Uncertainty Minimization and Feasibility Study for Managing the Complex and Interacting Anesthesia-Hemodynamic System

A novel control strategy is proposed to enable uncertainty minimization through knowledge infusion into the closed loop. The developed methodology aims the application of predictive control to regulate the complex anesthetic-hemodynamic (AH) interaction in a set of patients during general anesthesia. A special focus is given to solutions for minimizing the risk of instability arising from large uncertainty in the patient model dynamics. The paper explores the concept of digitalizing surgical actions as part of the natural mimicking strategy of actual anesthesiologists' real-life decision-making process. The simulations supporting the claims use an AH simulator. A feasibility study for solutions in an actual constrained input-output variable set is performed. Results confirm that the feasibility is enhanced when minimizing uncertainty conditions, having important clinical relevance in multi-drug optimization.

Automated summary

This paper proposes a novel control strategy to minimize uncertainty by infusing knowledge into the closed loop. The strategy aims to regulate the complex interaction between anesthesia and hemodynamics in a set of patients during general anesthesia using predictive control. The paper focuses on minimizing the risk of instability caused by large uncertainty in the patient model dynamics. The concept of digitalizing surgical actions as part of mimicking real-life decision-making process of anesthesiologists is explored, and simulations using an anesthesia-hemodynamics simulator support the claims. A feasibility study in an actual constrained input-output variable set confirms its clinical relevance in multi-drug optimization.