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Modelling and multi-parametric control for delivery of anaesthetic agents

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Abstract

This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

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References

  1. Abbod MF, Linkens DA (1998) Anesthesia simulator: intelligent monitoring and control of depth of anesthesia. IEE Colloquium on “Simulation in Medicine”, IEE Savoy Place, London 4/(1–5)

  2. Behbehani K, Cross RR (1991) A controller for regulation for mean arterial pressure using sodium optimum nitroprusside infusion rate. IEEE Trans Biomed Eng 38:513–521

    Article  CAS  PubMed  Google Scholar 

  3. Bemporad A, Morari M, Dua V, Pistikopoulos EN (2002) The explicit linear quadratic regulator for constrained systems. Automatica 38:3–20

    Article  Google Scholar 

  4. Caruso ALG, Bouillon TW, Schumacher PM, Zanderigo E, Morari M (2009) Control of drug administration during monitored anaesthesia care. IEEE Trans Autom Sci Eng 6:256–264

    Article  Google Scholar 

  5. Derighetti MP (1999) Feedback control in anaesthesia. PhD Thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland

  6. Dua P, Doyle FJ III, Pistikopoulos EN (2006) Model based blood glucose control for type 1 diabetes via parametric programming. IEEE Trans Biomed Eng 53:1478–1491

    Article  PubMed  Google Scholar 

  7. Dua P, Doyle FJ III, Pistikopoulos EN (2009) Multi-objective blood glucose control for type 1 diabetes. Med Biol Eng Comput 47:343–352

    Article  PubMed  Google Scholar 

  8. Frei CW, Derighetti M, Zbinden AM (1997) Modelling for control of mean arterial blood pressure (MAP) during anesthesia. In: Proc. 2nd Int Symp Math Mod (MATHMOD), Vienna, pp 395–400

  9. Galvanin F, Barolo M, Macchietto S, Bezzo F (2009) Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus. Ind Eng Chem Res 48:1989–2002

    Article  CAS  Google Scholar 

  10. Garcia CE, Prett DM, Morari M (1989) Model predictive control: theory and practice—a survey. Automatica 25:335–348

    Article  Google Scholar 

  11. Gentilini A, Frei CW, Glattfelder AH, Morari M, Sieber TJ, Wymann R, Schnider TW, Zbinden AM (2001) Multiasked closed-loop control in anesthesia. IEEE Eng Med Biol 20:39–53

    Article  CAS  Google Scholar 

  12. Gentilini A, Schaniel C, Morari M, Bieniok C, Wymann R, Schnider T (2002) A new paradigm for the closed-loop intraoperative administration of analgesics in humans. IEEE Trans Biomed Eng 49:289–299

    Article  PubMed  Google Scholar 

  13. Gopinath R, Bequette BW, Roy RJ, Kaufman H (1995) Issues in the design of a multirate model-based controller for a nonlinear drug infusion system. Biotech Prog 11:318–332

    Article  CAS  Google Scholar 

  14. gPROMS (2003) Introductory user’s guide, release 2.2. Process Systems Enterprise Limited, London, UK

    Google Scholar 

  15. Haung H-H, Lee Y-H, Chan H-L, Wang Y-P, Huang C-H, Fan S-Z (2008) Using a short-term parameter of heart rate variability to distinguish awake from isoflurane anesthetic states. Med Biol Eng Comput 46:977–984

    Article  Google Scholar 

  16. Held CM, Roy RJ (1995) Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model. IEEE Trans Biomed Eng 42:371–385

    Article  CAS  PubMed  Google Scholar 

  17. Linkens DA, Mahfouf M (1994) Generalized predictive control (GPC) with prespecified set-points in clinical anesthesia. In: Proc 3rd IEEE conference on control applications, vol 3, pp 1669–1674

  18. Mansour NE, Linkens DA (1989) Pole-assignment self-tuning control in postoperative patients: a simulation study. IEE Proc Contr Theory Appl 136:1–11

    Article  Google Scholar 

  19. MATLAB (2005) The Mathworks Inc., Natick, MA

  20. Mortier E, Struys M, De Smet T, Versichelen L, Rolly G (1998) Closed-loop controlled administration of propofol using bispectral analysis. Anaesthesia 53:749–754

    Article  CAS  PubMed  Google Scholar 

  21. Pistikopoulos EN (2009) Perspectives in multiparametric programming and explicit model predictive control. AIChE J 55:1918–1925

    Article  CAS  Google Scholar 

  22. Pistikopoulos EN, Dua V, Bozinis NA, Bemporad A, Morari M (2002) On-line optimization via off-line parametric optimization tools. Comput Chem Eng 26:175–185

    Article  CAS  Google Scholar 

  23. Pistikopoulos EN, Bozinis NA, Dua V, Perkins JD, Sakizlis V (2002b) Improved process control. European Patent WO 02/097540 A1

  24. Pistikopoulos EN, Georgiadis M, Dua V (2007) Multi-parametric programming, process systems engineering, vol 1. Wiley-VCH, Weinheim

    Google Scholar 

  25. Pistikopoulos EN, Georgiadis M, Dua V (2007) Multi-parametric model-based control, process systems engineering, vol 2. Wiley-VCH, Weinheim

    Google Scholar 

  26. Rao R, Aufderheide B, Bequette BW (2003) Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables. IEEE Trans Biomed Eng 50:277–288

    Article  PubMed  Google Scholar 

  27. Simpson PJ, Popat M (2002) Understanding anaesthesia, 4th edn. Butterworth-Heinemann, Oxford

    Google Scholar 

  28. Slate JB, Sheppard LC, Rideout VC, Blackstone EH (1979) A model for design of a blood pressure controller for hypertensive patients. In: Proc IEEE Engineering in Medicine and Biology Conf, Denver, pp 867–872

  29. Yasuda N, Targ AG, Eger EI (1989) Solubility of I-653, Sevoflurane, isoflurane and halothane in human tissues. Anesth Analg 69:370–373

    Article  CAS  PubMed  Google Scholar 

  30. Yasuda N, Lockhart SH, Eger EI, Weiskopf RB, Johnson BH, Freire BA, Fassoulaki A (1991) Kinetics of desflurane, isoflurane and halothane in humans. Anesthesiology 74:489–498

    Article  CAS  PubMed  Google Scholar 

  31. Yasuda N, Lockhart SH, Eger EI, Weiskopf RB, Laster M, Taheri S, Peterson NA (1991) Comparison of kinetics of sevoflurane and isoflurane in humans. Anesth Analg 72:316–324

    Article  CAS  PubMed  Google Scholar 

  32. Yu C, Roy RJ, Kaufman H (1990) A circulatory model for combined nitroprusside-dopamine therapy in acute heart failure. Med Prog Technol 16:77–88

    CAS  PubMed  Google Scholar 

  33. Yu C, Roy RJ, Kaufman H, Bequette BW (1992) Multiple-model adaptive predictive control of mean arterial pressure and cardiac output. IEEE Trans Biomed Eng 39:765–778

    Article  CAS  PubMed  Google Scholar 

  34. Zwart A, Smith NT, Beneken JEW (1972) Multiple model approach to uptake and distribution of halothane: the use of an analog computer. Comput Chem Eng 5:228–238

    CAS  Google Scholar 

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Author notes

  1. Pinky Dua

    Present address: GlaxoSmithKline Research and Development Limited, New Frontiers Science Park, Third Avenue, Harlow, CM19 5AW, UK

Authors and Affiliations

  1. Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Pinky Dua & Efstratios N. Pistikopoulos

  2. Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK

    Vivek Dua

Authors
  1. Pinky Dua
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  3. Efstratios N. Pistikopoulos
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Correspondence to Efstratios N. Pistikopoulos.

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Dua, P., Dua, V. & Pistikopoulos, E.N. Modelling and multi-parametric control for delivery of anaesthetic agents. Med Biol Eng Comput 48, 543–553 (2010). https://doi.org/10.1007/s11517-010-0604-3

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  • DOI: https://doi.org/10.1007/s11517-010-0604-3

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