Kidney360 · 2026
Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury.
СтатьяНазвание и аннотация: Перевод в очереди
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Adiyeke Esra, Liu Tianqi, Naganaboina Venkata Sai Dheeraj, Li Han, Loftus Tyler J, Ren Yuanfang, Shickel Benjamin, Ruppert Matthew M, Singh Karandeep, Fang Ruogu, Rashidi Parisa, Bihorac Azra, Ozrazgat-Baslanti Tezcan
Аннотация
KEY POINTS: Intraoperative hypotension is associated with postoperative AKI, which is a common and morbid postoperative complication. We developed a reinforcement learning model to guide intraoperative fluids and vasopressors, avoiding hypotension and postoperative AKI. The model's policy showcases the potential to lower postoperative AKI and improve outcomes driven by intraoperative hypotension.
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 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 3 days after surgery.
RESULTS: The developed model replicated 69% of physicians' 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 per 15 minutes 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 with the physicians' actual treatments, as well as random policies and zero-drug policies. AKI incidence was the 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.
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 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 3 days after surgery.
RESULTS: The developed model replicated 69% of physicians' 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 per 15 minutes 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 with the physicians' actual treatments, as well as random policies and zero-drug policies. AKI incidence was the 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.