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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 communication Date Submitted: May 14, 2009 Source: Darrell M. Wilson, MD Contact: Stanford

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Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

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  1. Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: BAN and Diabetes a template for medical device communication Date Submitted: May 14, 2009 Source: Darrell M. Wilson, MD Contact: Stanford Voice: +1 650 723-5791, E-Mail: dwilson@stanford.edu Re: 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.

  2. IEEE Body Area NetworkDiabetes - July 09 Darrell M. Wilson, MD dwilson@stanford.edu dped.stanford.edu

  3. Goals Review diabetes for a few minutes Discuss current conventional treatment approaches Discuss cut-edge approaches include closed loop systems and there problems

  4. 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

  5. Insulin dependent IDDM Juvenile onset Brittle Type 1 Non-insulin dependent NIDDM Adult onset Type 2 Diabetes MellitusMajor Forms Atypical Diabetes Minor forms

  6. Environmental triggers Genetics Insulitis Type 1 Diabetes Diabetes Exposure Renal Complications Eye Complications Large Vessels

  7. Honeymoon

  8. Travis, DM in Children, MPCP#29, 1987 Diab Care 29:1150, 2006

  9. 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

  10. Costs Continue to Increase (U.S.)(in Billions of Dollars) Diabetes Care 26:917-932, 2003

  11. Single Subject without DM Mazze DTT 2008

  12. Single Subject With DM Mazze DTT 2008

  13. Hemoglobin A1c http://www.cem.msu.edu/~cem252/sp97/ch18/ch18s20.GIF

  14. Hemoglobin A1c http://home.comcast.net/~creationsunltd/images/comparebsandhga1c.gif

  15. DCCT DCCT NEJM, 329:977,1993

  16. Glucose ControlGlycosylated Hemoglobin DCCT NEJM, 329:977,1993

  17. RetinopathyPrimary Prevention DCCT NEJM, 329:977,1993

  18. Insulin Action Curves

  19. Four Shots

  20. 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

  21. Pumps

  22. Pump Example

  23. 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

  24. 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

  25. Measurement of Glucose Direct Methods meters future sensors Data analysis average variability extremes

  26. Insulin Variability Heinemann DTT 4:673, 2002

  27. Maximizing Bolus DeliveryGetting the Bolus The price of a missed bolus is high Burdick Peds 113:211e, 2004

  28. Kinetics vsDynamics

  29. Snacks LOW FAT 30 gm CHO 2.5 gm protein 1.3 gm fat 138 kCal HIGH FAT 30 gm CHO 2 gm protein 20 gm fat 320 kCal

  30. Sensor Lag

  31. Feature Summary

  32. Trend Arrows Navigator MiniMed >2 (mg/dL)/min 1 to 2 (mg/dL)/min -1 to 1 (mg/dL)/min -1 to -2 (mg/dL)/min < -2 (mg/dL)/min Updated every minute Updated every 5 minutes

  33. FreeStyle Navigator™Continuous Glucose Monitor Transmitter Receiver Sensor/Sensor Mount

  34. 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

  35. 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

  36. MiniMed Paradigm REAL-Time Insulin Pump and Continuous Glucose Monitoring System

  37. 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

  38. 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

  39. 0-6 Months of the Study 0 – 6 mos. 6 – 12 mos.

  40. Changes in A1c in > 25 yr olds + Difference: -0.53% P-value <0.001

  41. Differences in Distribution of A1c Levels in > 25 yr olds at 26 Weeks Cumulative % 26 week glycated hemoglobin (%)

  42. Changes in A1c in 8-14 yr olds P-value=0.29

  43. Secondary A1c Outcomes in 8-14 yr olds P=0.009 P=0.04 P= 0.01

  44. Changes in A1c in 15-24 yr olds P-value=0.52

  45. Artificial Pancreas (b-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

  46. Proportional-Integral-Derivative (PID) Control Integral “windup” can lead to postprandial hypoglycemia Many possible tuning procedures Error = setpoint – measured output = desired glucose – measured glucose Manipulated Input (insulin) Proportional gain Integral time Derivative time B. Wayne Bequette

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