An interpretable machine learning model for early prediction of subsequent observed CRRT initiation in patients with sepsis-associated acute kidney injury

We developed and externally validated an interpretable machine-learning model for observed continuous renal replacement therapy (CRRT) initiation in sepsis-associated acute kidney injury (SA-AKI) using a strict 24-hour landmark framework. This multicenter retrospective study used MIMIC-IV and eICU-CRD data. Predictors were restricted to the period from sepsis diagnosis to 24 h, and the outcome was first observed CRRT initiation after the landmark and within 7 days. Patients who died, initiated CRRT, or were no longer under observation by 24 h were excluded. Eight algorithms were evaluated, and a seven-predictor Gradient Boosting model was selected. Discrimination, calibration, incremental value, SHAP-based interpretation, and exploratory prediction-subgroup outcomes were assessed. The final MIMIC-IV cohort included 5,238 patients, with 239 CRRT events; 3,666 were assigned to training and 1,572 to internal validation. The eICU-CRD cohort included 4,683 patients and 157 events. The model retained serum creatinine, AKI stage, SOFA score, urine output, lactate, red blood cell distribution width, and peripheral oxygen saturation. AUCs were 0.905 internally and 0.816 externally, with an external calibration slope of 0.567. In an exploratory age- and sex-matched eICU analysis, the high predicted-risk/no observed CRRT subgroup had higher ICU, 7-day, and 28-day mortality than the true-negative subgroup. The model provided interpretable risk estimates for observed CRRT initiation, but external calibration and incremental value were limited. Findings should not be interpreted as evidence of CRRT indication, undertreatment, or treatment benefit. Prospective validation and local recalibration are required.