Optimized Renal Replacement Therapy Decisions in Intensive Care: A Reinforcement Learning Approach

Purpose Acute kidney injury (AKI) is highly prevalent in intensive care units (ICUs) and often requires renal replacement therapy (RRT). However, the optimal timing for initiating RRT remains controversial. The aim of this study was to develop a reinforcement learning (RL) model to support individualized RRT decision-making for critically ill AKI patients. Methods We trained and validated our RL model using ICU data from two cohorts: the publicly available MIMIC-IV database and a dataset from the Medical University of Vienna (MUW). Patients with AKI of stage I or higher were included, and those with chronic kidney disease or prior kidney transplantation were excluded. We extracted 88 features, employing weighted K-means clustering for state definition. A Q-learning–based RL approach was applied, with off-policy evaluation to assess the policy’s performance versus clinician decisions. Results In both the MIMIC and MUW cohorts, the RL model demonstrated a high level of concordance (up to 98.5%) with clinicians but exhibited superior performance on key metrics. Notably, the model identified a reproducible patient subgroup with greater illness severity for whom earlier or more frequent RRT could improve outcomes, suggesting a beneficial role for AI-driven decision support. Conclusions Our RL model provides dynamic, data-driven recommendations for initiating and ceasing RRT, closely aligning with clinical practice and identifying high-risk patients who may benefit from earlier intervention.