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July 2009
Darrell M. Wilson, MD (Stanford)Slide 1Submission
Doc: 15-09-0537-00-0006July 2009
Slide 1
Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)
Submission Title: BAN and Diabetes a template for medical device communicationDate Submitted: May 14, 2009Source: Darrell M. Wilson, MDContact: Stanford Voice: +1 650 723-5791, E-Mail: [email protected]: Diabetes Abstract:.Purpose: Same
Notice: This document has been prepared to assist the IEEE P802.15. It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein.
Release: The contributor acknowledges and accepts that this contribution becomes the property of IEEE and maybe made publicly available by P802.15.
July 2009
Darrell M. Wilson, MD (Stanford)Slide 2
IEEE Body Area NetworkDiabetes - July 09
• Darrell M. Wilson, MD
• dped.stanford.edu
July 2009
Darrell M. Wilson, MD (Stanford)Slide 3Submission
Doc: 15-09-0537-00-0006
Goals
• Review diabetes for a few minutes
• Discuss current conventional treatment approaches
• Discuss cut-edge approaches include closed loop systems and there problems
July 2009
Darrell M. Wilson, MD (Stanford)Slide 4Submission
Doc: 15-09-0537-00-0006
Goals
• What “we” envision as Body Area Network upsides for diabetes
• What “we” envision as important features/functional aspects to such a network
• Q and A
Diabetes MellitusMajor Forms
• Insulin dependent
• IDDM
• Juvenile onset
• Brittle
• Type 1
• Non-insulin dependent
• NIDDM
• Adult onset
• Type 2
Atypical DiabetesMinor forms
GeneticsEnvironmental
triggers
Insulitis
Type 1 Diabetes
Diabetes Exposure
RenalComplications
EyeComplications
LargeVessels
Time Course of Diabetes
Time .....0
20
40
60
80
100
Pe
rce
nt
DemandMassFunction
Trigger?
Insulinresistantperiods
ClinicalPresentation
Honeymoon
Travis, DM in Children, MPCP#29, 1987Diab Care 29:1150, 2006
July 2009
Darrell M. Wilson, MD (Stanford)Slide 9Submission
Doc: 15-09-0537-00-0006
Diabetes Impact
• Type 1 ~ 800,000 to 1,000,000– ~120,000 < 20 years of age
• Type 2 ~ 7 million– another ~ 7 million undiagnosed– Prevalence
• 1.3% 18-44 years of age• 6.2% 45-65 years of age• 10.4% 65-74 years of age
$92
$109
$138
$40$47
$54
$132
$156
$192
$0
$40
$80
$120
$160
$200
$240
Direct Indirect Total
2002
2010
2020
Diabetes Care 26:917-932, 2003
Costs Continue to Increase (U.S.)(in Billions of Dollars)
Mazze DTT 2008
Single Subject without DM
Mazze DTT 2008
Single Subject With DM
Hemoglobin A1c
http://www.cem.msu.edu/~cem252/sp97/ch18/ch18s20.GIF
Hemoglobin A1c
http://home.comcast.net/~creationsunltd/images/comparebsandhga1c.gif
DCCT
DCCT NEJM, 329:977,1993
Glucose ControlGlycosylated Hemoglobin
DCCT NEJM, 329:977,1993
RetinopathyPrimary Prevention
DCCT NEJM, 329:977,1993
DCCT Data
Glycosylated Hemoglobin (%)5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5
Pro
gre
ssio
n -
Ret
ino
pat
hy
(per
100
pt-
yr)
0
2
4
6
8
10
Sev
ere
Hyp
og
lyce
mia
(per
100
pt/
yr)
20
40
60
80
100
120
Insulin Action Curves
Hours
0 5 10 15 20 25 30
Act
ion
0
20
40
60
80
100 LisproRegularNPH & LenteUltra
Four Shots
Time
0 4 8 12 16 20 24
Act
ion
July 2009
Darrell M. Wilson, MD (Stanford)Slide 22Submission
Doc: 15-09-0537-00-0006
Pumps
• What do they do?• Basal(s) rates• Meal boluses• Correction bolus• What don't they do?• Still open loop• Require a great deal of attention to detail
Pumps
Pump Example
Time
0 4 8 12 16 20 24
Act
ion
July 2009
Darrell M. Wilson, MD (Stanford)Slide 25Submission
Doc: 15-09-0537-00-0006
How to Select the Correct Amount of Insulin
• Good carbohydrate counting– Frequently in error
• Using pumps– Use the calculators/wizards
• Using injections– Use a discrete plan
• Adjusting for exercise
• Bedtime snacks
July 2009
Darrell M. Wilson, MD (Stanford)Slide 26Submission
Doc: 15-09-0537-00-0006
Pumps and Injections
• I like dose calculators– Earlier age of dosing “competency”– The paradox of both greater dose flexibility and
consistency• Time of day• Fine tuning
– Better download and data analysis • Meal “buckets”
– Future “automatic” adjustment of parameters– Lead into the feedback controlled pump
July 2009
Darrell M. Wilson, MD (Stanford)Slide 27Submission
Doc: 15-09-0537-00-0006
Measurement of Glucose
• Direct– Methods
• meters• future sensors
– Data analysis• average• variability• extremes
Lucile Packard Children's Hospital at StanfordPackard Pediatric Diabetes Center Diabetes Educator Line: (650) 498-7353
Insulin Scale for: Test subject
Standard Insulin DoseBreakfast Lunch DinnerNPH: NPH:Lantus: Lantus:Humalog Humalog Humalog
Insulin to Carbohydrate Ratio Carb Ratio= Amount of grams of carbs covered by 1 unit of Humalog
Breakfast Carb Ratio: Lunch Carb Ratio: Dinner Carb Ratio:12 15 18
Grams of Carbs Insulin Grams of Carbs Insulin Grams of Carbs Insulin6 0.5 H 8 0.5 H 9 0.5 H12 1.0 H 15 1.0 H 18 1.0 H18 1.5 H 23 1.5 H 27 1.5 H24 2.0 H 30 2.0 H 36 2.0 H30 2.5 H 38 2.5 H 45 2.5 H36 3.0 H 45 3.0 H 54 3.0 H42 3.5 H 53 3.5 H 63 3.5 H48 4.0 H 60 4.0 H 72 4.0 H54 4.5 H 68 4.5 H 81 4.5 H60 5.0 H 75 5.0 H 90 5.0 H
Correction insulin Correction Factor= How many points 1 unit of Humalog lowers blood glucose Target BG= Correction factor tries to bring BG to this desired number Do not use correction scale if your last shot was less than 2 hours agoBreakfast Lunch Dinner
Correction Target BG: Correction Target BG: Correction Target BG:
50 120 60 120 80 120 My BG is between My BG is between My BG is between
70 100 = subtract 1H 70 100 = subtract 1H 70 100 = subtract 1H
101 120 no extra 101 120 no extra 101 120 no extra121 145 +0.5 H 121 150 +0.5 H 121 160 +0.5 H146 170 +1.0 H 151 180 +1.0 H 161 200 +1.0 H171 195 +1.5 H 181 210 +1.5 H 201 240 +1.5 H196 220 +2.0 H 211 240 +2.0 H 241 280 +2.0 H221 245 +2.5 H 241 270 +2.5 H 281 320 +2.5 H246 270 +3.0 H 271 300 +3.0 H 321 360 +3.0 H271 295 +3.5 H 301 330 +3.5 H 361 400 +3.5 H296 320 +4.0 H 331 360 +4.0 H 401 440 +4.0 H321 345 +4.5 H 361 390 +4.5 H 441 480 +4.5 H346 370 +5.0 H 391 420 +5.0 H 481 520 +5.0 H371 395 +5.5 H 421 450 +5.5 H 396 420 +6.0 H 451 480 +6.0 H 421 445 +6.5 H 481 510 +6.5 H 446 470 +7.0 H
Total Humalog insulin dose= correction insulin + insulin for carbs
Insulin Variability
Heinemann DTT 4:673, 2002
July 2009
Darrell M. Wilson, MD (Stanford)Slide 30Submission
Doc: 15-09-0537-00-0006
Maximizing Bolus DeliveryGetting the Bolus
• The price of a missed bolus is high
Burdick Peds 113:211e, 2004
July 2009
Darrell M. Wilson, MD (Stanford)Slide 31Submission
Doc: 15-09-0537-00-0006
Kinetics vs Dynamics
Approximate Time (min)
0 100 200 300 400
Per
cen
t
0
20
40
60
80
100pharmacokineticspharmacodynamics
Snacks
LOW FAT
30 gm CHO2.5 gm protein
1.3 gm fat138 kCal
HIGH FAT
30 gm CHO2 gm protein20 gm fat320 kCal
July 2009
Darrell M. Wilson, MD (Stanford)Slide 33Submission
Doc: 15-09-0537-00-0006
Sensor LagSensor Lag
Time (minutes) (0 = start if meal)
-40 -20 0 20 40 60 80 100 120 140
Blo
od G
luco
se (
mg/
dl)
0
100
200
300
400
500
Freestyle Sensor
Feature Summary
Paradigm 722
DexCom7-plus
Navigator
Rate of change arrows
Yes Yes Yes
Projected low alarm
No No Yes
Days of wear 5 3-7 5
Ability to download Yes Yes Yes
Ability to integrate with pump
Yes (MiniMed)
No Pending (Cozmo)
>2 (mg/dL)/min
< -2 (mg/dL)/min
-1 to -2 (mg/dL)/min
1 to 2 (mg/dL)/min
-1 to 1 (mg/dL)/min
Trend ArrowsNavigator MiniMed
Updated every minute Updated every 5 minutes
July 2009
Darrell M. Wilson, MD (Stanford)Slide 36Submission
Doc: 15-09-0537-00-0006FreeStyle Navigator™ Continuous Glucose Monitor
Receiver
Transmitter
Sensor/Sensor Mount
FreeStyle Navigator™ System
Intended Features– Home continuous monitoring system. – 3-day sensor continuously measures
glucose– Transmitter sends updated glucose
reading every minute– Alarms for hi/lo glucose– Alarms for projected hi/lo glucose– On-board trend and statistical reporting– Event entry (food, insulin, meds, exercise,
etc)– 60-day memory & upload to computer– Traditional glucose meter built in
• System calibration
• Backup glucose meter
Pilot Study to Evaluate the Navigator in Children with T1D
• 30 children with T1D• HA1c 7.1 ± 0.6%• Smart pumps• Ask to wear sensor
daily• Algorithm based
adjustments of insulin infusion rates
MiniMed Paradigm REAL-Time Insulin Pump and
Continuous Glucose Monitoring System
DexCom 7 Plus
• 91 insulin requiring adults– 75 Type 1– 16 Type 2
• Three 72 hour wears• Randomized
– Blinded– Shows 2/3 wears
Garg Diabetes Care 29:44–50, 2006
July 2009
Darrell M. Wilson, MD (Stanford)Slide 41Submission
Doc: 15-09-0537-00-0006
Modes of Glucose Sensor Use
• Meter replacement• Hypoglycemia alarm
– Down alert
• Hyperglycemia alarm– Up alert
• Pattern recognition• Dynamic adjustment• Infusion controller
– Suggestive vs closed loop• Nocturnal pump shutoff for unaddressed low alarms
– Non-diabetic inpatients– Research studies
Enrollment
Randomization
RT-CGM “Usual Care”
6 mo. Outcome
Continue RT-CGM
12 mo. Outcome
run-in phase
Start RT-CGM
0 – 6 mos.
6 – 12 mos.
0-6 Months of the Study
Changes in A1c in > 25 yr olds
Difference: -0.53%
+
P-value <0.001
Differences in Distribution of A1c Levels in > 25 yr olds at 26 Weeks
Cum
ulat
ive
%
26 week glycated hemoglobin (%)
Changes in A1c in 8-14 yr olds
P-value=0.29
P=0.04
P=0.009
P= 0.01
Secondary A1c Outcomes in 8-14 yr olds
Changes in A1c in 15-24 yr olds
P-value=0.52
Artificial Pancreas (-cell) • Artificial Pancreas Software (APS)
Features:– Communication with sensors & pumps
– Modularity, Plug-and-Play (PnP)
– Human Machine Interfaces (HMIs)
– Physician control
– Data storage
– Audio & Visual alarms
– Standalone application
– Data recording
– Safety and redundancy
Controller
Insulin PumpHMI
APS HMI
Sensor HMI
APS
Database
Startup Interface
July 2009
Darrell M. Wilson, MD (Stanford)Slide 50Submission
Doc: 15-09-0537-00-0006
B. Wayne Bequette
Proportional-Integral-Derivative (PID) Control
dt
tdedttetekutu
tytrte
D
t
Ic
00
1
Manipulated
Input (insulin)
Error = setpoint – measured output
= desired glucose – measured glucose
Proportional
gainIntegral time Derivative time
• Integral “windup” can lead to postprandial hypoglycemia
• Many possible tuning procedures
B. Wayne Bequette
Internal Model Control (IMC)
Controller
(approximate
“model inverse”)
sensor pump subject
glucose setpoint
Insulin-
Glucose
Model
Sensor
Model
~ymodel-
predicted
output
measured
output (glucose)
y
+_
manipulated input
(insulin infusion)
u
r
Estimates of other variables (possible)
model-reality mismatch
July 2009
Darrell M. Wilson, MD (Stanford)Slide 52Submission
Doc: 15-09-0537-00-0006
B. Wayne Bequette
Controller Information
• Feedback-only• Meal Announcement (feedforward)• Meal Detection
– Meal size estimation vs. priming bolus
• Insulin-on-Board– Impose infusion constraint vs. modified tuning
• Individualization– Subject supplied history vs. results of protocol
July 2009
Darrell M. Wilson, MD (Stanford)Slide 53Submission
Doc: 15-09-0537-00-0006
Closed-loop vs. hybrid control
6 12 18 24 30 36 420
100
200
300
400Closed Loop (N=5)
meals
setpoint
Hybrid Closed Loop (N=5)
Glu
cose
(m
g/d
l)
Mean Nocturnal Peak PP
Full CL 156 (149-163) 109 (87-131) 232 (208-256)
Hybrid 135 (129-141) 114 (98-131) 191 (168-215)
July 2009
Darrell M. Wilson, MD (Stanford)Slide 54Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• A stable, company neutral system to reliably exchange data among diabetes related devices– Glucose meters– Glucose sensors– Insulin infusion devices – Control algorithm devices (if not
embedded)
July 2009
Darrell M. Wilson, MD (Stanford)Slide 55Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• A stable, company neutral system to reliably exchange data among diabetes related devices– External alarms– Activity monitors– GPS– Phone– External communication devices– Ear buds?
July 2009
Darrell M. Wilson, MD (Stanford)Slide 56Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• Note the difference – NOT just a sensor network– Insulin infusion devices– Insulin infusion algorithm
• NOW we are infusing insulin, a potential lethal medicine
July 2009
Darrell M. Wilson, MD (Stanford)Slide 57Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• High security
• High specificity
July 2009
Darrell M. Wilson, MD (Stanford)Slide 58Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• Bidirectional communications between devices with confirmation and error checking
• Reasonable transmission range– thru the body at least
• Monitoring of BAN status
• Fails safely with clear warnings
July 2009
Darrell M. Wilson, MD (Stanford)Slide 59Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• Easily interrogated (downloadable)– Cell phone, internet
July 2009
Darrell M. Wilson, MD (Stanford)Slide 60Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• Privacy and safety
• Privacy vs safety
July 2009
Darrell M. Wilson, MD (Stanford)Slide 61Submission
Doc: 15-09-0537-00-0006What we would like to see in a Body Area Network
• And of course– Cheap– Low energy requiring– Long lasting– Green– Easy to use
Thanks!