AReS: A patient simulator to facilitate testing of automated anesthesia

This paper presents the Anesthesia Response Simulator, a novel, open-source patient simulator developed to replicate physiological responses to four commonly used drugs: propofol, remifentanil, norepinephrine, and rocuronium during anesthesia. It models depth of hypnosis, cardiac output, mean arterial pressure, heart rate, stroke volume, and neuromuscular blockade. Developed through integrated clinical practice, literature models, and medical expertise, it aims to facilitate the development of decision-making systems for anesthesia.

The simulator integrates population-based and subject-specific pharmacokinetics-pharmacodynamics models, a target-controlled infusion system, and a novel approach to simulate surgical stimuli based on drug concentrations. Both surgical stimuli and intravascular volume status are modeled as disturbances to the anesthesia state. The simulator was evaluated through a series of experiments. The median absolute error between the simulated response and clinical recordings from 10 patients is compared with each output's maximum reasonable measurement errors. To ensure that the model's response aligns with existing literature and clinical practice, we analyzed the output sensitivities to drug infusion rates and illustrated the output responses to the simulated disturbances.

For most patients, the median absolute errors between simulated and clinical recordings were within reasonable measurement ranges for each output. Although we incorporate inter-individual variability using subject-specific pharmacokinetics-pharmacodynamics models, the median response accurately reflected clinical trends for depth of hypnosis, cardiac output, mean arterial pressure, heart rate, and stroke volume. This finding validates the simulator's representation of the median population response. The output sensitivity analysis, conducted across various drug infusion rates, identifies the impact of each drug on each output. Finally, the sensitivity analysis and illustration of disturbance effects confirm that the simulator's performance is consistent with the literature and clinical practice.

This simulator models median responses of the population to four drugs, inter-individual variability, and disturbances according to current literature and expert knowledge. This open-source tool is suitable for various objectives in developing and evaluating multi-variable closed-loop controllers and decision support systems for anesthesia. We also identify limitations that encourage future work on improving the simulator.