Departm e nt of Inten sive C are
Geoffrey M Shaw1
J Geoffrey Chase2
Balazs Benyo3
1 Dept of Intensive Care, Christchurch Hospital2 Dept of Mechanical Engineering, Univ of Canterbury3 Dept informatics, Budapest University of Technology
and Economics
Model-based Therapeutics: Tomorrow’s care at yesterday’s cost
NZ ANZICS Dunedin March 15 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 carepoor 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”
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
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Model based therapeutics “MBT”
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Finally, we use computational analysis to solve these equations to help us design
and implement new, safer therapies.
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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 nursesPatient management
Measured data
“Nurse-in-the-loop” system. Standard ICU equipment and/or low-cost commodity hardware.
Decision Support System
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Identify and utilise “immeasurable”
patient parametersFor 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.
INPUT OUTPUT OUTPUT
Blood Glucose levels
Controller
Fixed dosing systemsTypical care
Adaptive controlEngineering 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
4.4
6.5
Insulin sensitivity
Blood glucose
tnow
Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level
For a given feed+insulinintevention an output BG distribution can be forecast using the model
tnow+(1-3)hr
95th
75th
50th
25th
5th
5th
25th
50th
75th
95th
5th, 25th, 50th (median), 75th, 95th percentile bounds for SI(t) variation based on current value
Insulin sensitivity
Blood glucose
tnow
Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level
For a given feed+insulinintevention an output BG distribution can be forecast using the model
tnow+(1-3)hr
95th
75th
50th
25th
5th
5th
25th
50th
75th
95th
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!Patient response forecast can be recalculated for
different treatments
Models, Variability and Risk
BG [mg/dL]
Time
4.4
6.5
Insulin sensitivity
Blood glucose
tnow
Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level
For a given feed+insulinintevention an output BG distribution can be forecast using the model
tnow+(1-3)hr
95th
75th
50th
25th
5th
5th
25th
50th
75th
95th
5th, 25th, 50th (median), 75th, 95th percentile bounds for SI(t) variation based on current value
Insulin sensitivity
Blood glucose
tnow
Stochastic model shows the bounds (5th – 95th percentile) for insulin sensitivity variation over next 1-3 hours from the initially identified level
For a given feed+insulinintevention an output BG distribution can be forecast using the model
tnow+(1-3)hr
95th
75th
50th
25th
5th
5th
25th
50th
75th
95th
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!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 Chch STAR Gyula SPRINT Chch SPRINT Gyula
Workload# BG measurements: 1,486 622 26,646 1088Measures/day: 13.5 12.8 16.1 16.4Control 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)* 89.4 84.1 86.0 76.4% BG > 10 mmol/L 2.48 7.7 2.0 2.8Safety% BG < 4.0 mmol/L 1.54 4.5 2.89 1.90% BG < 2.2 mmol/L 0.0 0.16 0.04 0
# patients < 2.2 mmol/L 0 1 (started hypo) 8 (4%) 0
Clinical interventionsMedian 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…
• 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)
Surv
ival
Odd
s R
atio
4.0 – 7.0 5.0 – 8.0 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=0.077 P=0.023 P=0.012 P=0.010P=0.244
LOS ≥ 2 days LOS ≥ 3 days LOS ≥ 4 days LOS ≥ 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 2Organ 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…C
ost p
er a
nnum
Cos
t per
pat
ient
$0.5 M
$1.5M
Pre-SPRINT SPRINT
$2M
$1 M
Cos
t per
yea
r
Transfusions
Dialysis
Inotropes
Laboratory
Ventilation
Antimicrobials
Glucose control
ICU Costs
$0.5 M
$1.5M
Pre-SPRINTPre-SPRINT SPRINTSPRINT
$2M
$1 M
Cos
t per
yea
r
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 LotzJess LinAaron LeCompte
Jason Wong et alHans Gschwendtner
Lusann
Yang
Amy Blakemore &
Piers Lawrence
Carmen Doran
Kate Moorhead Sheng-Hui WangSimone
Scheurle
Uli
Goltenbott Normy Razak Chris PrettyJackie
Parente
Darren Hewett James RevieFatanah Suhaimi
UmmuJamaludin
Leesa PfeiferHarry ChenSophie PenningStephan Schaller
Sam Sah PriBrianJuliussen Ulrike Pielmeier
Klaus Mayntzhusen Matt Signal Azlan Othman Liam Fisk Jenn Dickson
Math, Stats and Engineering Gurus
Dr Dom LeeDr 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 DesaiveDr Jean-Charles
Preiser
Hungarians
Dr Balazs Benyo
Belgium: Dr. Fabio Taccone, Dr JL Vincent, Dr P Massion, Dr R RadermeckerHungary: 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
AcknowledgementsDept of Intensive Care