Key result
PID control outperforms ILQG and LQG in accuracy and bias during simulated closed-loop anesthesia.
Why the study?
Control strategies for closed-loop anesthesia delivery vary across reported systems, making a comparative analysis of common strategies useful.
PID control provides better accuracy and bias than ILQG and LQG for closed-loop anesthesia delivery in simulation models, though ILQG offers smoother infusion rates with noisy observations.
Simulation data preclude practice change in closed-loop anesthesia; hypothesis-generating for PID superiority pending clinical validation.
Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.
No takes yet. Share an insight, caveat, or question.
Chakravarty et al. (2020) studied Closed-loop anesthesia delivery (n=45). Proportional-integral-derivative (PID) control vs. Linear quadratic Gaussian (LQG) and ILQG control was evaluated on Set-point tracking performance (inaccuracy and bias). In simulated closed-loop anesthesia delivery, PID control demonstrated better accuracy and lower bias than ILQG, which in turn performed better than LQG.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: