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Quantification of the variability in response to propofol administration in children

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Author

  • Klaske van Heusden
  • J. Mark Ansermino
  • Kristian Soltesz
  • Sara Khosravi
  • Nicholas West
  • Guy A. Dumont

Summary, in English

Closed-loop control of anesthesia is expected to decrease drug dosage and wake up time while increasing patient safety and decreasing the work load of the anesthesiologist. The potential of closed-loop control in anesthesia has been demon- strated in several clinical studies. One of the challenges in the development of a closed-loop system that can be widely accepted by clinicians and regulatory authorities is the effect of inter- patient variability in drug sensitivity. This system uncertainty may lead to unacceptable performance, or even instability of the closed-loop system for some individuals. The development of reliable models of the effect of anesthetic drugs and charac- terization of the uncertainty is therefore an important step in the development of a closed-loop system. Model identification from clinical data is challenging due to limited excitation and the lack of validation data. In this paper, approximate models are therefore validated for controller design by evaluating the predictive accuracy of the closed-loop behavior. A set of 47 validated models that describe the inter-patient variability in the response to propofol in children is presented. This model set can be used for robust linear controller design provided that the experimental conditions are similar to the conditions during data collection.

Publishing year

2013

Language

English

Pages

2521-2529

Publication/Series

IEEE Transactions on Biomedical Engineering

Volume

60

Issue

9

Document type

Journal article

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Medical Engineering

Keywords

  • anesthesia
  • system identification
  • robust control

Status

Published

Project

  • Anesthesia in Closed Loop
  • LCCC

Research group

  • LCCC

ISBN/ISSN/Other

  • ISSN: 1558-2531