An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia.
Design of closed-loop anesthetic delivery (CLAD) systems is an important topic, particularly for medically induced coma, which needs to be maintained for long periods. Current CLADs for medically induced coma require a separate offline experiment for model parameter estimation, which causes interruption in treatment and is difficult to perform. Also, CLADs may exhibit bias due to inherent time-variation and non-stationarity, and may have large infusion rate variations at steady state. Finally, current CLADs lack theoretical performance guarantees. We develop the first adaptive CLAD for medically induced coma, which addresses these limitations. Further, we extend our adaptive system to be generalizable to other states of anesthesia.



We designed general parametric pharmacodynamic, pharmacokinetic and neural observation models with associated guidelines, and derived a novel adaptive controller. We further penalized large steady-state drug infusion rate variations in the controller. We derived theoretical guarantees that the adaptive system has zero steady-state bias. Using simulations that resembled real time-varying and noisy environments, we tested the closed-loop system for control of two different anesthetic states, burst suppression in medically induced coma and unconsciousness in general anesthesia.



In 1200 simulations, the adaptive system achieved precise control of both anesthetic states despite non-stationarity, time-variation, noise, and no initial parameter knowledge. In both cases, the adaptive system performed close to a baseline system that knew the parameters exactly. In contrast, a non-adaptive system resulted in large steady-state bias and error. The adaptive system also resulted in significantly smaller steady-state infusion rate variations compared to prior systems.



These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.
Department of Electrical Engineering, University of Southern California, CA, USA.
Yuxiao
Y
Yang
Maryam M
MM
Shanechi
D000465:Algorithms; D000764:Anesthesia, Closed-Circuit / Q000379:methods*; D000768:Anesthesia, General / Q000379:methods*; D000777:Anesthetics / Q000008:administration & dosage / Q000493:pharmacokinetics; D001499:Bayes Theorem; D003128:Coma / Q000139:chemically induced*; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D007262:Infusions, Intravenous; D008954:Models, Biological; D013269:Stochastic Processes
D000777:Anesthetics