Quantifying uncertainty bounds in anesthetic PKPD models

TitleQuantifying uncertainty bounds in anesthetic PKPD models
Publication TypeConference Paper
Year of Publication2004
AuthorsBibian, S., G. A. Dumont, M. Huzmezan, and C. R. Ries
Conference NameEngineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Pagination786 -789
Date Publishedsep.
Keywordsanesthetic drug delivery, anesthetic PKPD models, control design, controllers, drug delivery systems, inter-patient variability, intra-patient variability, patient-specific model, pharmacodynamics, pharmacokinetics, physiological models, population-normed model, thiopental induction, uncertainty bounds

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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