PATIENT VARIABILITY AND UNCERTAINTY QUANTIFICATION IN ANESTHESIA: PART I - PKPD MODELING AND IDENTIFICATION

摘要

The outcome of any surgery is particularly dependent on the adequate delivery of anesthetic drugs. Not surprisingly, clinical researchers have been trying to automatize their delivery in order to provide anesthesiologists with titration tools that can target the exact needs of each individual patient. As compared to today's population-normed drug delivery strategy, closed-loop drug delivery systems would provide patients with customized pharmacological action, thereby improving surgery outcome. While some anesthesia closed-loop designs have already shown promising results within controlled clinical protocols, the pharmacological variability that exists between patients needs to be addressed within a mathematical framework to prove the stability of the control laws, and gain faster and wider acceptance of these systems by the clinical community and regulatory committees. This paper is the first of a series of 2 papers addressing the issue of pharmacological variability, and how this variability translates into quantifiable system uncertainty. In this work, we focus essentially on deriving patient-specific models to assess inter-patient variability. These models will serve as basis for illustrating the uncertainty quantification approach proposed in the accompanying paper.