Quantifying uncertainty bounds in anesthetic PKPD models.

  • Bibian S
  • Dumont G
  • Huzmezan M
 et al. 
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A major challenge faced when designing controllers to automate anesthetic drug delivery is the large variability that exists between and within patients. This intra- and inter-patient variability have been reported to lead to instability. Hence, defining and quantifying uncertainty bounds provides a mean to validate the control design, ensure its stability and assess performance. In this work, the intra- and inter-patient variability measured from thiopental induction data is used to define uncertainty bounds. It is shown that these bounds can be reduced by up to 40% when using a patient-specific model as compared to a population-normed model. It is also shown that identifying only the overall static gain of the patient system already decreases significantly this uncertainty.

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  • Stéphane Bibian

  • Guy a Dumont

  • Mihai Huzmezan

  • Craig R Ries

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