1. Clinical Validation of a Model-based Glycaemic Control Design Approach and Comparison to Other Clinical Protocols J.G. Chase et al Dept of Mechanical Engineering University of Canterbury IEEE EMBC, August 30 – September3, 2006, New York, NY

2. A 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

3. Glucose 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

4. Glucose Control = Balance Nutritional Inputs Endogenous Glucose Production Exogenous Insulin Endogenous Insulin Non-insulin Removal Rising GlucoseFalling Glucose Control Inputs Measurement & Intervention Frequency  Size of swings in glucose Match inputs and ability to utilise

5. A Simple PK-PD Model Glucose compartment Interstitial insulin compartment Plasma insulin compartment Model has been validated in 3 prior clinical studies

6. A Simple Nutritional Input Model  Plasma glucose rate of appearance from stepwise enteral feed rate fluxes  Increasing stepwise enteral feed rate fluxes  ~ intestinal absorption = FAST  t 1/2 = 20mins  Decreasing stepwise enteral feed rate fluxes  Impaired splanchnic and peripheral glucose uptake = SLOW  t 1/2 = 100mins P(t)

7. Virtual Patient Design Approach Virtual Patient Trials - using fitted long term, retrospective patient data to create virtual patients to test protocols 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)

8. SPRINT 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…)

9. Protocol 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

10. Protocol Comparisons ~18-45%

11. Protocol Comparisons SPRINTAIC4Mayo clinicLeuvenBathYale Sliding scale Agg. sliding Log Median or Mean5.795.938.595.606.216.706.896.61 Multiplicative STD1.291.351.291.651.451.41.361.33 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 band61.7%62.2%11.2%35.8%45.5%22.3%41.9%43.8% Time in 4-7.75 band83.5%82.9%27.4%51.0%70.0%64.8%60.0%65.2% Time less than 44.4%1.1%0.6%23.6%7.1%5.9%2.4%2.8% Time higher than 7.7512.1%16.1%72.0%25.3%22.9%29.3%37.5%32.0% Average insulin (U/hr)2.42.61.63.05.84.61.92.1 Average % feed of goal61.9%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 tightestClear 2nd

12. 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 Results

13. Time in Band and CDF Glucose mmol/ L Cumulative probability 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%

14. 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 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% Percentageof 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

15. Mortality 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% 2004-05SPRINT Mortality % 44 deaths in 168 patients 21 deaths in 119 patients ICU mortality reduced 32% p = 0.03 APACHE II 2004-05SPRINT TotalMortalityTotalMortality 1-14191142 15-2480224913 25-342710253 35+1053 ~75% of APACHE II scores available in both cases Rest 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?

16. Summary & 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

17. Acknowledgements Maths and Stats Gurus Dr Dom Lee Dr Bob Broughton Dr Chris Hann Prof Graeme Wake Thomas Lotz Jessica Lin & AIC3 AIC2 Jason Wong & AIC4 AIC1 The Danes Prof Steen Andreassen Dunedin Dr Kirsten McAuley Prof Jim Mann Assoc. Prof. Geoff Chase AIC5: Mike, Aaron and Tim

18. Acknowledgements Intensive Care Unit Nursing Staff Christchurch Hospital

20. Cohort Match 2004-05SPRINT (78% available)(76% available) APACHE II score Risk of death 21.3 33% 20.1 39% APACHE III diagnosis Cardiovascular Respiratory Gastrointestinal Neurological Trauma Renal Gynaecologic Orthopaedic Sepsis Metabolic Haematologic Other Total 37 36 15 12 15 1 0 8 3 0 16 23 17 8 11 0 1 8 2 0 1 12787 APACHE II similar Distribution of diagnoses similar between cohorts

21. Mortality 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. Both ICU mortality and in- hospital mortality significant for lengths of stay greater than 3 days.

22. Survival Curves - ICU 2004-05 SPRINT

23. Survival Curves - Hospital 2004-05 SPRINT

24. BG Distribution - Overall

25. Distribution of BG Means

26. BG Variation

27. Time to Band – Hourly Average BG 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

28. Nursing Survey: SPRINT

29. 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

30. 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