Transcript Slide 1

Model-based Therapeutics:
Tomorrow’s care at yesterday’s
cost
Geoffrey M Shaw1
J Geoffrey Chase2
Balazs Benyo3
1 Dept of Intensive Care, Christchurch Hospital
2 Dept of Mechanical Engineering, Univ of Canterbury
3 Dept informatics, Budapest University of Technology
and Economics
NZ ANZICS Dunedin March 15 2013
Department of Intensive Care
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”
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  xL ) uen (G )
I   1   I I (t )  nK I (t )  nI ( I (t )  Q(t ))  ex
VI  (1  xL ) enVI
1   I I (t )
VI
VI
So where does this go?
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•
•
•
•
•

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 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