Personalized Fluid Management in Patients with Sepsis and AKI: A Policy Tree Approach.

Rationale: Intravenous fluids are mainstay of management of acute kidney injury (AKI) after sepsis but can cause fluid overload. Recent literature shows that restrictive fluid strategy may be beneficial in some patients with AKI, however, identifying these patients is challenging. Objectives: We aimed to develop and validate a machine learning algorithm to identify patients who would benefit from a restrictive fluid strategy. Methods: We included patients with sepsis who developed AKI within 48 hours of ICU admission and defined restrictive fluid strategy as receiving <500mL fluids within 24 hours after AKI. Our primary outcome was early AKI reversal at 24 hours of AKI onset, and secondary outcomes included sustained AKI reversal and major adverse kidney events by 30 days (MAKE30). We used a causal forest, a machine learning algorithm to estimate individual treatment effects and policy tree algorithm to identify patients who would benefit by restrictive fluid strategy. We developed the algorithm in MIMIC-IV and validated it in SICdb databases. Measurements and Main Results: Among 2,044 patients in the external validation cohort, policy tree recommended restrictive fluids for 66.7%. Among these, patients who received restrictive fluids demonstrated significantly higher rate of early AKI reversal (47.1% vs 31.7%,p=0.004), sustained AKI reversal (28.7% vs 17.5%, p=0.013) and lower rates of MAKE30 (23.0% vs 37.1%, p=0.011). These results were consistent in adjusted analysis. Conclusion: Policy tree based on causal machine learning can identify septic patients with AKI who benefit from a restrictive fluid strategy. This approach needs to be validated in prospective trials.