Modeling the Effect of Intravenous Anesthetics: A Path Toward Individualization

The modeling of the effect of intravenous anesthetics in the human body that accounts for the interpatient and intrapatient variability is essential not only for therapy individualization, but also for enabling the construction of reduced complexity feedback controllers. This paper reviews the path from complex pharmacokinetic/pharmacodynamic (PK/PD) models to reduced models that allow for runtime parameter estimation and provide good runtime prediction capabilities in the closed-loop CPSs delivering the anesthesia.

[1]  M.M. Silva,et al.  Local identifiability and sensitivity analysis of neuromuscular blockade and depth of hypnosis models , 2014, Comput. Methods Programs Biomed..

[2]  Kristian Soltesz,et al.  On Automation in Anesthesia , 2013 .

[3]  R. Evans,et al.  Stabilization with data-rate-limited feedback: tightest attainable bounds , 2000 .

[4]  Alexander Medvedev,et al.  Automatic recovery from nonlinear oscillations in PID-controlled anesthetic drug delivery , 2015, 2015 European Control Conference (ECC).

[5]  Luis A. Paz,et al.  Performance of an Adaptive Controller for the Neuromuscular Blockade Based on Inversion of a Wiener Model , 2015 .

[6]  A. W. Kelman,et al.  Compartmental models and their application , 1985 .

[7]  Lewis B. Sheiner,et al.  Building population pharmacokineticpharmacodynamic models. I. Models for covariate effects , 1992, Journal of Pharmacokinetics and Biopharmaceutics.

[8]  Robin De Keyser,et al.  Evaluation of a Propofol and Remifentanil interaction model for predictive control of anesthesia induction , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  Teresa Mendonca,et al.  A simple model for the identification of drug effects , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[10]  John Baillieul,et al.  Feedback Designs for Controlling Device Arrays with Communication Channel Bandwidth Constraints , 1999 .

[11]  Thomas W. Schnider,et al.  Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil. I. Model development , 1997 .

[12]  M M da Silva,et al.  Online nonlinear identification of the effect of drugs in anaesthesia using a minimal parameterization and BIS measurements , 2010, Proceedings of the 2010 American Control Conference.

[13]  Torsten Jeinsch,et al.  Online Process Identification of Hypnosis , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[14]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[15]  Margarida Martins da Silva,et al.  Bifurcation analysis of PID-controlled neuromuscular blockade in closed-loop anesthesia , 2015 .

[16]  Panos J. Antsaklis,et al.  Control and Communication Challenges in Networked Real-Time Systems , 2007, Proceedings of the IEEE.

[17]  Hugo Alonso,et al.  Comparing different identification approaches for the depth of anesthesia using BIS measurements , 2012 .

[18]  Michel Struys,et al.  An overview of target controlled infusions and total intravenous anaesthesia , 2007 .

[19]  Erik Olofsen,et al.  Propofol Reduces Perioperative Remifentanil Requirements in a Synergistic Manner: Response Surface Modeling of Perioperative Remifentanil–Propofol Interactions , 2003, Anesthesiology.

[20]  S. Shafer,et al.  The Influence of Method of Administration and Covariates on the Pharmacokinetics of Propofol in Adult Volunteers , 1998, Anesthesiology.

[21]  S L Shafer,et al.  Response Surface Model for Anesthetic Drug Interactions , 2000, Anesthesiology.

[22]  Margarida Martins da Silva,et al.  Nonlinear Estimation of a Parsimonious Wiener Model for the Neuromuscular Blockade in Closed-loop Anesthesia , 2014 .