Developing a personalized closed-loop controller of medically-induced coma in a rodent model

Objective. Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Current control systems for medically-induced coma require a separate offline model fitting experiment to deal with inter-subject variabilities, which would lead to therapy interruption. Removing the need for these offline interruptions could help facilitate clinical feasbility. In addition, current systems do not track intra-subject variabilities. Tracking intra-subject variabilities is essential for studying whether or how the brain response to anesthetic infusion rate changes during therapy. Further, such tracking could enhance control precison and thus help facilitate clinical feasibility. Approach. Here we develop a personalized closed-loop anesthetic delivery (CLAD) system in a rodent model that tracks both inter- and intra-subject variabilities in real time while simultaneously controlling the anesthetic in closed loop. We tested the CLAD in rats by administrating propofol to control the electroencephalogram (EEG) burst suppression. We first examined whether the CLAD can remove the need for offline model fitting interruption. We then used the CLAD as a tool to study whether and how the brain response to anesthetic infusion rate changes as a function of changes in the depth of medically-induced coma. Finally, we studied whether the CLAD can enhance control compared with prior systems by tracking intra-subject variabilities. Main results. The CLAD precisely controlled the EEG burst suppression in each rat without performing offline model fitting experiments. Further, using the CLAD, we discovered that the brain response to anesthetic infusion rate varied during control, and that these variations correlated with the depth of medically-induced coma in a consistent manner across individual rats. Finally, tracking these variations reduced control bias and error by more than 70% compared with prior systems. Significance. This personalized CLAD provides a new tool to study the dynamics of brain response to anesthetic infusion rate and has significant implications for enabling clinically-feasible automatic control of medically-induced coma.