Glycemic Control: how tight?
How model could help?
T Desaive
Cardiovascular Research Center
University of Liege
Belgium
JG Chase
Centre for Bio-Engineering
University of Canterbury
New Zealand
A Well Known Story
 Tight glycaemic control (TGC) can improve outcomes

Reduced ICU mortality up to 45% using a target of 6.1 mmol/L or below (Van den Berghe et al,
2001; Krinsley, 2004; Chase et al, 2008)

Reduced organ failure rate and severity (Chase et al, 2010)

Reduced cost (Krinsley, 2006; van den Berghe et al, 2006)
 In-silico simulated clinical trials (“Virtual trials”) can
increase safety and save time + cost

Enable the rapid testing of new TGC intervention protocols and analysing control protocol
performance

Used to simulate a TGC protocol using a virtual patient profile identified from clinical data
and different insulin and nutrition inputs.

Development of SPRINT protocol and optimisation of adult and neonatal model-based control
(Lonergan et al, 2006)
 The problem is it is hard ... Many studies have failed to
repeat the initial very positive results ... But some have!

Hypoglycemia is the main issue and only 1 study reduce it (as well as mortality)
The Problem and Solution (?)

The main issues:

Variability: Inability to control glucose to target tightly
Variability: Increased hypoglycemia
Variability: Tendency to measure infrequently

Outcome = poor performance and inability to achieve outcomes



Solution?



Model-based methods that identify patient condition and adapt treatment
to match
Model-based methods that understand the likely (stochastic) variability in
patient condition and response to therapy [Unique to this work]
Model-based engineering of clinical therapy, as developed from
engineering models and methods
The Model
Physiologically Relevant Model
17000
ND data
ND model
T2DM data
T2DM model
16000
15000
Pre-hepatic insulin secretion, (Uen), [mU/hr]

14000
Normal
13000
12000
T2DM
11000
10000
Limited to 1-16U/hour
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Blood glucose, (BG), [mmol/L]
Simple Physiology

Model-based approach:

SI identified hourly for every patient
Insulin
Brain
Insulin losses
(liver, kidneys)
Blood
Glucose
Effective
insulin
Dextrose Absorption
P(t )  min( d 2 P2 , Pmax )
P2   min( d 2 P2 , Pmax )  d1P1
P  d P  D(t )
1
1 1
Glucose
n
uli
s
In
ty
ivi
t
i
ns
se
Liver
Liver
Other
cells
Plasma
Insulin
Pancreas
A bit more detail
Central Nervous
System Uptake
Endogenous
Other insulin-independent
CNS
glucose production
glucose uptake pGG
EGP
d2
d1
Glucose absorption through
stomach and gut
Insulin
injections
(1-xL)uen
uen=f(e-I(t))
Endogenous insulin after
1st pass hepatic filtration
Blood Glucose
G(t)
receptor
binding
PN
uex
Insulin-dependent
glucose removal through
receptor-bound insulin
Q
SI(t) G
1+αGQ
Plasma
Insulin
I(t)
nI
Kidney
Hepatic
clearance clearance
nL I
1+αII
nK  I
Interstitial
Insulin
Q(t)
nC
Q
1+αGQ
Interstitial insulin
degradation
through receptor
binding
Determining insulin sensitivity
Patient data from the hospital records:
Patient 7
BG [mmol/L]
15
10
5
0
Model BG fit
0
20
40
60
80
100
2
Identified insulin sensitivity
1
I
S [L/(mU.min)]
-3
x 10
0
20
40
60
80
100
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
Dextrose [mmol/min]
0
Model
 Model-based SI
Insulin
Brain

“Whole-body” insulin sensitivity

Captures overall metabolic balance, including the
relative net effect of :

Altered endogenous glucose production

Peripheral and hepatic insulin mediated
glucose uptake



Endogenous insulin secretion
Insulin losses
(liver, kidneys)
Blood
Glucose
Glucose
Effective
insulin
ty
ivi
sit
n
se
lin
u
Ins
Has been used to guide model-based TGC in
several studies
Provides a means to analyse the evolution and
hour-to-hour variability of SI in critically ill patients
Liver
Liver
Other
cells
Plasma
Insulin
Pancreas
SI Variability

Plot on axes from t = n vs t = n+1

1-hour lag model is all that is
required and more doesn’t add
value [Lin et al, 2008, Le Compte et al, 2009]

So, what to do with this data that at
any hour clearly shows the
variation potential

Note most likely change is on the
1.0 line and is “no change”
So, at a given hour
Can be extended to 2, 3, 4, … hours forward
Models

Parsimonious models

Single critical parameter (SI)

Used to guide TGC in several (7+ over 3 countries) clinical
glycemic control trials

SI reflects patient condition and is, as one might expect, highly
variable in an hour-to-hour sense


This must be accounted for in control
This is a major, leading cause of TGC failure in many trials [Chase et al, 2011, CMPB]
Clinical Uses: STAR
 Target to Range
+(1-2)
hr
tnow+(1-3)hr
tnow
Stochastic model shows the
bounds (5th – 95th percentile)
Stochastic
shows
the (5th,
for
insulin model
sensitivity
variation
25th, 50th = median, 75th and 95th)
over next 1-3 hours from the
percentile bounds for insulin
initially identified level
Insulin sensitivity
95th
75th
50th
25th
sensitivity (SI(t)) variation over the
next time interval from the
currently identified value.
5th
tnow
tnow+(1-3)hr
Blood glucose
Stochastic model shows the
For aa given
given insulin
intervention, an
feed+insulin
boundsFor
(5th – 95BGth distribution
percentile)
output
forecast
intevention
an outputisBG
using the system model
distribution
be forecast
for insulin
sensitivitycan
variation
using the model
over next 1-3 hours from the
initially identified level
Insulin sensitivity
5th
25th
95th
50th
tnow
75th
tnow+(1-3)hr
95th
75th
50th
25th
5th
95th
50th
Stochastic model shows the
bounds (5th – 95th percentile)
for insulin sensitivity variation
over next 1-3 hours from the
BG
(mg/dL)
initially identified
level
Insulin sensitivity
75th
25th
5th
Blood glucose
Blood glucose
5th
5th
25th
50th
75th
95th
For a given feed+insulin 6.5
intevention an output BG
distribution can be forecast
using the model
4.4
25th
50th
75th
95th
For a given feed+insulin
intevention an output BG
distribution can be forecast
using the model
Time

Calculate likely range of outputs for a given insulin dose based on patient variation from
stochastic model

Align 5th percentile with clinically defined lower bound to maximise likelihood in target band
What does this mean for control?
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
80
100
20
40
60
80
100
1
2
0.5
1
Dextrose [mmol/min]
0
0
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
I
S [L/(mU.min)]
SI [L/(mU.min)]
-3
x 10
Usually, we only know the median
likely change (no change)
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
1
2
100
Insulin sensitivity might not
change much, so expect a
~constant BG response
x 10
0.5
1
I
S [L/(mU.min)]
SI [L/(mU.min)]
-3
80
0
20
40
60
80
100
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
Dextrose [mmol/min]
0
For a given insulin input w/ bounds
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
1
2
100
Insulin sensitivity might
rise suddenly, so there is a
possibility of lower BG
x 10
0.5
1
I
S [L/(mU.min)]
SI [L/(mU.min)]
-3
80
0
20
40
60
80
100
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
Dextrose [mmol/min]
0
For a given insulin input w/ bounds
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
1
2
100
Insulin sensitivity might
drop suddenly, so there
may spike in BG
x 10
0.5
1
I
S [L/(mU.min)]
SI [L/(mU.min)]
-3
80
0
20
40
60
80
100
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
Dextrose [mmol/min]
0
Ranges of outcomes
(IQR and 90%CI)
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
1
2
100
Work out the 90%
confidence range for future
insulin sensitivity and BG
values
x 10
0.5
1
I
S [L/(mU.min)]
SI [L/(mU.min)]
-3
80
0
20
40
60
80
100
0.06
1.5
0.04
1
0.02
0.5
0
0
20
40
60
Time [hours]
80
100
0
Insulin [mU/min]
Dextrose [mmol/min]
0
Goals and Outcomes

Guaranteed maximum risk of 5% forBG < 4.4 mmol/L

Stochastic forecasting (model-based) approach ensures insulin is limited
to minimise hypoglycemia, while maximising what can be done to limit
hyperglycemia

Goal: best overlap of outcome distribution with desired, safe range
STAR Controller in Practice

STAR is a model-based and computerised TGC protocol.
Designed to be a replacement for SPRINT.
STAR
Insulin and Nutrition
Patient 3 – Very variable
Patient 4 – Keeping nutrition higher
Patient 5 – Know when to say no!
+ PN  EN titration
Clinical Results
Whole cohort statistics
# patients:
Total hours:
# BG measurements:
Measures per Day
Median BG [IQR] (resampled)
% resampled BG within 4.4 - 6.5 mmol/L
% resampled BG within 4.4 - 7.0 mmol/L
% resampled BG within 4.4 - 8.0 mmol/L
% resampled BG within 8.0 - 10 mmol/L
% resampled BG > 10 mmol/L
% resampled BG < 4.4 mmol/L
% resampled BG < 4.0 mmol/L
% resampled BG < 2.22 mmol/L
# patients < 2.22 mmol/L
Median insulin rate [IQR] (U/hr):
Median glucose rate [IQR] (g/hour):

STAR Pilot Trial
13
902 hours
501
13.3
6.13
[5.60 - 6.81]
61.48
78.04
91.83
5.30
1.21
1.66
0.883
0.000
0
2.5 [1.0 - 5.5]
4.8 [0.2 - 6.4]
STAR (Predicted)
444
40101 hours
20050
12.0
6.15
[5.65 - 6.75]
63.87
78.88
90.95
5.10
1.69
2.27
0.969
0.020
6
2.5 [1.5 - 4.0]
5.0 [2.2 - 6.4]
SPRINT Clinical Data
444
39841 hours
26646
16.1
5.60
[5.00 - 6.40]
70.09
78.48
85.95
4.45
2.00
7.83
2.894
0.040
14
3.0 [2.0 - 4.0]
4.1 [1.9 - 5.6]
Results match virtual results well and show very good performance
Overall Summary

A clinically (well) validated model and virtual trial methods



STAR, a computerised version that is more flexible and
customisable and offers guaranteed risk tradeoff with
maximised performance likelihood





Proved for prediction accuracy of interventions (median < 4% error)
Proved to predict cohort outcomes in crossover virtual trials of
matched cohorts [Chase et al, BioMed Online, 2010]
Target to Range approach
Guaranteed maximum risk of 5% for BG < 4.4 mmol/L, our results
currently at ~1.5-2.0%
Direct management of inter- and intra- patient variability
Stochastic forecasting is unique to this approach
Engineering + Mathematics = Medical Practice
Acknowledgments
Geoff Chase
Jessica Lin
Fatanah Suhaimi
Chris Pretty
Ummu Jamaludin
Normy Razak
The Belgians
Geoff Shaw
Aaron Le Compte
Dr Thomas Desaive
Dr Jean-Charles
Preiser
Sophie Penning
The Hungarians: Dr Balazs Benyo, Dr Levente Kovacs, Mr Peter Szalay
and Mr Tamas Ferenci, Dr Attila Ilyes, Dr Noemi Szabo, ...
STAR Summary

Computerised control can offer far tighter control

Better intra- and inter- patient variability

Target to range

Guaranteed maximum risk of 5% for “light hypoglycemia”
of BG < 4.0 mmol/L

Clinical trials beginning now and ongoing successfully with
insulin alone and insulin + nutrition control approaches
Target to Range
 This targeting of a range ensures that the outcome BG best overlaps the 4.4-6.5 mmol/L
range safely.
 It also ensures there is less than a 5% chance of the next blood glucose being less than
4.4 mmol/L dramatically reducing moderate and severe hypoglycemia risk
STAR = Use ranges to guide
insulin input
Stochastic model:
Patient 7
BG [mmol/L]
15
10
5
0
0
20
40
60
Forecasted BG values are
used to make sure BG
doesn’t
go too 100
low
80
-3
1
2
x 10
S [L/(mU.min)]
SI [L/(mU.min)]
Bigger insulin input = wider range of outcome glycaemia all else equal
0.5
1
I
Many TGC protocols allow infusions past 10, 20 or 30 U/hour well over
level of saturation affects at 6-8 U/hour
0
0
20
40
60
80
100
0.02
0
0
20
40
60 these bounds
80
STAR = Stochastic
TARgeted
control
using
Time [hours]
0.5
100
0
Insulin [mU/min]
Dextrose [mmol/min]
It is immediately
clear that a variable patient with (increasingly) high1.5levels
0.06
of insulin – exacerbated by 2-4 hourly measurement when “in the band” –
0.04
1
will likely end up hypo’ed  and then hyper in response.