PATIENT VARIABILITY AND UNCERTAINTY QUANTIFICATION IN ANESTHESIA: PART II - PKPD UNCERTAINTY

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, close loop drug delivery systems would provide patients with customized pharmacological action, thereby improving surgery outcome. While some anesthesia close 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 second of a series of 2 papers addressing the issue of pharmacological variability and PKPD uncertainty. In the first paper, we presented our own drug modeling approach, which we applied towards the identification of 44 adult patient models for propofol, a central nervous system depressant drug. The individual patient models have shown a large inter-patient variability. In this paper, we further expand on our previous result in order to derive an uncertainty metrics that can be used in the control design to ensure stability and assess performances.