Optimal control of midazolam infusion for post operative patients in intensive care units
Author | Padmanabhan, R. |
Author | Meskin, Nader |
Author | Haddad, W.M. |
Available date | 2022-04-14T08:45:43Z |
Publication Date | 2015 |
Publication Name | Asian Journal of Control |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1002/asjc.922 |
Abstract | The critical task of anesthesia administration demands optimal drug titration by considering patient-specific features and sedation requirements. Though there exist several sedation assessment monitors, physicians still largely rely on subjective sedation scoring systems. This paper presents an optimal infusion controller for the continuous infusion of midazolam along with the analgesic fentanyl. Specifically, we extend the convex optimization-based optimal drug dosing regimen for propofol to midazolam. We additionally propose an alternative optimization approach using the Kullback-Leibler divergence measure of two probability distributions. It is shown that by using the new proposed approach one can achieve the desired sedation level by using less drug infusion in comparison with previous methods. A nonlinear two-compartment model is used to describe the pharmacokinetics of midazolam and a stochastic pharmacodynamic model is used that utilizes the modified Ramsay sedation scoring method to link the plasma drug concentration with the effective probabilistic sedation level. 2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd. |
Language | en |
Publisher | Wiley-Blackwell |
Subject | Anesthetics Controlled drug delivery Convex optimization Drug infusion Intensive care units Pharmacodynamics Pharmacokinetics Stochastic models Stochastic systems Continuous infusions Kullback Leibler divergence Optimal controls Optimization approach Patient specific Pharmacodynamic models Plasma drug concentration Two-compartment models Probability distributions |
Type | Article |
Pagination | 675-686 |
Issue Number | 2 |
Volume Number | 17 |
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Electrical Engineering [2813 items ]