1. Evaluation of a plasma Insulin Model for Glycaemic Control in Intensive Care
Jennifer Dickson, Felicity Thomas, Chris Pretty, Kent Stewart, Geoffrey Shaw, J. Geoffrey Chase

2. Motivation Hyperglycaemia is prevalent in critical care
Impaired insulin production and increased insulin resistance lead to high blood glucose (BG)
Average blood glucose values > 10mmol/L are not uncommon
All due to the stress of the patient’s condition
Exacerbated by natural positive feedback mechanisms
Glycaemic control may save both lives and money
Some studies suggest maintaining normo-glycaemia has a number of significant benefits
Reduced mortality (up to 45% [van den Berghe, Krinsley]) and morbidity
Shorter ICU stays and reduced overall costs (Savings of $ per patient treated [van den Berghe, Krinsley])
Costly treatments & tests (mech. ventilation, transfusions, … ) are also reduced

3. Insulin can be used to reduce blood glucose levels
Motivation
Insulin can be used to reduce blood glucose levels
But hypoglycaemia (very low blood glucose) is dangerous
Tight Glucose Control
Hypoglycaemia

4. Insulin can be used to reduce blood glucose levels
Motivation
Insulin can be used to reduce blood glucose levels
But hypoglycaemia (very low blood glucose) is dangerous
However:
Required treatment dose is highly patient specific
Current methods for dosing insulin use cohort wide sliding scales or are ad hoc, often resulting in poor control and hypoglycaemia
Tight Glucose Control
Hypoglycaemia
Tight Glucose Control
Hypoglycaemia

5. Motivation Model based glycaemic control for intensive care patients
Model based: use mathematics to describe and predict glucose and insulin response to therapy
Use statistics to predict future blood glucose outcomes
Quantify and manage the risk of hypoglycaemia
Blood Glucose
Too high = bad
Target range
95th BG
5th BG
LIKELY BG
OUTCOME RANGE
Too low = bad
Time
Time now
Time next

6. Motivation Model based glycaemic control for intensive care patients
Model based: use mathematics to describe and predict glucose and insulin response to therapy
Use statistics to predict future blood glucose outcomes
Quantify and manage the risk of hypoglycaemia
Aim: Balance the risks and benefits of tight glycaemic control
Tight Glucose Control
Hypoglycaemia

7. Model-Based Glycaemic Control
Effective Model-Based Glycaemic Control must:
Have a good model
Sufficiently describing key physiology, mechanisms, and pathways
Mathematically identifiable using common clinical measures
Clinically identifiable
and useful
Physiologically
accurate
Approximations
and assumptions
Requires dense and
detailed measurements

8. Objectives Aim: evaluate performance of a glucose-insulin model
Model based off the minimal model (Bergman et al)
Required measurements: blood glucose concentrations
Methods: Using plasma Insulin measurements from another study
Evaluate dynamic and steady state performance of insulin kinetic model

9. ICING model Equations Endogenous and Exogenous Insulin
Liver Production
Parenteral Nutrition
Enteral Nutrition
Cellular degradation
Liver and kidney clearance
Kidney clearance
Central Nervous System

10. Sepsis Study Patient Cohort
19 patients enrolled in a prospective clinical trial studying sepsis
Age≥ 16 years
Expected Survival ≥ 72 hours & expected ICU stay ≥ 48 hours
Suspected Sepsis or SIRS score ≥ 3
Entry to SPRINT glycaemic control protocol
Patients received insulin under the SPRINT protocol
Insulin boluses and nutrition modulation to target 4 – 7 mmol/L N 19
Age (years)
68 [57-75]
Gender (M/F)
10/9
APACHE II score
22.0 [ ]
Confirmed Sepsis
79%
Hospital mortality (L/D)
(13/6)
Diagnosed T2DM 3

11. Study Protocol Two sets of samples (4 blood samples each):
Sample Set 1: At commencement of SPRINT Protocol
Sample Set 2: when patient consistently met <2 of SIRS criteria
t = 0
Insulin Bolus
Blood Samples
t = -1
t = 10
t = 40
t = 60min

12. Analysis of Model Accuracy Two errors analysed
Vertical Error: Direct difference with model solution
Perpendicular Error: Takes into consideration timing errors

13. Liver and kidney clearance
ICING model Equations
Cellular degradation
Liver and kidney clearance
To test sensitivity of insulin dynamics to clearance parameter values:
nL, nK, nI and nC were multiplied by a constant, ξ
ξ allowed to range between 0.1 and 3.0 to find minimum perpendicular and vertical error

14. Time since TGC onset [hrs] C-peptide concentration [pmol/L]
Results
Time since TGC onset [hrs]
Insulin
[mU/L]
C-peptide concentration [pmol/L]
Sample set 1 -
24.0 [10.4 – 52.7]
2050 [993 – 2770]
Sample set 2
84 [77-142]
20.9 [7.9 – 42.9]
758[487 – 1052]
All
20.9 [8.6 – 51.4]
1270 [558 – 2345]
C-peptide concentration significantly higher in Sample Set 2 (p<<0.001)
Either insulin secretion was higher
Or kidney C-peptide (but not insulin) clearance was lowered

15. Results Insulin kinetics of model likely slower than reality
Huge variability between patients and sample sets
ICING model kinetics fall within what might be clinically observed

16. Analysis of Model Accuracy
Model fit to insulin assay data for different insulin clearance parameters. Data is Median [IQR].
ξ=1.0
Minimum error
RMS
Vertical error [mU/L]
Perpendicular error ξ
Vertical error
Sample set 1
202 [116.2 – 454.3]
24.8 [18.9 – 71.7]
2.4 [1.1 – 2.7]
158.6 [64.6 –318.9]
16.0 [11.7 – 31.2]
Sample set 2
87.4 [ ]
18.6 [13.7 – 33.2]
1.8 [1.3 – 2.3]
62.1 [29.6 – 128.6]
11.3 [ 4.7 – 18.0]
All
123.7 [89.2 – 234.7]
22.7 [15.4 – 37.9]
2.1 [1.3 – 2.7]
97.1 [43.5 – 192.5]
13.3 [9.3 – 19.8]
ξ > 1 suggests that insulin clearance is higher than modelled
Insulin clearance is on average faster in the first sample set (septic) than the second (SIRS score <2)
In septic patients (at least) one or more insulin clearance dynamics is significantly faster than currently modelled

17. Analysis of Model Accuracy
Model fit to insulin assay data for different insulin clearance parameters. Data is Median [IQR].
ξ=1.0
Minimum error
RMS
Vertical error [mU/L]
Perpendicular error ξ
Vertical error
Sample set 1
202 [116.2 – 454.3]
24.8 [18.9 – 71.7]
2.4 [1.1 – 2.7]
158.6 [64.6 –318.9]
16.0 [11.7 – 31.2]
Sample set 2
87.4 [ ]
18.6 [13.7 – 33.2]
1.8 [1.3 – 2.3]
62.1 [29.6 – 128.6]
11.3 [ 4.7 – 18.0]
All
123.7 [89.2 – 234.7]
22.7 [15.4 – 37.9]
2.1 [1.3 – 2.7]
97.1 [43.5 – 192.5]
13.3 [9.3 – 19.8]
Insulin clearance is on average higher in the first sample set (septic) than the second (SIRS score <2)
Higher inter-patient variability in Sample Set 1 (septic)
Better model fit in Sample set 2 (SIRS score <2)
When patients are most ill they are most variable!

18. Analysis of Model Accuracy Perpendicular error:
Scale of X axis makes a difference (here in min)
But: much lower perpendicular error means slight changes/mismatches in timing significantly affect model error 62 11

19. Results
Sampling regime did not optimally capture dynamics:  5, 10, 20 and 60 minutes would have more clearly shown insulin clearance dynamics

20. Discussion Original model formulation:
Grid search for clearance parameters over likely physiological range
Aim: minimise blood glucose fitting error
Blood glucose measurements less frequent (~ every 2 – 3 hours)
Thus original model formulation better suited for long term dynamics than short term dynamics
Insulin clearance could thus be faster than currently modelled
BUT: It seems clearance depends on patient condition

21. Conclusions
Modelled insulin dynamics fall within what is clinically observed
High inter-patient variability in clearance dynamics
In generally insulin clearances should be faster than currently modelled in this cohort
Insulin clearance seems to be higher when a patient is septic
Septic cohort only one of several ICU cohorts, the model needs to adequately capture a wide cohort
Future work: Analyse model insulin fit in other ICU cohorts
Is the faster Insulin clearance dynamics a function of sepsis only?

22. Acknowledgements Felicity Thomas Kent Stewart Dr Chris Pretty
Dr Geoff Shaw
Prof. Geoff Chase

23. Any Questions?