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An enriched simulation environment for evaluation of closed-loop anesthesia

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Abstract

To simulate and evaluate the administration of anesthetic agents in the clinical setting, many pharmacology models have been proposed and validated, which play important roles for in silico testing of closed-loop control methods. However, to the authors’ best knowledge, there is no anesthesia simulator incorporating closed-loop feedback control of anesthetic agent administration freely available and accessible to the public. Consequently, many necessary but time consuming procedures, such as selecting models from the available literatures and establishing new simulator algorithms, will be repeated by different researchers who intend to explore a novel control algorithm for closed-loop anesthesia. To address this issue, an enriched anesthesia simulator was devised in our laboratory and made freely available to the anesthesia community. This simulator was built by using MATLAB® (The MathWorks, Natick, MA). The GUI technology embedded in MATLAB was chosen as the tool to develop a human–machine interface. This simulator includes four types of anesthetic models, and all have been wildly used in closed-loop anesthesia studies. For each type of model, 24 virtual patients were created with significant diversity. In addition, the platform also provides a model identification module and a control method library. For the model identification module, the least square method and particle swarm optimization were presented. In the control method library, a proportional-integral-derivative control and a model predictive control were provided. Both the model identification module and the control method library are extensive and readily accessible for users to add user-defined functions. This simulator could be a benchmark-testing platform for closed-loop control of anesthesia, which is of great value and has significant development potential. For convenience, this simulator is termed as Wang’s Simulator, which can be downloaded from http://www.AutomMed.org.

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References

  1. Hemmerling TM. Automated anesthesia. Curr Opin Anesthesiol. 2009;22(6):757–63.

    Article  Google Scholar 

  2. Liu N, Chazot T, Genty A, Landais A, Restoux A, McGee K, Laloë PA, Trillat B, Barvais L, Fischler M. Titration of propofol for anesthetic induction and maintenance guided by the bispectral index: closed-loop versus manual control: a prospective, randomized, multicenter study. Anesthesiology. 2006;104(4):686–95.

    Article  CAS  PubMed  Google Scholar 

  3. Bibian S, Ries CR, Huzmezan M, Dumont GA. Introduction to automated drug delivery in clinical anesthesia. Eur J Control. 2005;11(6):535–57.

    Article  Google Scholar 

  4. Bibian S, Zikov T, Dumont GA, Ries CR, Puil E, Ahmadi H, Huzmezan M, MacLeod BA, Estimation of the anesthetic depth using wavelet analysis of electroencephalogram. In: Proceedings of the 23rd annual international conference of the IEEE engineering in medicine and biology society, 2001, pp. 951–955.

  5. Liu N, Le Guen M, Benabbes-Lambert F, Chazot T, Trillat B, Sessler DI, Fischler M. Feasibility of closed-loop titration of propofol and remifentanil guided by the spectral M-Entropy monitor. Anesthesiology. 2012;116(2):286–95.

    Article  CAS  PubMed  Google Scholar 

  6. Struys MMRF, Vereecke H, Moerman A, Jensen EW, Verhaeghen D, De Neve N, Dumortier FJE, Mortier EP. Ability of the bispectral index, autoregressive modelling with exogenous input-derived auditory evoked potentials, and predicted propofol concentrations to measure patient responsiveness during anesthesia with propofol and remifentanil. Anesthesiology. 2003;99(4):802–12.

    Article  CAS  PubMed  Google Scholar 

  7. Rampil IJ. A primer for EEG signal processing in anesthesia. Anesthesiology. 1998;89(4):980–1002.

    Article  CAS  PubMed  Google Scholar 

  8. Stadler KS. Modelling and control in anaesthesia from design to validation: ETH Zürich, Institut f. Automatik, 2003.

  9. Bailey JM, Haddad WM. Drug dosing control in clinical pharmacology. Control Syst IEEE. 2005;25(2):35–51.

    Article  Google Scholar 

  10. Schnider TW, Minto CF, Gambus PL, Andresen C, Goodale DB, Shafer SL, Youngs EJ. The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology. 1998;88(5):1170–82.

    Article  CAS  PubMed  Google Scholar 

  11. Schnider TW, Minto CF, Shafer SL, Gambus PL, Andresen C, Goodale DB, Youngs EJ. The influence of age on propofol pharmacodynamics. Anesthesiology. 1999;90(6):1502–16.

    Article  CAS  PubMed  Google Scholar 

  12. Schüttler J, Ihmsen H. Population pharmacokinetics of propofol: a multicenter study. Anesthesiology. 2000;92(3):727–38.

    Article  PubMed  Google Scholar 

  13. Upton RN, Ludbrook G. A physiologically based, recirculatory model of the kinetics and dynamics of propofol in man. Anesthesiology. 2005;103(2):344–52.

    Article  CAS  PubMed  Google Scholar 

  14. Kataria BK, Ved SA, Nicodemus HF, Hoy GR, Lea D, Dubois MY, Mandema JW, Shafer SL. The pharmacokinetics of propofol in children using three different data analysis approaches. Anesthesiology. 1994;80(1):104–22.

    Article  CAS  PubMed  Google Scholar 

  15. Absalom A, Amutike D, Lal A, White M, Kenny G. Accuracy of the ‘Paedfusor’ in children undergoing cardiac surgery or catheterization. Br J Anaesth. 2003;91(4):507–13.

    Article  CAS  PubMed  Google Scholar 

  16. Marsh B, White M, Morton N, Kenny G. Pharmacokinetic model driven infusion of propofol in children. Br J Anaesth. 1991;67(1):41–8.

    Article  CAS  PubMed  Google Scholar 

  17. Masui K, Upton RN, Doufas AG, Coetzee JF, Kazama T, Mortier EP, Struys MMRF. The performance of compartmental and physiologically based recirculatory pharmacokinetic models for propofol: a comparison using bolus, continuous, and target-controlled infusion data. Anesth Analg. 2010;111(2):368–79.

    Article  CAS  PubMed  Google Scholar 

  18. Frei CW. Fault tolerant control concepts applied to anesthesia. Dissertation, Technische Wissenschaften ETH Zürich, Nr. 13599, 2000.

  19. Yasuda N, Lockhart SH, Eger EI II, Weiskopf RB, Liu J, Laster M, Taheri S, Peterson NA. Comparison of kinetics of sevoflurane and isoflurane in humans. Anesth Analg. 1991;72(3):316–24.

    Article  CAS  PubMed  Google Scholar 

  20. Schnider T, Minto C, Stanski D. The effect compartment concept in pharmacodynamic modelling. Anaes Pharmacol Rev. 1994;2:204–13.

    CAS  Google Scholar 

  21. McClain DA, Hug CC. Intravenous fentanyl kinetics. Clin Pharmacol Ther. 1980;28(1):106–14.

    Article  CAS  PubMed  Google Scholar 

  22. Maitre PO, Vozeh S, Heykants J, Thomson DA, Stanski DR. Population pharmacokinetics of alfentanil: The average dose-plasma concentration relationship and interindividual variability in patients. Anesthesiology. 1987;66(1):3–12.

    Article  CAS  PubMed  Google Scholar 

  23. Bovill JG, Sebel PS, Blackburn CL, Oei-Lim V, Heykants JJ. The pharmacokinetics of sufentanil in surgical patients. Anesthesiology. 1984;61(5):502–6.

    Article  CAS  PubMed  Google Scholar 

  24. Minto CF, Schnider TW, Egan TD, Youngs E, Lemmens HJM, Gambus PL, Billard V, Hoke JF, Moore KHP, Hermann DJ. Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil: I. Model development. Anesthesiology. 1997;86(1):10–23.

    Article  CAS  PubMed  Google Scholar 

  25. Weatherley B, Williams S, Neill E. Pharmacokinetics, pharmacodynamics and dose-response relationships of atracurium administered iv. Br J Anaesth. 1983;55:39S–45S.

    PubMed  Google Scholar 

  26. Dumont GA, Martinez A, Ansermino JM. Robust control of depth of anesthesia. Int J Adapt Control Signal Process. 2009;23(5):435–54.

    Google Scholar 

  27. Niño J, De Keyser R, Syafiie S, Ionescu C, Struys M. EPSAC-controlled anesthesia with online gain adaptation. Int J Adapt Control Signal Process. 2009;23(5):455–71.

    Article  Google Scholar 

  28. Ionescu CM, De Keyser R, Torrico BC, De Smet T, Struys M, Normey-Rico JE. Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia. IEEE Trans Biomed Eng. 2008;55(9):2161–70.

    Article  PubMed  Google Scholar 

  29. Nunes CS, Mendonça T, Lemos JM, Amorim P. Feedforward adaptive control of the bispectral index of the EEG using the intravenous anaesthetic drug propofol. Int J Adapt Control Signal Process. 2009;23(5):485–503.

    Google Scholar 

  30. Yelneedi S, Samavedham L, Rangaiah G. Advanced control strategies for the regulation of hypnosis with propofol. Ind Eng Chem Res. 2009;48(8):3880–97.

    Article  CAS  Google Scholar 

  31. Liu N, Chazot T, Trillat B, Michel-Cherqui M, Marandon JY, Law-Koune JD, Rives B, Fischler M. Closed-loop control of consciousness during lung transplantation: an observational study. J Cardiothorac Vasc Anesth. 2008;22(4):611–5.

    Article  PubMed  Google Scholar 

  32. Mendonça T, Lic HM, Lago P, Esteves S. Hipocrates: a robust system for the control of neuromuscular blockade. J Clin Monit Comput. 2004;18(4):265–73.

    Article  PubMed  Google Scholar 

  33. Hemmerling TM, Charabati S, Zaouter C, Minardi C, Mathieu PA. A randomized controlled trial demonstrates that a novel closed-loop propofol system performs better hypnosis control than manual administration. Can J Anesth. 2010;57(8):725–35.

    Article  PubMed  Google Scholar 

  34. Furutani E, Tsuruoka K, Kusudo S, Shirakami G, Fukuda K. A hypnosis and analgesia control system using a model predictive controller in total intravenous anesthesia during day-case surgery. In: Proceedings of SICE annual conference 2010, 2010, pp. 223–6.

  35. Liu N, Chazot T, Hamada S, Landais A, Boichut N, Dussaussoy C, Trillat B, Beydon L, Samain E, Sessler DI, Fischler M. Closed-loop coadministration of propofol and remifentanil guided by bispectral index: a randomized multicenter study. Anesth Analg. 2011;112(3):546–57.

    Article  CAS  PubMed  Google Scholar 

  36. Rinehart J, Liu N, Alexander B, Cannesson M. Closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization? Anesth Analg. 2012;114(1):130–43.

    Article  PubMed  Google Scholar 

  37. Hunt BR, Lipsman RL, Rosenberg JM, editors. A guide to MATLAB. Cambridge: Cambridge University Press; 2001.

    Google Scholar 

  38. Gentilini A, Rossoni-Gerosa M, Frei CW, Wymann R, Morari M, Zbinden AM, Schnider TW. Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane. IEEE Trans Biomed Eng. 2001;48(8):874–89.

    Article  CAS  PubMed  Google Scholar 

  39. Mendonça T, Lemos JM, Magalhães H, Rocha P, Esteves S. Drug delivery for neuromuscular blockade with supervised multimodel adaptive control. IEEE Trans Control Syst Technol. 2009;17(6):1237–44.

    Article  Google Scholar 

  40. Lennart L. System identification: theory for the user. Upper Saddle River: PTR Prentice Hall; 1999.

    Google Scholar 

  41. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 1995, pp. 1942–48.

  42. Yusup N, Zain AM, Hashim SZM. Overview of PSO for optimizing process parameters of machining. Procedia Eng. 2012;29:914–23.

    Article  Google Scholar 

  43. Rivera DE, Morari M, Skogestad S. Internal model control: PID controller design. Ind Eng Chem Res Process Des Dev. 1986;25(1):252–65.

    Article  CAS  Google Scholar 

  44. Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Eng Pract. 2003;11(7):733–64.

    Article  Google Scholar 

  45. Tao Y, Fang M, Wang Y. A fault tolerant closed-loop anesthesia system based on internal model control and extended state observer. In: Proceedings of the 25th chinese control and decision conference, 2013, pp. 4910–14.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (61074081), Doctoral Fund of Ministry of Education of China (20100010120011), Beijing Nova Program (2011025), and the Fok Ying-Tong Education Foundation (131060).

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The authors declare that they have no conflict of interest.

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  1. College of Information Science and Technology, Beijing University of Chemical Technology, Mail Box 4, 15# Beisanhuan East Road, Beijing, 100029, China

    Mengqi Fang, Yuan Tao & Youqing Wang

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  1. Mengqi Fang
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  2. Yuan Tao
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Correspondence to Youqing Wang.

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Declaration: The experiments comply with the current laws of the country in which they were performed.

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Fang, M., Tao, Y. & Wang, Y. An enriched simulation environment for evaluation of closed-loop anesthesia. J Clin Monit Comput 28, 13–26 (2014). https://doi.org/10.1007/s10877-013-9483-0

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