Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury

BACKGROUND: Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system that generates treatment recommendations based on patient states can be a substantial asset in perioperative decision-making for cases of intraoperative hypotension in which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. METHODS: In this retrospective cohort study, we analyzed 50,021 surgeries from 42,547 adult patients who underwent major surgery at a quaternary care hospital between 2014 and 2020. We developed a deep reinforcement learning model to recommend the optimum doses of intravenous fluids and vasopressors during surgery to avoid intraoperative hypotension and AKI defined by Kidney Disease: Improving Global Outcomes serum creatinine criteria within three days following surgery. RESULTS: The developed model replicated 69% of physician's decisions for the dosage of vasopressors and proposed higher or lower dosage of vasopressors than received in 10% and 21% of the treatments, respectively. In terms of intravenous fluids, the model's recommendations were within 0.05 ml/kg/15 min of the actual dose in 41% of the cases, with higher or lower doses recommended for 27% and 32% of the treatments, respectively. The reinforcement learning policy resulted in a higher estimated policy value compared to the physicians' actual treatments, as well as random policies and zero-drug policies. AKI incidence was lowest in patients who received medication dosages that aligned with our agent model's decisions. CONCLUSIONS: Our findings suggest that implementation of the model's policy has the potential to lower postoperative AKI and improve other outcomes driven by intraoperative hypotension.