Closed-loop anesthetic drug concentration estimation using clinical-effect feedback.
This letter presents a novel closed-loop approach to anesthetic drug concentration estimation using clinical-effect measurement feedback. Compared with the open-loop prediction used in current target-controlled infusion systems, closed-loop estimation exploits the discrepancy between the measured and predicted clinical effects to make corrections to the drug-concentration estimate, achieving improved robustness against variability in the patient pharmacokinetics and pharmacodynamics. A robust estimator, which processes drug administration and clinical-effect measurements to estimate the plasma- and effect-site drug concentrations, is designed using μ-synthesis theory. Initial proof of principle of the closed-loop estimation is demonstrated using the Monte Carlo simulation of surgical procedures with a wide range of patient models. Closed-loop estimation results in statistically significant reductions in median percentage, median absolute percentage, and maximum absolute percentage drug-concentration errors compared to open-loop prediction.
Hahn
Jin-Oh
JO
Department ofMechanical Engineering, University ofAlberta, Edmonton, AB T6G2R3, Canada. jinoh.hahn@alum.mit.edu
Dumont
Guy A
GA
Ansermino
J Mark
JM
D000465:Algorithms; D000764:Anesthesia, Closed-Circuit / Q000379:methods*; D000777:Anesthetics / Q000008:administration & dosage / Q000493:pharmacokinetics*; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008954:Models, Biological
D000777:Anesthetics