Estimation of pharmacokinetic model parameters.
This paper addresses the problem of estimating the depth of anesthesia in clinical practice where many drugs are used in combination. The aim of the project is to use pharmacokinetically-derived data to predict episodes of light anesthesia. The weighted linear combination of anesthetic drug concentrations was computed using a stochastic pharmacokinetic model. The clinical definition of light anesthesia was based on the hemodynamic consequences of autonomic nervous system responses to surgical stimuli. A rule-based expert system was used to review anesthesia records to determine instances of light anesthesia using hemodynamic criteria. It was assumed that light anesthesia was a direct consequence of the weighted linear combination of drug concentrations in the patient's body that decreased below a certain threshold. We augmented traditional two-compartment models with a stochastic component of anesthetics' concentrations to compensate for interpatient pharmacokinetic and pharmacodynamic variability. A cohort of 532 clinical anesthesia cases was examined and parameters of two compartment pharmacokinetic models for 6 intravenously administered anesthetic drugs (fentanyl, thiopenthal, morphine, propofol, midazolam, ketamine) were estimated, as well as the parameters for 2 inhalational anesthetics (N2O and isoflurane). These parameters were then prospectively applied to 22 cases that were not used for parameter estimation, and the predictive ability of the pharmacokinetic model was determined. The goal of the study is the development of a pharmacokinetic model that will be useful in predicting light anesthesia in the clinically relevant circumstance where many drugs are used concurrently.
Timcenko
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A
Department of Anesthesiology, Mount Sinai Medical Center, New York, NY 10029, USA.
Reich
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DL
Trunfio
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G
D018685:Anesthetics, Inhalation / Q000097:blood / Q000493:pharmacokinetics*; D018686:Anesthetics, Intravenous / Q000097:blood / Q000493:pharmacokinetics*; D003198:Computer Simulation; D005103:Expert Systems; D006801:Humans; D008954:Models, Biological; D013269:Stochastic Processes
D018685:Anesthetics, Inhalation; D018686:Anesthetics, Intravenous