Personalized Hemodynamic Management Using Reinforcement Learning to Prevent Persistent Acute Kidney Injury After Cardiac Surgery.

Importance: Acute kidney injury (AKI) affects one-third of patients after cardiac surgery and increases morbidity and mortality. AKI lasting over 48 hours, known as persistent AKI (pAKI), has much worse outcomes. Hemodynamic optimization is cornerstone of AKI management, however, current strategies rely on bundled care interventions that are inconsistently implemented, underscoring the need for personalized hemodynamic optimization. Objective: To develop and validate a reinforcement learning (RL) model to guide individualized dosing of intravenous (IV) fluids, vasopressors, and inotropes for prevention of pAKI after cardiac surgery. Design: Cohort study. Model development and internal validation were performed retrospectively in MIMIC-IV, with external validation in SICdb, a European database (retrospective), and then in Mount Sinai Health System cohort using data from Jan 1-Aug 18, 2025). Setting: Multicenter retrospective cohort study. Participants: Admissions to ICU after cardiac surgery. Exposures: Postoperative hemodynamic management during first 72 hours of ICU stay using IV fluids, vasopressors, and inotropes. Main Outcomes and Measures: Primary outcome was pAKI within 5 days after surgery. The RL model optimized treatment policies through reward-based learning, where higher rewards reflected improved outcome. We assessed model performance relative to clinicians using Fitted Q Evaluation and adjusted weighted pooled logistic regression. Results: There were 6,643 adult ICU admissions following cardiac surgery in MIMIC-IV, 2,254 in SICdb, and 846 in MSHS. Median age was 70 years in MIMIC-IV, 70.0 years in SICdb, and 64 years in MSHS cohort with 72%, 73%, and 70% males respectively. AKI occurred in 41.4%, 19.7%, and 22.5% of admissions, with pAKI in 30.5%, 43.0%, and 33.7% of AKI cases, respectively. RL model achieved higher cumulative rewards than clinicians across all cohorts. Concordance between clinician actions and RL model's recommendations was associated with lower adjusted odds of pAKI (OR, 0.92 [0.89-0.96] in SICdb; 0.91 [0.86-0.96] in MSHS). RL model favored smaller IV fluid volumes, moderate vasopressor dosing, and greater inotrope use. Conclusions and Relevance: In this study, personalization of early postoperative hemodynamic management using an RL model was associated with decreased risk of pAKI. These findings suggest that AI guided hemodynamic strategies may enhance postoperative care after cardiac surgery. Key Points: Question: Can reinforcement learning (RL) personalize early postoperative hemodynamic management to prevent persistent AKI (pAKI) after cardiac surgery?Findings: In 9,743 postoperative cardiac surgery ICU admissions across 3 cohorts (MIMIC-IV, SICdb, and Mount Sinai Health System), the RL model achieved higher cumulative rewards than clinician policies and was associated with lower adjusted odds of developing pAKI when clinician actions aligned with model recommendations. The RL model favored smaller intravenous fluid volumes and earlier, graded adjustments in vasopressor and inotrope dosing compared with standard practice.Meaning: RL guided individualized hemodynamic management after cardiac surgery shows promise in reducing the risk of persistent AKI and should be tested in randomized clinical trials.