Geoffrey M Shaw 1 J Geoffrey Chase 2 Balazs Benyo 3 1Dept of Intensive Care, Christchurch Hospital 2Dept of Mechanical Engineering, Univ of Canterbury 3Dept informatics, Budapest University of Technology and Economics Model-based Therapeutics: Tomorrow’s care at yesterday’s cost NZ ANZICS Dunedin March

The bread and butter of ICU: Some of the basic things that we do... Glucose control and nutrition Sedation Cardiovascular management: “tropes and fluids” Mechanical ventilation

The bread and butter of ICU: Intuition and experience, provides the fundamental basis of care delivered to the critically ill; it is specific to the clinician, but it is not specific to the patient. The result:  highly variable and over customised care  poor quality and increased costs of care, What are needed :  Treatments that are patient specific and independent of clinician variability and bias  A “one model”, not “one size”, fits-all approach

The bread and butter of ICU: Glucose control and nutrition Mechanical Ventilation (next presentation!)

Model based therapeutics  “MBT”

First, we describe the physical systems to analyse

Model based therapeutics  “MBT” Next, we build up a mathematical representation of the system

Model based therapeutics  “MBT” Finally, we use computational analysis to solve these equations to help us design and implement new, safer therapies.

So where does this go? Doctors clinical experience and intuition  Insulin  Glucose  Sedation  Steroids and vaso-pressors  Inotropes  And many many more … Glucose levels Cardiac output Blood pressures SPO2 / FiO2 HR and ECG And many more…  Insulin Sensitivity  Sepsis detection  Circulation resistance  A better picture of the patient-specific physiology in real-time at the bedside  Optimise glucose control  Manage ventilation  Diagnose and treat CVS disease  And many other things…

A wish list What will happen if I add more insulin? What is the hypoglycemia risk for this insulin dose? – Over time? – When should I measure next to be sure? How good is my control? Does it need to be better? Should I change nutrition? What happens if someone else has changed it? How should I then change my insulin dose? – Many if not all protocols are “carbohydrate blind” and thus BG is a very poor surrogate of response to insulin Is patient condition changing? What happens if it changes between measurements?

Standard infuser equipment adjusted by nurses Patient management Measured data “Nurse-in-the-loop” system. Standard ICU equipment and/or low-cost commodity hardware. Decision Support System Identify and utilise “immeasurable” patient parameters For insulin sensitivity (SI) Feedback control

ICU bed setup Nutrition pumps: Feed patient through nasogastric tube, IV routes or meals Glucometers: Measure blood sugar levels Infusion pumps: Deliver insulin and other medications to IV lines. Sub-cut insulins may also be used. INPUTOUTPUT

Blood Glucose levels Controller Fixed dosing systems Typical care Fixed dosing systems Typical care Adaptive control Engineering approach Adaptive control Engineering approach Variability flows through to BG control Variability stopped at controller Models offer the opportunity to identify, diagnose and manage variability directly, to guaranteed risk levels. Fixed protocol treats everyone much the same Controller identifies and manages patient-specific variability Patient response to insulin Variability, not physiology or medicine…

BG [mg/dL] Time th, 25 th, 50 th (median), 75 th, 95 th percentile bounds for S I (t) variation based on current value Stochastic model predicts SI Forecast BG percentile bounds: A predicted patient response! Forecast BG percentile bounds: A predicted patient response! SI percentile bounds + known insulin + system model =... SI percentile bounds + known insulin + system model =... Iterative process targets this BG forecast to the range we want: = optimal treatment found! Patient response forecast can be recalculated for different treatments Models, Variability and Risk

BG [mg/dL] Time th, 25 th, 50 th (median), 75 th, 95 th percentile bounds for S I (t) variation based on current value Stochastic model predicts SI Forecast BG percentile bounds: A predicted patient response! SI percentile bounds + known insulin + system model =... Iterative process targets this BG forecast to the range we want: = optimal treatment found! Iterative process targets this BG forecast to the range we want: = optimal treatment found! Patient response forecast can be recalculated for different treatments Maximum 5% Risk of BG < 4.4 mmol/L

Why this approach? Model lets us guarantee and fix risk of hypo- and hyper- glycemia Giving insulin (and nutrition) is a lot easier if you know the range of what is likely to happen. Thus, one can optimise the dose under all the normal uncertainties – No risk of “unexplained” hypoglycemia Allows clinicians to select a target band of desired BG and guarantee risk of BG above or below We tend to fix a 5% risk of BG < 4.4 mmol/L which translates to less than 1/10,000 (interventions) risk of BG < 2.2 mmol/L (should be about 2% by patient) – Fyi, this is how airplanes are designed and how Christchurch's high rises should have been designed!

Some Results to Date Very tight Very safe Works over several countries and clinical practice styles Also been used in Belgium Measuring SI is very handy whether you do it with a model (STAR) or estimated by response (SPRINT) STAR ChchSTAR GyulaSPRINT ChchSPRINT Gyula Workload # BG measurements: 1, , Measures/day: Control performance BG median [IQR] (mmol/L): 6.1 [5.7 – 6.8] 6.0 [5.4 – 6.8] 5.6 [5.0 – 6.4] 6.30 [5.5 – 7.5] % BG in target range)* % BG > 10 mmol/L Safety % BG < 4.0 mmol/L % BG < 2.2 mmol/L # patients < 2.2 mmol/L 0 1 (started hypo) 8 (4%)0 Clinical interventions Median insulin (U/hr): Median glucose (g/hr): *4-8mmol/L

So, because we know the risk … We get tight control We are very safe We do it by identifying insulin sensitivity (SI) every intervention – Measuring SI is a direct surrogate of patient response to all aspects of metabolism, and is not available without a (good) model – Using just BG level is a very poor surrogate because it lacks insulin/nutrition context. Like trying to estimate kidney function from just urine output – it lacks context

So, because we know the risk … We can minimise interventions, measurements and clinical effort with confidence and exact knowledge of the risk We know what to do when nutrition changes, and can change it directly if we require! So, what’s the glycemic target you ask? To what level do we control? – All we know is that level is bad and so is variability with about 1M opinions as to what and how much…. – We, of course, have an answer… we think…

Measures both level and variability We examined 3 “intermediate ranges” that most would think are not at all different! And 4 thresholds (50, 60, 70 and 80%) versus outcome (odds ratio) cTIB = cumulative time in band: exposure (badness) over time

cTIB 1700 patients from SPRINT and before SPRINT, and both arms (high and low) of Glucontrol trial in 7 EU countries Is there a difference between 7 and 8 mmol/L or 3-4 mmol/L of variability??? Yes, significantly so from day 2-3 onward Difference is more stark if you eliminate patients who have at least 1 hypo (BG < 2.2) We think the answer is clear and know how to safely achieve those goals Because you can calculate it in real time you can use it as an endpoint for a RCT Day (1-14) Survival Odds Ratio 4.0 – – – 8.0 cTIB > 50% cTIB > 60% cTIB > 70% cTIB > 80%

“SPRINT”: Specialised Relative Insulin and Nutrition Tables Chase JG, Shaw G, Le Compte A, Lonergan T, Willacy M, Wong XW, Lin J, Lotz T, Lee D, Hann C: Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Crit Care 2008, 12:R49

P=0.077P=0.023P=0.012P=0.010P=0.244 LOS ≥ 2 daysLOS ≥ 3 daysLOS ≥ 4 daysLOS ≥ 5 daysLOS ≥ 1 day The horizontal blue line shows the mortality for the retro cohort. The green line is the total mortality of SPRINT patients against total number of patients treated on the protocol Hospital mortality SPRINT/Pre-SPRINT

SOFA scores reduce faster with SPRINT and do so from day 2 Organ failure free days: SPRINT = 41.6% > Retro = 36.6% (p<0.0001) Number of organ failures (% total possible) defined as SOFA > 2 for 1 SOFA score component: SPRINT = 16% < Retro = 19% (p<0.0001) Why? Better resolution of organ failure… Chase JG, Pretty CG, Pfeifer L, Shaw GM, Preiser JC, Le Compte AJ, Lin J, Hewett D, Moorhead KT, Desaive T: Organ failure and tight glycemic control in the SPRINT study. Crit Care 2010, 14:R154.

At yesterday's cost… Cost per annum Cost per patient $0.5 M $ 1.5M Pre-SPRINT $ 2M $ 1 M Cost per year Transfusions Dialysis Inotropes Laboratory Ventilation Antimicrobials Glucose control ICU Costs $0.5 M $ 1.5M Pre-SPRINTPre-SPRINT $ 2M $ 1 M Cost per year Transfusions Dialysis Inotropes Laboratory Ventilation Antimicrobials Glucose control ICU Costs Transfusions Dialysis Inotropes Laboratory Ventilation Antimicrobials Glucose control ICU Costs Pfeifer L, Chase JG, Shaw GM, “What are the benefits (or costs) of tight glycaemic control? A clinical analysis of the outcomes,” Univ of Otago, Christchurch, Summer Studentship 2010

In summary … We approach glycemic control like any problem – Understand the system (what happens when I do “x”?) – Understand the risk (how likely will the situation change? What happens if it does?) We accomplish this by using models – Of metabolism to understand the system – Of variability to understand the risk From understanding the system and understanding the risk we can dose to get safe and effective glycemic control by understanding that there are two ways (not just 1!) to lower (or raise) glycemia. STAR = Stochastic TARgeted glycemic control – Semi-automated – Reduced effort – Improved confidence and performance

A brief pause for reflection …

The future : digital human?

But beware of hyperbole! “Scientists have developed a technology that can bring people back from the dead up to seven hours after their hearts have stopped – and want it installed routinely in hospitals and even ambulances “Ecmo (sic) machines, which act like heart bypass systems, but can be fitted in minutes are already used to save cardiac arrest victims in Japan and South Korea, where they are credited with reviving people long after they have apparently died “ [Dr Sam] Parnia...director of resuscitation at Stony Brook University...is publishing a book, The Lazarus Effect, about how death- reversing technologies are changing medicine”

The RCT methodology was created to validate responses to interventions amongst populations of highly complex biological systems (aka humans). Prediction of individual responses is not possible because it requires an understanding beyond our current state of knowledge. Clinical ‘trialists’ therefore must regard all patients as “black boxes”

State-of-the-art computing can be used model and validate these relationships; previously only guessed at, to create new knowledge and understanding. Future RCTs should clinically validate interventions based on model-based therapeutics; a one-model-fits all approach. (Patient-specific)

Acknowledgements Glycemia PG Researchers Thomas Lotz Jess Lin Aaron LeCompte Jason Wong et al Hans Gschwendtner LusannYang Amy Blakemore & Piers Lawrence Carmen Doran Kate Moorhead Sheng-Hui Wang SimoneScheurle UliGoltenbott Normy Razak Chris Pretty JackieParente Darren Hewett James Revie FatanahSuhaimi UmmuJamaludin LeesaPfeifer Harry Chen Sophie Penning Stephan Schaller Sam Sah Pri BrianJuliussen Ulrike Pielmeier Klaus Mayntzhusen Matt Signal Azlan Othman Liam Fisk Jenn Dickson

Math, Stats and Engineering Gurus Dr Dom Lee Dr Bob Broughton Dr Paul Docherty Prof Graeme Wake The Danes Prof Steen Andreassen Dunedin Dr Kirsten McAuley Prof Jim Mann Acknowledgements Glycemia - 1 Geoff Shaw and Geoff Chase Don’t let this happen to you! Some guy named Geoff The Belgians Dr Thomas Desaive Dr Jean-Charles Preiser Hungarians Dr Balazs Benyo Belgium: Dr. Fabio Taccone, Dr JL Vincent, Dr P Massion, Dr R Radermecker Hungary: Dr B Fulesdi, Dr Z Benyo, Dr P Soos, Dr I Attila, and 12 others And all the clinical staff at over 12 different ICUs

Acknowledgements (Neonatal) Glycemia - 2 And Dr Adrienne Lynn and all the clinical staff at Christchurch Women's Hospital, and all the clinical staff Waikato Hospital Prof Jane Harding Ms Deb Harris RN Dr Phil Weston Auckland and Waikato

eTIME (Eng Tech and Innovation in Medicine) Consortia 4 countries, 7 universities, 12+ hospitals and ICUs and 35+ people

Acknowledgements Dept of Intensive Care