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

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Presentation transcript:

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

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 Patient model ? A = B + (C * D) C = E + F (B+G) E = … White-Box Grey-Box Black-Box

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 Black-Box model structure Data are used for 1. Model structure 2. Model estimation Black-Box

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 Black-Box model structure

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 Initial and adaptive input-output modelling

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 Black-Box model structure Data are used for 1. Model structure 2. 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 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 Intensive Care Unit - Minimal Model (ICU-MM) Model structure features: Endogenous (I 2 ) and Exogenous (F I ) insulin 2 input variables: Exogenous insulin (F I ) + Carbohydrate calories (F G ) 7 patient parameters to be estimated

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 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) (P 1 < 0) fractional rate of net remote insulin disappearance (P 2 < 0) fractional rate of insulin dependent increase (P 3 > 0) meal glucose disturbance exogenous insulin plasma insulin effect of endogenous insulin

Adaptive ICU-MM 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

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 Patient case study (patient no. 10) P = 4 hrs P = 1 hr

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 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) : RMSnE ≤ 1 clinically acceptable (ISO)

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

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 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 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 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 ≤ F I ≤ max insulin flow) Flexibility with “adaptive” models (to capture varying patient dynamics) Future known disturbances (prevention of deviations from normoglycemia)

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 MPC simulation study Design settings: Cost function Prediction horizon = N = 4 hours Known disturbance input = F G = carbohydrate calories Unknown disturbance inputs (medication + 15% meas. error) Simulation results (19 critically ill patients): Median GPI25% - 75% IQ range for GPI One-hour-period simulations: Four-hours-period simulations: GPI ≤ 23 ==> “clinically acceptable”

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 Patient case study (patient no. 11)

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 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, 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 , 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 , Black-Box

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 CONCLUSION FOR OBJECTIVE 3 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, 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 , 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, Grey-Box

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 CONCLUSION FOR OBJECTIVE 3 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, 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 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 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 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 FUTURE RESEARCH New inspiration Patient Database Management System (PDMS)

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 FUTURE RESEARCH New inspiration Near-continuous glucose sensor

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 FUTURE RESEARCH Five future research topics:

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 FUTURE RESEARCH Five future research topics: 1. Optimization of GPI Time (min)

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 FUTURE RESEARCH Optimization of GPI Time (min) Frequency Time (min) Five future research topics: 1. Optimization of GPI

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 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 Five future research topics:

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 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 Five future research topics:

The End