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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

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CONTROL SYSTEM

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  1. 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

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

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

  4. 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

  5. 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

  6. 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 ?!!! Grey-Box Ph.D. Defense Tom Van Herpe April 15, 2008 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”

  7. Intensive Care Unit - Minimal Model (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 Model structure features: • Endogenous (I2) and Exogenous (FI) insulin • 2 input variables: Exogenous insulin (FI) + Carbohydrate calories (FG) • 7 patient parameters to be estimated

  8. Intensive Care Unit - Minimal Model (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 glucose effectiveness (fractional clearance) (P1 < 0) fractional rate of net remote insulin disappearance (P2 < 0) meal glucose disturbance plasma glucose effect of insulin on net glucose disappearance exogenous insulin fractional rate of insulin dependent increase (P3 > 0) plasma insulin effect of endogenous insulin Ph.D. Defense Tom Van Herpe April 15, 2008 Model structure based on: • Minimal model [Bergman et al., 1981] • Type I diabetes minimal model [Furler et al., 1985]

  9. 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

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

  11. Model evaluation 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 Average model prediction performance (per patient): RMSnE ≤1 clinically acceptable (ISO) Ph.D. Defense Tom Van Herpe April 15, 2008 “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

  12. 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

  13. Controller example: how to navigate a ship? 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 Feedback control Error…?! oooops I need my MODEL !!! A = B + (C * D) C = E + F(B+G) E = … Ph.D. Defense Tom Van Herpe April 15, 2008 Model based Predictive Control

  14. Model based Predictive Control (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 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)

  15. MPC simulation study 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 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): GPI ≤ 23 ==> “clinically acceptable”

  16. 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

  17. CONCLUSION FOR OBJECTIVE 3 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 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.

  18. CONCLUSION FOR OBJECTIVE 3 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 Grey-Box 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.

  19. CONCLUSION FOR OBJECTIVE 3 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 • 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.

  20. GENERAL CONCLUSIONS 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 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

  21. FUTURE RESEARCH 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 New inspiration • Patient Database Management System (PDMS)

  22. FUTURE RESEARCH 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 New inspiration • Near-continuous glucose sensor

  23. FUTURE RESEARCH 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 Five future research topics:

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

  25. FUTURE RESEARCH 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 Optimization of GPI Frequency Ph.D. Defense Tom Van Herpe April 15, 2008 Time (min) Five future research topics: 1. Optimization of GPI Time (min)

  26. FUTURE RESEARCH 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 3. Modelling of glycemia • Critically ill rabbits ==> improved dynamic behaviour • PDMS ==> patient clustering ==> initial model per cluster Ph.D. Defense Tom Van Herpe April 15, 2008 Five future research topics: 2. Assessment of near-continuous glucose sensors • Quality requirements for individual measurement can be lower • GLYCENSIT version 2

  27. FUTURE RESEARCH 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 Five future research topics: 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 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

  28. The End

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