannen‰-avonden in stand houden, ook al zijn er soms wat kilometers te overbruggen. Lieve Pa en Ma, zonder een berucht bord havermoutpap was ik er niet geweest, laat staan dat deze promotie dan had plaatsgevonden. Altijd kan ik onvoorwaardelijk op jullie rekenen. Ik ben trots op zulke lieve ouders. Tot slot, waar onderzoek al niet toe kan leiden: lief Caatje, je bent de mooiste, belangrijkste en vrolijkste ontdekking van mijn promotie! En nu een feest.

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Documents

Sensor-augmented pump therapy lowers HbA1c in suboptimally controlled Type 1 diabetes; a randomized controlled trial