Model-based Therapeutics and Critical Care - models + medicine for better clinical outcomes …Tomorrow’s care at yesterday’s prices! Geoffrey M Shaw1 J Geoffrey Chase2 1 Dept of Intensive Care, Christchurch Hospital 2 Dept of Mechanical Engineering, Univ of Canterbury Chch MedTech Workshop Dec 18, 2013 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 (another day) Model based therapeutics “MBT” 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 . G pG G (t ) S I G (t ) . min( d 2 P2 , Pmax ) EGPb CNS PN (t ) Q(t ) 1 G Q(t ) VG Q nI ( I (t ) Q(t )) nc . I Q(t ) 1 G Q(t ) u (t ) u (G ) nL I (t ) nK I (t ) nI ( I (t ) Q(t )) ex (1 xL ) en 1 I I (t ) VI VI Model based therapeutics “MBT” Finally, we use computational analysis to solve these equations to help us design and implement new, safer therapies. . min( d P , P ) EGPb CNS PN (t ) Q(t ) G. pG G (t ) S I G (t ) Q(t ) min( d 22 P22 , Pmax max ) EGPb CNS PN (t ) G pG G (t ) S I G (t ) 1 G Q(t ) VG 1 G Q(t ) VG . Q ( t ) Q. nI ( I (t ) Q(t )) nc Q(t ) Q nI ( I (t ) Q(t )) nc 1 G Q(t ) 1 G Q(t ) . u (t ) u (G ) n I ( t ) I. nLL I (t ) nK I (t ) nI ( I (t ) Q(t )) uex (t ) (1 x ) uen (G ) I 1 I I (t ) nK I (t ) nI ( I (t ) Q(t )) exVI (1 xLL ) enVI 1 I I (t ) VI VI So where does this go? • • • • • • 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… Doctors clinical experience and intuition 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 glycemic control 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? Feedback control Decision Support System Measured data . G pG G (t ) S I G (t ) . min( d 2 P2 , Pmax ) EGPb CNS PN (t ) Q(t ) 1 G Q(t ) VG Q nI ( I (t ) Q(t )) nc Q(t ) 1 G Q(t ) u (t ) u (G ) n I (t ) I L nK I (t ) nI ( I (t ) Q(t )) ex (1 xL ) en 1 I I (t ) VI VI . Identify and utilise “immeasurable” patient parameters For insulin sensitivity (SI) Patient management Standard infuser equipment adjusted by nurses “Nurse-in-the-loop” system. Standard ICU equipment and/or low-cost commodity hardware. ICU bed setup INPUT OUTPUT OUTPUT Glucometers: Measure blood sugar levels Nutrition pumps: Feed patient through nasogastric tube, IV routes or meals Infusion pumps: Deliver insulin and other medications to IV lines. Sub-cut insulins may also be used. Variability, not physiology or medicine… Fixed dosing systems Typical care Adaptive control Engineering approach Patient response to insulin Controller identifies and manages patient-specific variability Fixed protocol treats everyone much the same Controller Variability flows through to BG control Variability stopped at controller Blood Glucose levels Models offer the opportunity to identify, diagnose and manage variability directly, to guaranteed risk levels. Models, Variability and Risk 5th, 25th, 50th (median), 75th, 95th percentile bounds for SI(t) variation based on current value Stochastic model predicts SI SI percentile bounds + known insulin tnow tnow tnow+(1-3)hr tnow+(1-3)hr Insulin sensitivity Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level Insulin sensitivity 95th 75th 50th 25th th th Blood glucose 25th 50th 75th 95th intevention an output BG distribution can be forecast using the model Stochastic m bounds (5th Forecast BG percentile bounds: for insulin s 75 A predicted patient response! over next 150 initially ide 25 th Iterative process targets this BG 5th forecast to the range we want: BG [mg/dL] Patient response forecast can be recalculated for For a different given feed+insulin treatments = ... 95th 5th 5th + system model Blood glucose = optimal treatment found! 5th 6.5 25th 50th 4.4 75th 95th Time For a given intevention distribution using the m Maximum 5% Risk of BG < 4.4 mmol/L 5th, 25th, 50th (median), 75th, 95th percentile bounds for SI(t) variation based on current value Stochastic model predicts SI SI percentile bounds + known insulin + tnow tnow tnow+(1-3)hr system model = ... tnow+(1-3)hr Insulin sensitivity Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level Insulin sensitivity 95th 75th 50th 25th th th th Iterative 5th process targets this BG forecast to the range we want: 5th BG [mg/dL] Blood glucose 5th 25th 50th 75th 95th Patient response forecast can be recalculated for For a given feed+insulin different treatments intevention an output BG distribution can be forecast using the model Stochastic m bounds (5th Forecast BG percentile bounds: for insulin s 75 A predicted patient response! over next 150 initially ide 25 95th Blood glucose = optimal treatment found! 5th 6.5 25th 50th 4.4 75th 95th Time For a given intevention distribution using the m 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 STAR Chch STAR Gyula SPRINT Chch SPRINT Gyula # BG measurements: 1,486 622 26,646 1088 Measures/day: 13.5 12.8 16.1 16.4 6.1 [5.7 – 6.8] 89.4 6.0 [5.4 – 6.8] 84.1 5.6 [5.0 – 6.4] 86.0 6.30 [5.5 – 7.5] 76.4 2.48 7.7 2.0 2.8 % BG < 4.0 mmol/L 1.54 4.5 2.89 1.90 % BG < 2.2 mmol/L 0.0 0.16 1 (started hypo) 0.04 0 8 (4%) 0 Workload Control performance BG median [IQR] (mmol/L): % BG in target range)* % BG > 10 mmol/L • 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) Safety # patients < 2.2 mmol/L 0 Clinical interventions Median insulin (U/hr): 3 2.5 3.0 3.0 Median glucose (g/hr): 4.9 4.4 4.1 7.4 *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… cTIB = cumulative time in band: exposure (badness) over time • 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) 4.0 – 7.0 5.0 – 8.0 4.0 – 8.0 cTIB Survival Odds Ratio cTIB > 50% cTIB > 60% cTIB > 70% • 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 cTIB > 80% Day (1-14) “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 Hospital mortality SPRINT/Pre-SPRINT LOS ≥ 1 day P=0.244 LOS ≥ 2 days P=0.077 LOS ≥ 3 days LOS ≥ 4 days LOS ≥ 5 days P=0.023 P=0.012 P=0.010 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 Why? Better resolution of organ failure… 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) 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… $2M Cost per patient Cost per annum Cost per year $1.5M ICU Costs $1 M Laboratory Glucose control Antimicrobials Inotropes $0.5 M Dialysis Ventilation Transfusions Pre-SPRINT SPRINT 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 deathreversing 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 Jess Lin Aaron LeCompte Thomas Lotz Carmen Doran Kate Moorhead Stephan Schaller Sam Sah Pri Sheng-Hui Wang Sophie Penning Brian Juliussen Jason Wong et al Uli Simone Goltenbott Scheurle Harry Chen Ulrike Pielmeier Leesa Pfeifer Klaus Mayntzhusen Hans Gschwendtner Ummu Jamaludin Matt Signal Lusann Amy Blakemore & Yang Piers Lawrence Jackie Normy Razak Fatanah Suhaimi Azlan Othman Chris Pretty Darren Hewett Liam Fisk Parente James Revie Jenn Dickson Acknowledgements Glycemia - 1 Dunedin Dr Kirsten McAuley Prof Jim Mann The Danes Prof Steen Andreassen Dr Thomas Desaive Math, Stats and Engineering Gurus Dr Dom Lee Dr Bob Broughton Prof Graeme Wake Hungarians The Belgians Dr Jean-Charles Preiser Dr Balazs Benyo Some guy named Geoff Geoff Shaw and Geoff Chase Dr Paul Docherty 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 Don’t let this happen to you! Acknowledgements (Neonatal) Glycemia - 2 Auckland and Waikato Prof Jane Harding Ms Deb Harris RN Dr Phil Weston And Dr Adrienne Lynn and all the clinical staff at Christchurch Women's Hospital, and all the clinical staff Waikato Hospital eTIME (Eng Tech and Innovation in Medicine) Consortia 4 countries, 7 universities, 12+ hospitals and ICUs and 35+ people Acknowledgements Dept of Intensive Care