Real-time control
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Real-time control Model-Based Blood Glucose Control for Neonatal Intensive Care Engineering robust solutions for our most fragile infants Background and Aims Premature, low-birth-weight infants represent a significant proportion of the neonatal intensive care population. Glucose is a primary source of energy, however the body’s glucose control mechanisms are often under-developed in premature infants. Hyperglycaemia (high blood sugar levels) occurs in 40%-80% of very low birth weight infants. This condition has been linked to increased mortality and: • Reduced immune system performance and sepsis • Electrolyte imbalance and osmotic diuresis • Retinopathy of prematurity (affecting vision) There is currently no fixed method for controlling hyperglycaemia in neonatal intensive care. Glucose restriction is often used, but this deprives the neonate of an important energy source for growth. Insulin therapy can lower blood sugar and promote growth, but increases the risk of dangerous low blood sugar levels. Tests commonly performed on adults are difficult for the fragile neonate. A validated model of the neonatal glucose system can be used to create “virtual patients” to test control schemes on computer before real-life trials are commenced. Aaron Le Compte, Centre for Bioengineering Methodology A model based on adult models of the glucose system is modified to capture the fundamental dynamics of the neonate. Retrospective clinical data is used to generate virtual patient profiles, which are used to simulate patient responses to glucose and insulin inputs. Simulated trials can be performed on the virtual patients to simulate the effects of different glucose and insulin control schemes in the safety of a computerised trial. Stochastic and time-series modelling of insulin sensitivity is used to provide confidence bands for blood glucose predictions to allow clinicians to assess the safety of proposed dosing recommendations. Results and Conclusions Hospita l records Simulate d control Change Measurements in target band (4.0 - 6.0 mmol/L) 19.0% 64.5% + 239% Mean BG 8.3 5.2 - 37% BG standard deviation 3.0 1.2 - 60% Stress responses, drug therapies 2000 3000 4000 5000 6000 7000 8000 0 2 4 6 8 10 12 BG [mmol/L] 2000 3000 4000 5000 6000 7000 8000 0 4000 2000 3000 4000 5000 6000 7000 8000 0 6000 Tim e [m ins] H ospitalcontrol Sim ulated control D extrose infusion Insulin infusion Model has been validated on over 3,500 hours of retrospective data. Control simulations show 239% improvement in target measurements compared to hospital control Parameter identification Retrospective data Before – high BG After – on target •90% of simulated 1-hour predictions within 20% of actual measurement. •Prediction error at 1-hour, 2- hour and 4-hour intervals of 6.3%, 10.4% and 14.6% - similar to error inherent in BG measurement devices. •88% of simulated predictions within 5% - 95% prediction confidence range. Figure 1: Simulated BG trial compared to hospital control Figure 2: Distribution of blood glucose measurements on hospital control and simulated trials. Figure 3: Blood glucose prediction intervals (top) and insulin sensitivity predictions bands (bottom) generated by stochastic model. 0 500 1000 1500 2000 2500 3000 0 5 10 B G [mmol/L] 0 500 1000 1500 2000 2500 3000 0 1 2 3 x 10 -3 Tim e [m ins] Insulin sensitivity 5% -95% predictions IQ R predictions M odel-fitted sI ) ( ) ( 1 . . . t V CNS P t P Q Q G S G p G G END G I G kI kQ Q B t u k I ex I I e V t u I nI I ex I )) ( ( ) ( 1 System model Brain Other cells Insulin losses (liver, kidneys) Glucose Insulin Liver Blood Glucose Liver Insulin sensitivity Effective insulin Plasma Insulin Pancreas Virtual in-silico trials 0 5 10 15 20 0 0.2 0.4 P roportion ofm easurements 0 5 10 15 20 0 0.2 0.4 B G [mmol/L] Before: wide BG spread After: tight BG spread
description
Insulin. Brain. Plasma Insulin. Insulin losses (liver, kidneys). Blood Glucose. Effective insulin. Pancreas. Glucose. Insulin sensitivity. Liver. Other cells. Retrospective data. Real-time control. Virtual in-silico trials. Before: wide BG spread. After: tight BG spread. - PowerPoint PPT Presentation
Transcript of Real-time control
Real-time control
Model-Based Blood Glucose Control for
Neonatal Intensive CareEngineering robust solutions for our most fragile infants
Background and AimsPremature, low-birth-weight infants represent a significant proportion of the neonatal intensive care population. Glucose is a primary source of energy, however the body’s glucose control mechanisms are often under-developed in premature infants.
Hyperglycaemia (high blood sugar levels) occurs in 40%-80%of very low birth weight infants. This condition has been linkedto increased mortality and:
• Reduced immune system performance and sepsis• Electrolyte imbalance and osmotic diuresis• Retinopathy of prematurity (affecting vision)
There is currently no fixed method for controlling hyperglycaemia in neonatal intensive care. Glucose restriction is often used, but this deprives the neonate of an important energy source for growth. Insulin therapy can lower blood sugar and promote growth, but increases the risk of dangerous low blood sugar levels.
Tests commonly performed on adults are difficult for the fragile neonate. A validated model of the neonatal glucose system can be used to create “virtual patients” to test control schemes on computer before real-life trials are commenced.
Aaron Le Compte, Centre for Bioengineering
MethodologyA model based on adult models of the glucose system is modified to capture the fundamental dynamics of the neonate. Retrospective clinical data is used to generate virtual patient profiles, which are used to simulate patient responses to glucose and insulin inputs. Simulated trials can be performed on the virtual patients to simulate the effects of different glucose and insulin control schemes in the safety of a computerised trial.
Stochastic and time-series modelling of insulin sensitivity is used to provide confidence bands for blood glucose predictions to allow clinicians to assess the safety of proposed dosing recommendations.
Results and Conclusions
Hospital records
Simulated control Change
Measurements in target band (4.0 - 6.0 mmol/L) 19.0% 64.5% + 239%
Mean BG 8.3 5.2 - 37%BG standard deviation 3.0 1.2 - 60%
Stress responses, drug therapies
2000 3000 4000 5000 6000 7000 80000
2
4
6
8
10
12
BG
[mm
ol/L
]
2000 3000 4000 5000 6000 7000 80000
4000
2000 3000 4000 5000 6000 7000 80000
6000
Time [mins]
Hospital controlSimulated control
Dextrose infusion
Insulin infusion
Model has been validated on over 3,500 hours of retrospective data. Control simulations show 239% improvement in target measurements compared to hospital control
Parameter identification
Retrospective data
Before – high BG
After – on target
•90% of simulated 1-hour predictions within 20% of actual measurement.
•Prediction error at 1-hour, 2-hour and 4-hour intervals of 6.3%, 10.4% and 14.6% - similar to error inherent in BG measurement devices.
•88% of simulated predictions within 5% - 95% prediction confidence range.
Figure 1: Simulated BG trial compared to hospital control
Figure 2: Distribution of blood glucose measurements on hospital control and simulated trials.
Figure 3: Blood glucose prediction intervals (top) and insulin sensitivity predictions bands (bottom) generated by stochastic model.
0 500 1000 1500 2000 2500 30000
5
10
BG
[mm
ol/L
]
0 500 1000 1500 2000 2500 30000
1
2
3x 10
-3
Time [mins]
Insu
lin s
ensi
tivity
5% - 95% predictionsIQR predictionsModel-fitted sI
)()(
1...
tVCNSPtP
QQGSGpG
G
END
GIG
kIkQQ
Btuk
I
ex
I
IeVtu
InII exI ))(()(
1
System model
Brain
Othercells
Insulin losses (liver, kidneys)
Glucose
Insulin
Liver
BloodGlucose
Liver
Insulin sensitivity
Effective insulin
PlasmaInsulin
Pancreas
Virtualin-silico trials
0 5 10 15 200
0.2
0.4
Pro
port
ion
of m
easu
rem
ents
0 5 10 15 200
0.2
0.4
BG [mmol/L]
Before:wide BG spread
After:tight BG spread
