Introduction Clinical Setting - Glucoregulatory system - Patients and Data Assessment Proc. - Glucose sensors - Glycemia control system Control System

Introduction

Clinical Setting - Glucoregulatory system - Patients and Data

Assessment Proc. - Glucose sensors - Glycemia control system

Control System - Black-Box model - Grey-Box model - Controller

Conclusions

Ph.D. Defense Tom Van Herpe April 15, 2008

CONTROL SYSTEM

GLYCEMIA CONTROL SYSTEM

PATIENT MODEL

CONTROLLER

ACTUATOR SENSOR

Known input variables

Introduction




Conclusions


Patient model ?


PATIENT MODEL

CONTROLLER

ACTUATOR SENSOR


A = B + (C * D)C = E + F(B+G)

E = … White-Box

Grey-Box

Black-Box

Introduction




Conclusions


Black-Box model structure

Data are used for1. Model structure2. Model estimation

Black-Box

Introduction




Conclusions



Pat 2

Data set 1

Permutation 1

Data set size

Estim

atio

n (3

0 pa

tient

s)

Pat 9

Pat 22

Pat 38

Pat 10

. . .

Pat 30

Data set size

Estim

atio

n (3

0 pa

tient

s)Pat 3

Pat 25

Pat 9

. . .

. . .

Pat 4

Data set size

Estim

atio

n (3

0 pa

tient

s)

Pat 38

Pat 15

. . .

Pat 41. . .Pat 22

Pat 9

Data set size

Test

(11

patie

nts)

Pat 20

Pat 40

. . .

Data set size

Test

(11

patie

nts)

Pat 5

. . .

Pat 39

Data set size

Test

(11

pat

ient

s)

Pat 1

. . .

. . .

Pat 25

Pat 10

Pat 41

Pat 3

Optimal model order (na*)

Msel(na) 1 Msel(na) 2 Msel(na) 500Msel(na) ...

Permutation 500Permutation 2

Introduction




Conclusions


Initial and adaptive input-output modelling

Patient 2

Data set 2

Permutation 1 Permutation 2 Permutation 500

Estim

ation

(8 p

atien

ts)

Patient 14

Patient 8

Patient 15

Patient 10

. . .

Patient 4

Estim

ation

(8 p

atien

ts)

Patient 3

Patient 1

Patient 14

. . .

. . .

Patient 4

Estim

ation

(8 p

atien

ts)

Patient 3

Patient 15

. . .

Patient 6. . .Patient 8

Patient 14

Test

(4 p

atien

ts)

Patient 12

Patient 11

Test

(4 p

atien

ts)

Patient 5

Patient 10

Test

(4 p

atien

ts)

Patient 9

. . .

Patient 1 Patient 6

Φopt

Patient 3 Patient 12

Patient 9

Patient 2

Patient 13

Initial model 1 Initial model 2 Initial model 500Initial model ...

Data set size

Valid

ation

(3 p

atien

ts)

Patient 7 . . .

Patient 5

Patient 13 Valid

ation

(3 p

atien

ts)

Patient 10

Patient 15

Patient 2

Data set size Data set size

Valid

ation

(3 p

atien

ts)

Patient 1

Patient 7

Patient 12

Data set size Data set size Data set size

Data set size Data set size Data set size

Introduction




Conclusions



Data are used for1. Model structure2. Model estimation

Black-Box

Acceptable model prediction performance

BUT RESERVATIONS:Not to be used for CONTROL purposes in clinical real-life due to underestimation of input (insulin) coefficients

==> CLOSED-LOOP DATA ~ “perfect control”

Grey-Box

?!!!

Introduction




Conclusions


Intensive Care Unit - Minimal Model (ICU-MM)

Model structure features: Endogenous (I2) and Exogenous (FI) insulin 2 input variables: Exogenous insulin (FI) + Carbohydrate calories (FG) 7 patient parameters to be estimated

Introduction




Conclusions


Intensive Care Unit - Minimal Model (ICU-MM)

Model structure based on: Minimal model [Bergman et al., 1981] Type I diabetes minimal model [Furler et al., 1985]

plasma glucose

effect of insulin on net glucose disappearance

glucose effectiveness (fractional clearance) (P1 < 0)

fractional rate of net remote insulin disappearance (P2 < 0)

fractional rate of insulin dependent increase (P3 > 0)

meal glucose disturbance

exogenous insulin

plasma insulineffect of endogenous insulin

Adaptive ICU-MM

Introduction




Conclusions


Data set 4

19

pa

tie

nts

Patient 1 Estimation data set

24 hours 24 hours

Initial modelfor patient 1 Validation:

P = 1 or 4 hour(s)

Estimation data k

Initial modelfor patient 1

B I T

Tekst

Estimation data set B I T

Estimation data set

24 hours 24 hours

Data set size

. .

.

Estimation data set

. .

.

. .

.

. .

.

. .

.

Patient 2

Patient 19

Re-estimated model 1

Re-estimatedmodel 1

Re-estimatedmodel 2

0 500 1000 1500 2000 2500

100

150

200

G (

mg

/dl)

0 500 1000 1500 2000 2500

100

150

200

G (

mg

/dl)

0 500 1000 1500 2000 25000

2

4x 10

5

FI (

U/m

in)

0 500 1000 1500 2000 2500100

200

300

FG

(m

g/m

in)

t (min)

Introduction




Conclusions


Patient case study (patient no. 10)

P = 4 hrs

P = 1 hr

Introduction




Conclusions


Model evaluation

“Optimal” re-estimation procedure: Model updates every 4 hours based on last 4 hours - data Model updates every hour based on last 5 hours - data

Average model prediction performance (per patient):

0.2

0.4

0.6

0.8

1

1.2

1.4

RM

Sn

E (

-)

P = 4 hours0.2

0.4

0.6

0.8

1

1.2

1.4

RM

Sn

E (

-)

P = 1 hour

RMSnE ≤ 1

clinically acceptable(ISO)

Introduction




Conclusions


CONTROL SYSTEM


PATIENT MODEL

CONTROLLER

ACTUATOR SENSOR


Introduction




Conclusions


Controller example: how to navigate a ship?

Model based Predictive Control

I need my MODEL !!!A = B + (C * D)C = E + F(B+G)

E = …

Feedback control

Error…?! oooops

Introduction




Conclusions


Model based Predictive Control (MPC)

MPC is a control paradigm which, based on a dynamic model of the system to be controlled, solves a mathematical optimization problem in order to find the optimal sequence of input signals within a finite future time window of length N, after which only the first input signal is applied to the system.

Constraints in the optimization problem (e.g., 0 ≤ FI ≤ max insulin flow) Flexibility with “adaptive” models (to capture varying patient dynamics) Future known disturbances (prevention of deviations from normoglycemia)

Introduction




Conclusions


MPC simulation study

Design settings: Cost function

Prediction horizon = N = 4 hours Known disturbance input = FG = carbohydrate calories Unknown disturbance inputs (medication + 15% meas. error)

Simulation results (19 critically ill patients):

Median GPI 25% - 75% IQ range for GPI

One-hour-period simulations: 9 6 - 15

Four-hours-period simulations: 12 8 - 15

GPI ≤ 23 ==> “clinically acceptable”

Introduction




Conclusions


Patient case study (patient no. 11)

0 100 200 300 400 500 600 700 800 900 100050

100

150

G (

mg

/dl)

0 100 200 300 400 500 600 700 800 900 1000

240

245

250

255

FG

(m

g/m

in)

0 100 200 300 400 500 600 700 800 900 10000

5

10

15x 10

4

FI (

U/m

in)

0 100 200 300 400 500 600 700 800 900 10000

1

2

FM

(m

g/d

l/min

)

t (min)

Introduction




Conclusions


CONCLUSION FOR OBJECTIVE 3

Black-Box model

Acceptable model prediction performance Closed-loop data affect model structure and model estimation NOT for use in a clinical real-life control system

Publications: T. Van Herpe, M. Espinoza, B. Pluymers, I. Goethals, P. Wouters, G. Van den Berghe, and B. De Moor. An adaptive input-output modeling approach for predicting the glycemia of critically ill patients. Physiol. Meas., 27(11):1057–1069, 2006.

T. Van Herpe, M. Espinoza, B. Pluymers, P. Wouters, F. De Smet, G. Van den Berghe, and B. De Moor. Development of a critically ill patient input-output model. In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), Newcastle, Australia, pages 481-486, 2006.

T. Van Herpe, I. Goethals, B. Pluymers, F. De Smet, P. Wouters, G. Van den Berghe, and B. De Moor. Challenges in data-based patient modeling for glycemia control in ICU-patients. In Proceedings of the Third IASTED International Conference on Biomedical Engineering, Innsbrück, Austria, pages 685-690, 2005.

Black-Box

Introduction




Conclusions



Grey-Box model

New model structure based on physiological insight: ICU-MM Closed-loop data only used for model estimation Adaptive modelling strategy Acceptable model prediction performance Potential use in a clinical real-life control system

Publications: T. Van Herpe, M. Espinoza, N. Haverbeke, B. De Moor, and G. Van den Berghe. Glycemia prediction in critically ill patients using an adaptive modeling approach. J. Diabetes. Sci. Technol., 1(3):348–356, 2007.

T. Van Herpe, B. Pluymers, M. Espinoza, G. Van den Berghe, and B. De Moor. A minimal model for glycemia control in critically ill patients. In Proceedings of the 28th IEEE EMBS Annual International Conference (EMBC 06), New York, United States, pages 5432-5435, 2006.

T. Van Herpe, N. Haverbeke, M. Espinoza, G. Van den Berghe, and B. De Moor. Adaptive modeling for control of glycemia in critically ill patients. In Proceedings of the 10th International IFAC Symposium on Computer Applications in Biotechnology (CAB 07), Cancún, Mexico, Vol. I, pages 159–164, 2007.

Grey-Box

Introduction




Conclusions



Controller

Critical review of currently available blood glucose algorithms Design of MPC MPC performance increases if insulin infusion rate can be adapted more frequently

Publications: T. Van Herpe, N. Haverbeke, B. Pluymers, G. Van den Berghe, and B. De Moor. The application of Model Predictive Control to normalize glycemia of critically ill patients. In Proceedings of the European Control Conference 2007 (ECC 07), Kos, Greece, pages 3116–3123, 2007.

N. Haverbeke, T. Van Herpe, M. Diehl, G. Van den Berghe, B. De Moor. Nonlinear model predictive control with moving horizon state and disturbance estimation - Application to the normalization of blood glucose in the critically ill. Accepted for publication in Proceedings of the 17th IFAC World Congress (IFAC WC 08), Seoul, Korea, 2008.

Introduction




Conclusions


GENERAL CONCLUSIONS

Three main objectives:

1. Design of evaluation tool for glucose sensors:GLYCENSIT procedure

2. Design of evaluation tool for blood glucose control algorithms used in the ICU:

Glycemic Penalty Index

3. Design of (semi-)automatic control system for normalizing blood glucose in the ICU:

ICU-MM & MPC

Introduction




Conclusions


FUTURE RESEARCH

New inspiration

Patient Database Management System (PDMS)

Introduction




Conclusions


FUTURE RESEARCH

New inspiration

Near-continuous glucose sensor

0 300 600 900 1200 1500 1800 210090

100

110

120

140

160

180

200

220

Time (min)

Blo

od

glu

cose

(m

g/d

l)

0 300 600 900 1200 1500 1800 210090

100

110

120

140

160

180

200

220

Time (min)

Blo

od

glu

cose

(m

g/d

l)

0 300 600 900 1200 1500 1800 210090

100

110

120

140

160

180

200

220

Time (min)

Blo

od

glu

cose

(m

g/d

l)

Introduction




Conclusions


FUTURE RESEARCH

Five future research topics:

Introduction




Conclusions


FUTURE RESEARCH


1. Optimization of GPI

0

20

40

60

80

100

120

140

160

0 5 10 15 20 25 30 35 40

Time (min)

Blo

od

glu

co

se (

mg

/dl)

Time (min)

Introduction




Conclusions


FUTURE RESEARCH

0

20

40

60

80

100

120

140

160

0 5 10 15 20 25 30 35 40

Time (min)

Blo

od

glu

cose

(m

g/d

l)

50 74 100 120 150 200 250 3000

10

23

30

40

50

60

70

80

90

100

G (mg/dl)

Pena

lty (-

)

= 1

= 2

= 3

= 0

Optimization of GPI

Time (min)

5 10 15 20 25 30 35 40

Time (min)

Fre

qu

en

cy

5 10 15 20 25 30 35 40

Time (min)

Fre

qu

en

cy

Fre

qu

en

cy

Time (min)


1. Optimization of GPI

Introduction




Conclusions


FUTURE RESEARCH

2. Assessment of near-continuous glucose sensors

Quality requirements for individual measurement can be lower GLYCENSIT version 2

3. Modelling of glycemia Critically ill rabbits ==> improved dynamic behaviour

PDMS ==> patient clustering ==> initial model per cluster


Introduction




Conclusions


FUTURE RESEARCH

5. Clinical validation of a glycemia control system

Testing the semi- or fully-closed-loop control system on group of critically ill rabbits Testing the semi-closed-loop control system on critically ill patients: advising system Testing the fully-closed-loop control system on critically ill patients

4. Control of glycemia Recognition of glucose sensor failings (introduction of tolerance intervals of GLYCENSIT phase 3) Robustness analysis of the developed glycemia control system


The End

Documents

Introduction Clinical Setting - Glucoregulatory system - Patients and Data Assessment Proc. - Glucose sensors - Glycemia control system Control System