Title :
A Direct Dynamic Dose-Response Model of Propofol for Individualized Anesthesia Care
Author :
Hahn, Jin-Oh ; Dumont, Guy A. ; Ansermino, J. Mark
Author_Institution :
Dept. of Mech. Eng., Univ. of Alberta, Edmonton, AB, Canada
Abstract :
In an effort to open up new opportunities in individualized anesthesia care, this paper presents a dynamic dose-response model of propofol that relates propofol dose (i.e., infusion rate) directly to a clinical effect. The proposed model consists of a first-order equilibration dynamics plus a nonlinear Hill equation model, each representing the transient distribution of propofol dose from the plasma to the effect site and the steady-state dose-effect relationship. Compared to traditional pharmacokinetic-pharmacodynamic (PKPD) models, the proposed model has structural parsimony and comparable predictive capability, making it more attractive than its PKPD counterpart for identifying an individualized dose-response model in real-time. The efficacy of the direct dynamic dose-response model over a traditional PKPD model was assessed using a mixed effects modeling analysis of the electroencephalogram (EEG)-based state entropty (SE) response to intravenous propofol administration in 34 pediatric subjects. An improvement in the mean-squared error and r2 value of individual prediction, as well as the Akaike´s information criterion (AIC) was seen with the direct dynamic dose-response model.
Keywords :
dosimetry; drugs; electroencephalography; paediatrics; patient care; Akaike information criterion; EEG-based state entropty response; direct dynamic dose-response model; electroencephalogram; first-order equilibration dynamics; individualized anesthesia care; intravenous propofol administration; mean-squared error; nonlinear Hill equation model; pediatric subjects; pharmacokinetic-pharmacodynamic models; plasma effects; propofol dose; steady-state dose-effect relationship; structural parsimony; transient distribution; Analytical models; Anesthesia; Brain modeling; Computational modeling; Data models; Mathematical model; Predictive models; Anesthesia; dose-response model; propofol; Adolescent; Anesthetics, Intravenous; Child; Dose-Response Relationship, Drug; Electroencephalography; Humans; Infusions, Intravenous; Linear Models; Models, Biological; Nonlinear Dynamics; Propofol; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2011.2177497