Clinical Validation of a Model-based
Glycaemic Control Design Approach and
Comparison to Other Clinical Protocols
IEEE EMBC, August 30 – September3, 2006, New York, NY
J.G. Chase et al
Dept of Mechanical Engineering
University of Canterbury
A Well Known Story
 Hyperglycaemia is prevalent in critical care

Impaired insulin production + Increased insulin resistance = High BG

Average blood glucose values > 10mmol/L are not uncommon

All due to the stress of the patient’s condition
 Tight control  better outcomes:

Reduced mortality ~17-43% (6.1-7.75 mmol/L) [van den Berghe, Krinsley]

SPRINT reduces mortality 32-45% depending on LoS in ICU (details to come)

Costly treatments (mech. ventilation, transfusions, … ) are also reduced
 However, how best to attack the problem?

How to manage highly insulin resistant patients (usually high APACHE score)?

How to provide better safety from hypoglycaemia?

Model-based methods may offer an opportunity to better design and compare
Glucose Balance & Control
• If the ability to remove excess glucose from nutrition was fully functional
• Then plasma glucose would be lower
–
–
•
Only 2 ways to reduce glucose levels:
–
–
•
Add insulin  limited by saturation effects, therefore only so much can be done
Reduce excess nutritional glucose
Several recent studies have shown that high glucose feeds in critical care are one
cause of hyperglycaemia [Patino et al, 1999; Krishnan, 2005; Weissman, 1999; Woolfson, 1980; … ]
–
–
•
Hence, any excess nutrition effectively “backs up” in the plasma
Inputs and the patients ability to utilise them are not being properly matched
Krishnan et al noted that above 66% of ACCP Guidelines increased mortality!
Patino et al kept glycaemia < 7.5 mmol/L (average) with reduced dextrose feeds
This study seeks to use both sides of the glucose balance to tightly regulate blood
glucose levels in critical care
–
–
Modulate both nutritional input and insulin bolus/infusion
Rather than a more typical normal insulin-only approach
Glucose Control = Balance
Measurement & Intervention Frequency  Size of swings in glucose
Rising Glucose
Falling Glucose
• Nutritional Inputs
• Exogenous Insulin
• Endogenous Glucose
Production
• Endogenous Insulin
• Non-insulin Removal
Control Inputs
Match inputs and ability to utilise
A Simple PK-PD Model
Glucose
compartment
G   pG G  S I G  Ge 
Q
1   GQ
Q  kQ  kI
Interstitial insulin compartment
Plasma insulin compartment
 P (t )
I  
nI
u (t )
 ex
1  I I
V
Model has been validated in 3 prior clinical studies
A Simple Nutritional Input Model

Plasma glucose rate of appearance
from stepwise enteral feed rate fluxes
P(t)


P2
Increasing stepwise enteral feed rate
fluxes

~ intestinal absorption = FAST

t1/2 = 20mins
P3
P1
P2
Decreasing stepwise enteral feed
rate fluxes

Impaired splanchnic and peripheral
glucose uptake = SLOW

t1/2 = 100mins
P(ti  t  ti 1 )  Pi 1  ( P(ti )  Pi 1 )e
 k pd ( t ti )
where Pi 1  P(ti )
 k pr ( t ti )
where Pi 1  P(ti )
P(ti  t  ti 1 )  Pi 1  ( P(ti )  Pi 1 )e
Virtual Patient Design Approach
P(t(i+1)) and u(t(i+1))
Virtual Patient Trials -
Control input/s
using
fitted long term, retrospective patient
data to create virtual patients to test
protocols in simulation
─
─
─
─
N = 19-49 patients used typically with an
average of 3-8 days stay each, which can
provide several patient years in Monte
Carlo analysis.
Ethics approval by the NZ South Island
Regional Ethics Committee (A)
SI(t) and pG(t)
SI (L/min.mU)
-3
3
Model-based targeted glucose reduction
Iterative Bisection Method: control inputs
G   pG G  S I (G  GE )
Insulin sensitivity
x 10
2
nI
u (t )
I  
 ex
1  I I
V
+
Constant feed-rate, variable insulin control protocol
6000
4000
G   pG G  S I (G  GE )
2000
0
Q
1   GQ
P(t)
Q  kQ  kI
6000
nI
u (t )
I  
 ex
1  I I
V
4000
2000
+
SI(t(i)) and pG(t(i))
 P (tu(t))
Integral-Fitting Method: time variant patient parameters
Variable feed-rate and insulin control protocol
G   pG G  S I (G  GE )
u(t)
P(t)
Generate patient glycaemic
0
Glucose-Insulin System
Hospital control
response to controller-determined
100
control inputs
50
0
Q
 P (t )
1   GQ
Q  kQ  kI
1
0
u(t) (mU/min) / P(t) (55000*mmol/L.min)
─
Tests algorithms and methods safely
Provides insight into potential long term
clinical performance
Provides relatively large, repeatable
cohort for easy comparison
Very fast  fine tuning performance and
safety schemes
Monte Carlo simulation to account for
different sensors and their errors
Controller
Retrospective patient profile of
u(t) (mU/min) / P(t) (1000*mmol/L.min)
─
─
"Virtual Patient"
0
1000
2000
3000
Model
P(t)
u(t)
4000
5000
Time (mins)
6000
7000
8000
9000
"Patient" Glycaemic
Response
G(t(i))
Q
 P (t )
1   GQ
Q  kQ  kI
nI
u (t )
I  
 ex
1  I I
V
SPRINT
•
Optimises both insulin and nutrition rates to control
glycaemic levels
•
Developed through extensive computer simulation
–
Designed to mimic computerised protocol based on effective
insulin sensitivity from prior hour
•
Simple interface for ease of use by nursing staff:
•
Mimics the very tight control of computerised simulations
with minimal implementation cost
–
(no bedside computer required…)
Protocol Comparisons
• Evaluated several published protocols
– Van den Berghe et al, Krinsley, Laver, Goldberg
– Two different sliding scales from Christchurch
• Virtual trials with N = 19 cohort
– Average APACHE II = 21.8
– Average LoS ~3days
• Monte Carlo simulation including measurement error n = 20
times per patient
• Results reported as probability density functions for
comparison
Protocol Comparisons
SPRINT
~18-45%
45%
AIC4
Density of measurements
0.4
Bath
0.35
Leuven
25%
0.3
Mayo Clinic
Yale
0.25
Sliding Scale
0.2
Bad!
Also Bad!
Aggressive sliding scale
0.15
0.1
Very
Bad!
0.05
0
0
Not Trying?
2
4
6
8
10
12
14
16
18
Blood glucose level [mmol/L]
20
Protocol Comparisons
SPRINT
AIC4
Mayo clinic
Leuven
Bath
Yale
Log Median or Mean
5.79
5.93
8.59
5.60
6.21
6.70
6.89
6.61
Multiplicative STD
1.29
1.35
1.29
1.65
1.45
1.4
1.36
1.33
68.3% range
(4.50-7.45)
(4.39-8.01)
(6.68-11.05)
(3.40-9.24)
(4.27-9.03)
(4.78-9.40)
(5.08-9.33)
(4.99-8.77)
95.5% range
(3.50-9.58)
(3.25-10.82)
(5.20-14.20)
(2.06-15.24)
(2.94-13.13)
(3.41-13.18)
(3.75-12.65)
(3.77-11.62)
Time in 4-6.1 band
61.7%
62.2%
11.2%
35.8%
45.5%
22.3%
41.9%
43.8%
Time in 4-7.75 band
83.5%
82.9%
27.4%
51.0%
70.0%
64.8%
60.0%
65.2%
Time less than 4
4.4%
1.1%
0.6%
23.6%
7.1%
5.9%
2.4%
2.8%
Time higher than 7.75
12.1%
16.1%
72.0%
25.3%
22.9%
29.3%
37.5%
32.0%
Average insulin (U/hr)
2.4
2.6
1.6
3.0
5.8
4.6
1.9
2.1
Average % feed of goal
61.9%
75.8%
67.7%
67.7%
71.8%
71.4%
67.7%
67.7%
Similar and tightest
Sliding scale Agg. sliding
Clear 2nd
Note virtual trial cohort APACHE II scores are
much higher than in many protocols
Clinical vs. Simulation Results
Note: SPRINT trial data for first 8613
measurements, ~90 patients.
All simulated results were for the 19 virtual
patients, ~1700 hours of trials
Time in Band and CDF
Very tight control puts high percentage in bands
Percentiles for ICU data- SPRINT
2.5mmol/L = 4.1x 10-5
Cumulative probability
3.0mmol/L = 0.001
4.0mmol/L = 0.041
6.1mmol/L = 0.59
7.0mmol/L = 0.81
7.75mmol/L =0.91
> 90%
SPRINT ICU raw data- 26-04-06
ICU data- SPRINT (lognormal) 26-04-06
Model simulation- SPRINT (lognormal)
Model simulation- van den Berghe (lognormal)
Model simulation- Krinsley
Simulated vs. Clinical differences smaller here
Glucose mmol/ L
Performance Outcomes
Tightness of glucose control: the first 165 patients
Average BG
Average time in 4 -6.1
Average time in 4 -7
Average time in 4 -7.75
Percentage of all measurements less than 4
Percentage of all measurements less than 2.5
Average hourly insulin
Average percentage of goal feed
Average feed rate
(assuming 1.06 cal/ml for feed)
5.8 mmol/L
61%
82%
89%
3.3%
0.1%
2.9 U
73%
56.3 ml/hr
1431 cal/day
All performance indicators agree with
simulation and tight control!
Protocol is safe – no clinically significant
hypoglycaemia
Effective use of insulin and nutrition
Sepsis:
• A major cause of mortality and significant clinical issue that can be addressed with tight control
• Mortality from Sepsis is down -46%
• For LoS > 3 days mortality with sepsis is down -51% (for LoS < 3 it is down -10%)
• Analysis is still rough and not yet significant, although trends appear stable at this time
• More importantly, no “Breakthrough Sepsis” reported yet, i.e. no new sepsis once under control
Mortality after ~25k Hours
• Reductions in mortality for patients with length of stay >= 3 days
• Cohorts are well matched for APACHE II and APACHE III diagnosis
ICU mortality reduced 32%
p = 0.03
30%
2004-05
Mortality %
25%
APACHE II
20%
15%
10%
5%
44 deaths in
168 patients
21 deaths in
119 patients
0%
2004-05
SPRINT
SPRINT
Total
Mortality
Total
Mortality
1-14
19
1
14
2
15-24
80
22
49
13
25-34
27
10
25
3
35+
1
0
5
3
~75% of APACHE II scores available in both cases
Rest of levels are not significant as yet
• Reductions are large, particularly compared to ROD for APACHE II
• Almost all reductions are in the more critically ill (APACHE II: 25-34, p = 0.02) - as expected?
Summary & Conclusions
• Development of an Insulin+Nutrition control approach
• Virtual Trials control design method
– Good correlation with published results for other protocols
– Other protocols do not appear to provide as tight a control as modelbased methods (e.g. Blank et al and others)
• SPRINT is a simpler version mimicking the computerized and
model-based method – for ease of clinical validation
• SPRINT results include:
– Reduced ICU and hospital mortality by ~29-36% for patients with LoS >
3 days – Bigger reductions at LoS > 4 and 5 days
– Very tight control shown with this approach
• Model-based approaches present a sure method of designing
safe, effective and optimal control for this and similar problems
Acknowledgements
AIC1
Jessica Lin & AIC3
AIC2
Jason Wong & AIC4
AIC5: Mike, Aaron and Tim
Dunedin
Thomas Lotz
Assoc. Prof. Geoff
Chase
The Danes
Prof Steen
Andreassen
Dr Kirsten
McAuley
Prof Jim Mann
Maths and Stats Gurus
Dr Dom Lee
Dr Bob
Broughton
Prof
Graeme Wake
Dr Chris Hann
Acknowledgements
Intensive Care Unit Nursing Staff
Christchurch Hospital
Cohort Match
APACHE II score
Risk of death
2004-05
SPRINT
(78% available)
(76% available)
21.3
20.1
33%
39%
37
36
15
12
15
1
0
0
8
3
0
0
16
23
17
8
11
0
0
1
8
2
0
1
127
87
APACHE II similar
APACHE III diagnosis
Cardiovascular
Respiratory
Gastrointestinal
Neurological
Trauma
Renal
Gynaecologic
Orthopaedic
Sepsis
Metabolic
Haematologic
Other
Total
Distribution of
diagnoses similar
between cohorts
Mortality vs. LoS
• Statistical significance for a reduction in mortality
for different length of ICU stay
– SPRINT reductions in mortality more evident for patients that
stay in ICU longer.
z-test
LOS > =2
LOS > =3
LOS > =4
LOS > =5
Fisher test
LOS > =2
LOS > =3
LOS > =4
LOS > =5
p-value
ICU
Hospital
0.13
0.15
0.03
0.03
0.02
0.01
0.02
0.01
p-value
ICU
Hospital
0.16
0.19
0.04
0.04
0.03
0.02
0.03
0.02
Both ICU mortality and inhospital mortality
significant for lengths of
stay greater than 3 days.
Survival Curves - ICU
Kaplan-Meier Survival Curve - ICU Mortality, SPRINT vs. 2004-05
Kaplan-Meier Method
Censoring Column in ICUMort - SPRINT, ICUMort - 2004-05
100
Variable
SPRINT - ICU Mortality
2004-05 - ICU Mortality
90
Table of S tatistics
M ean M edian IQ R
14.9914
*
*
18.4822
22.2
*
Percent
80
70
60
SPRINT
50
2004-05
40
0
5
10
15
20
Time (days)
p = 0.002 for a difference between curves
25
30
Survival Curves - Hospital
Kaplan-Meier Survival Curve - Hospital Mortality, SPRINT vs 2004-05
Kaplan-Meier Method
Censoring Column in HospMort - SPRINT, HospMort-04-05
100
Variable
SPRINT - Hospital Mortality
2004-05 - Hospital Mortality
90
Percent
80
Table of S tatistics
M ean M edian IQ R
*
*
17.0955
14.2 23.9
15.7395
70
60
SPRINT
50
40
2004-05
30
0
5
10
20
15
Time (days)
p < 0.002 for a difference between curves
25
30
BG Distribution - Overall
Distribution of BG Means
BG Variation
Time to Band – Hourly Average BG
Note: each hourly BG distribution is lognormal so
those statistics are used. Bars are 95% intervals.
7.75 mmol/L
6.1 mmol/L
~3.75 mmol/L minimum
Number of respondents
Nursing Survey: SPRINT
15
Very Good
10
Good
Satisfactory
5
Poor
0
Ease of Use
Quality
Suitability
23.6% of simulated van den Berghe
measurements < 4mmol/L
2.6% of SPRINT clinical
measurements < 4mmol/L
0.6% of simulated Krinsley
measurements < 4mmol/L
70% of simulated Krinsley
measurements > 7.75 mmol/L
10% of SPRINT ICU measurements
> 7.75 mmol/L
25.3% of simulated van den Berghe
measurements > 7.75 mmol/L