Clinical Validation of a Model-based Clinical Validation of a Model-based Glycaemic Control Design Approach and Glycaemic Control Design Approach and Comparison to Other Clinical ProtocolsComparison to Other Clinical Protocols

J.G. Chase et alDept of Mechanical Engineering

University of Canterbury

IEEE EMBC, August 30 – September3, 2006, New York, NY

A Well Known StoryA Well Known Story Hyperglycaemia is prevalent in critical care

Impaired insulin production + Increased insulin resistance = High BG Average blood glucose values > 10mmol/L are not uncommon All due to the stress of the patient’s condition

Tight control better outcomes: Reduced mortality ~17-43% (6.1-7.75 mmol/L) [van den Berghe, Krinsley]

SPRINT reduces mortality 32-45% depending on LoS in ICU (details to come)

Costly treatments (mech. ventilation, transfusions, … ) are also reduced

However, how best to attack the problem? How to manage highly insulin resistant patients (usually high APACHE score)?

How to provide better safety from hypoglycaemia?

Model-based methods may offer an opportunity to better design and compare

Glucose Balance & ControlGlucose Balance & Control• If the ability to remove excess glucose from nutrition was fully functional

• Then plasma glucose would be lower– Hence, any excess nutrition effectively “backs up” in the plasma– Inputs and the patients ability to utilise them are not being properly matched

• Only 2 ways to reduce glucose levels:– Add insulin limited by saturation effects, therefore only so much can be done– Reduce excess nutritional glucose

• Several recent studies have shown that high glucose feeds in critical care are one cause of hyperglycaemia [Patino et al, 1999; Krishnan, 2005; Weissman, 1999; Woolfson, 1980; … ]

– Krishnan et al noted that above 66% of ACCP Guidelines increased mortality!– Patino et al kept glycaemia < 7.5 mmol/L (average) with reduced dextrose feeds

• This study seeks to use both sides of the glucose balance to tightly regulate blood glucose levels in critical care

– Modulate both nutritional input and insulin bolus/infusion – Rather than a more typical normal insulin-only approach

Glucose Control = BalanceGlucose Control = Balance

• Nutritional Inputs

• Endogenous Glucose Production

• Exogenous Insulin

• Endogenous Insulin• Non-insulin Removal

Rising Glucose Falling Glucose

Control Inputs

Measurement & Intervention Frequency Size of swings in glucose

Match inputs and ability to utilise

)(1

tPQ

QGGSGpG

GeIG

A Simple PK-PD ModelA Simple PK-PD Model

kIkQQ

V

tu

I

nII ex

I

)(

1

Glucose compartment

Interstitial insulin compartment

Plasma insulin compartment

Model has been validated in 3 prior clinical studies

A Simple Nutritional Input ModelA Simple Nutritional Input Model

2P

1P

3P

2P

Plasma glucose rate of appearance from stepwise enteral feed rate fluxes

Increasing stepwise enteral feed rate fluxes

~ intestinal absorption = FAST

t1/2 = 20mins

Decreasing stepwise enteral feed rate fluxes

Impaired splanchnic and peripheral glucose uptake = SLOW

t1/2 = 100mins

)( where))(()( 1)(

111 iittk

iiiii tPPePtPPtttP ipd

)( where))(()( 1)(

111 iittk

iiiii tPPePtPPtttP ipr

P(t)

Virtual Patient Design ApproachVirtual Patient Design Approach

Virtual Patient Trials - usingfitted long term, retrospective patientdata to create virtual patients to testprotocols in simulation

─ Tests algorithms and methods safely─ Provides insight into potential long term

clinical performance─ Provides relatively large, repeatable

cohort for easy comparison─ Very fast fine tuning performance and

safety schemes─ Monte Carlo simulation to account for

different sensors and their errors

─ N = 19-49 patients used typically with an average of 3-8 days stay each, which can provide several patient years in Monte Carlo analysis.

─ Ethics approval by the NZ South Island Regional Ethics Committee (A)

"Virtual Patient" Controller

Retrospective patient profile of

+

G(t(i))

"Patient" Glycaemic

Response

Iterative Bisection Method: control inputs

Integral-Fitting Method: time variant patient parameters

Generate patient glycaemic

response to controller-determined

control inputs

Glucose-Insulin System

Model

SI(t(i)) and pG(t(i))

P(t(i+1)) and u(t(i+1))

SI(t) and pG(t)

Model-based targeted glucose reduction

Control input/s

+

0

1

2

3x 10

-3

SI (L/m

in.m

U)

Insulin sensitivity

0

2000

4000

6000

u(t) (m

U/m

in) / P

(t) (55000*m

mol/L.m

in)

Constant feed-rate, variable insulin control protocol

0

2000

4000

6000

Variable feed-rate and insulin control protocol

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

50

100

Time (mins)

u(t) (m

U/m

in) / P

(t) (1000*m

mol/L.m

in)

Hospital control

u(t)P(t)

u(t)P(t)

P(t)u(t)

)(1

)( tPQ

QGGSGpG

GEIG

kIkQQ

V

tu

I

nII ex

I

)(

1

)(1

)( tPQ

QGGSGpG

GEIG

kIkQQ

V

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nII ex

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1

)(1

)( tPQ

QGGSGpG

GEIG

kIkQQ

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SPRINTSPRINT• Optimises both insulin and nutrition rates to control

glycaemic levels

• Developed through extensive computer simulation– Designed to mimic computerised protocol based on effective

insulin sensitivity from prior hour

• Simple interface for ease of use by nursing staff:

• Mimics the very tight control of computerised simulations with minimal implementation cost

– (no bedside computer required…)

Protocol ComparisonsProtocol Comparisons

• Evaluated several published protocols– Van den Berghe et al, Krinsley, Laver, Goldberg

– Two different sliding scales from Christchurch

• Virtual trials with N = 19 cohort– Average APACHE II = 21.8

– Average LoS ~3days

• Monte Carlo simulation including measurement error n = 20 times per patient

• Results reported as probability density functions for comparison

Protocol ComparisonsProtocol Comparisons

45%

25%

Bad!

VeryBad!

Also Bad!

Not Trying?

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2 4 6 8 10 12 14 16 18 20Blood glucose level [mmol/L]

De

ns

ity o

f m

ea

su

rem

en

ts

45%

25%

Bad!

VeryBad!

Also Bad!

Not Trying?

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

45%

25%

Bad!

VeryBad!

Also Bad!

Not Trying?

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

SPRINT

AIC4

Bath

Leuven

Mayo Clinic

Yale

Sliding Scale

Aggressive sliding scale

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2 4 6 8 10 12 14 16 18 20Blood glucose level [mmol/L]

De

ns

ity o

f m

ea

su

rem

en

ts

~18-45%

Protocol ComparisonsProtocol Comparisons

SPRINT AIC4 Mayo clinic Leuven Bath Yale Sliding scale Agg. sliding

Log Median or Mean 5.79 5.93 8.59 5.60 6.21 6.70 6.89 6.61

Multiplicative STD 1.29 1.35 1.29 1.65 1.45 1.4 1.36 1.33

68.3% range (4.50-7.45) (4.39-8.01) (6.68-11.05) (3.40-9.24) (4.27-9.03) (4.78-9.40) (5.08-9.33) (4.99-8.77)

95.5% range (3.50-9.58) (3.25-10.82) (5.20-14.20) (2.06-15.24) (2.94-13.13) (3.41-13.18) (3.75-12.65) (3.77-11.62)

Time in 4-6.1 band 61.7% 62.2% 11.2% 35.8% 45.5% 22.3% 41.9% 43.8%

Time in 4-7.75 band 83.5% 82.9% 27.4% 51.0% 70.0% 64.8% 60.0% 65.2%

Time less than 4 4.4% 1.1% 0.6% 23.6% 7.1% 5.9% 2.4% 2.8%

Time higher than 7.75 12.1% 16.1% 72.0% 25.3% 22.9% 29.3% 37.5% 32.0%

Average insulin (U/hr) 2.4 2.6 1.6 3.0 5.8 4.6 1.9 2.1

Average % feed of goal 61.9% 75.8% 67.7% 67.7% 71.8% 71.4% 67.7% 67.7%

Note virtual trial cohort APACHE II scores are much higher than in many protocols

Similar and tightest Clear 2nd

Note: SPRINT trial data for first 8613 measurements, ~90 patients.

All simulated results were for the 19 virtual patients, ~1700 hours of trials

Clinical vs. Simulation ResultsClinical vs. Simulation Results

Time in Band and CDFTime in Band and CDF

Glucose mmol/ L

Cu

mu

lati

ve p

rob

abil

ity

Percentiles for ICU data- SPRINT

2.5mmol/L = 4.1x 10-5

3.0mmol/L = 0.001

4.0mmol/L = 0.041

6.1mmol/L = 0.59

7.0mmol/L = 0.81

7.75mmol/L =0.91

SPRINT ICU raw data- 26-04-06

ICU data- SPRINT (lognormal) 26-04-06

Model simulation- SPRINT (lognormal)

Model simulation- van den Berghe (lognormal)

Model simulation- Krinsley

Very tight control puts high percentage in bands

Simulated vs. Clinical differences smaller here

> 90%

All performance indicators agree with simulation and tight control!

Protocol is safe – no clinically significant hypoglycaemia

Effective use of insulin and nutrition

Tightness of glucose control: the first 165 patients

Performance OutcomesPerformance Outcomes

Average BG 5.8 mmol/L

Average time in 4 -6.1 61%

Average time in 4 -7 82%

Average time in 4 -7.75 89%

Percentage of all measurements less than 4 3.3%

Percentage of all measurements less than 2.5 0.1%

Average hourly insulin 2.9 U

Average percentage of goal feed 73%

Average feed rate 56.3 ml/hr

(assuming 1.06 cal/ml for feed) 1431 cal/day

Sepsis:

• A major cause of mortality and significant clinical issue that can be addressed with tight control • Mortality from Sepsis is down -46%• For LoS > 3 days mortality with sepsis is down -51% (for LoS < 3 it is down -10%) • Analysis is still rough and not yet significant, although trends appear stable at this time

• More importantly, no “Breakthrough Sepsis” reported yet, i.e. no new sepsis once under control

Mortality after ~25k HoursMortality after ~25k Hours• Reductions in mortality for patients with length of stay >= 3 days

• Cohorts are well matched for APACHE II and APACHE III diagnosis

0%

5%

10%

15%

20%

25%

30%

2004-05 SPRINT

Mo

rta

lity

%

44 deaths in 168 patients

21 deaths in 119 patients

ICU mortality reduced 32%

p = 0.03

APACHE II2004-05 SPRINT

Total Mortality Total Mortality

1-14 19 1 14 2

15-24 80 22 49 13

25-34 27 10 25 3

35+ 1 0 5 3

~75% of APACHE II scores available in both casesRest of levels are not significant as yet

• Reductions are large, particularly compared to ROD for APACHE II

• Almost all reductions are in the more critically ill (APACHE II: 25-34, p = 0.02) - as expected?

Summary & ConclusionsSummary & Conclusions

• Development of an Insulin+Nutrition control approach• Virtual Trials control design method

– Good correlation with published results for other protocols– Other protocols do not appear to provide as tight a control as model-

based methods (e.g. Blank et al and others)

• SPRINT is a simpler version mimicking the computerized and model-based method – for ease of clinical validation

• SPRINT results include:– Reduced ICU and hospital mortality by ~29-36% for patients with LoS >

3 days – Bigger reductions at LoS > 4 and 5 days– Very tight control shown with this approach

• Model-based approaches present a sure method of designing safe, effective and optimal control for this and similar problems

AcknowledgementsAcknowledgements

Maths and Stats Gurus

Dr Dom LeeDr Bob Broughton Dr Chris Hann

Prof Graeme Wake

Thomas Lotz

Jessica Lin & AIC3AIC2Jason Wong & AIC4AIC1

The Danes

Prof Steen Andreassen

Dunedin

Dr Kirsten McAuley Prof Jim Mann

Assoc. Prof. Geoff Chase

AIC5: Mike, Aaron and Tim

AcknowledgementsAcknowledgements

Intensive Care Unit Nursing Staff Christchurch Hospital

Cohort MatchCohort Match2004-05 SPRINT

(78% available) (76% available)

APACHE II score Risk of death

21.333%

20.139%

APACHE III diagnosis

Cardiovascular

Respiratory

Gastrointestinal

Neurological

Trauma

Renal

Gynaecologic

Orthopaedic

Sepsis

Metabolic

Haematologic

Other

Total

37

36

15

12

15

1

0

0

8

3

0

0

16

23

17

8

11

0

0

1

8

2

0

1

127 87

APACHE II similar

Distribution of diagnoses similar between cohorts

Mortality vs. LoSMortality vs. LoS

• Statistical significance for a reduction in mortality for different length of ICU stay– SPRINT reductions in mortality more evident for patients that

stay in ICU longer.

z-testICU Hospital

LOS > =2 0.13 0.15LOS > =3 0.03 0.03LOS > =4 0.02 0.01LOS > =5 0.02 0.01

Fisher testICU Hospital

LOS > =2 0.16 0.19LOS > =3 0.04 0.04LOS > =4 0.03 0.02LOS > =5 0.03 0.02

p-value

p-value

Both ICU mortality and in-hospital mortality

significant for lengths of stay greater than 3 days.

Survival Curves - ICUSurvival Curves - ICU

Time (days)

Perc

ent

302520151050

100

90

80

70

60

50

40

14.9914 * *18.4822 22.2 *

Mean Median IQRTable of Statistics

SPRINT - ICU Mortality2004-05 - ICU Mortality

Variable

Kaplan-Meier Survival Curve - ICU Mortality, SPRINT vs. 2004-05

Censoring Column in ICUMort - SPRINT, ICUMort - 2004-05Kaplan-Meier Method

p = 0.002 for a difference between curves

2004-052004-05

SPRINTSPRINT

Time (days)

Perc

ent

302520151050

100

90

80

70

60

50

40

30

17.0955 * *15.7395 14.2 23.9

Mean Median IQRTable of Statistics

SPRINT - Hospital Mortality2004-05 - Hospital Mortality

Variable

Kaplan-Meier Survival Curve - Hospital Mortality, SPRINT vs 2004-05

Censoring Column in HospMort - SPRINT, HospMort-04-05Kaplan-Meier Method

p < 0.002 for a difference between curves

Survival Curves - HospitalSurvival Curves - Hospital

2004-052004-05

SPRINTSPRINT

BG Distribution - OverallBG Distribution - Overall

Distribution of BG MeansDistribution of BG Means

BG VariationBG Variation

Time to Band – Hourly Average Time to Band – Hourly Average BGBG

Note: each hourly BG distribution is lognormal so those statistics are used. Bars are 95% intervals.

6.1 mmol/L

~3.75 mmol/L minimum

7.75 mmol/L

Nursing Survey: SPRINTNursing Survey: SPRINT

0

5

10

15

Ease of Use Quality Suitability

Num

ber

of r

espo

nden

ts

Very Good

Good

Satisfactory

Poor

23.6% of simulated van den Berghe measurements < 4mmol/L

2.6% of SPRINT clinical measurements < 4mmol/L

0.6% of simulated Krinsley measurements < 4mmol/L

10% of SPRINT ICU measurements > 7.75 mmol/L

70% of simulated Krinsley measurements > 7.75 mmol/L

25.3% of simulated van den Berghe measurements > 7.75 mmol/L