A deep learning-based early warning system for renal replacement therapy in the intensive care unit

Background: Renal replacement therapy (RRT) as a life-saving intervention for acute kidney injury in the intensive care unit (ICU). The decision to initiate RRT remains highly complex and subjective. Accurate and timely predictions for the need of RRT initiation, duration of therapy, and subsequent clinical outcomes are crucial components of personalized care. Using time series patient data (vital signs, laboratory findings, medications, ventilator settings, intake/output, risk scores), we aimed to develop deep learning models for predicting need for RRT. Methods: Using data from Medical Information Mart for Intensive Care (MIMIC)-III, MIMIC-IV, and eICU, we trained/validated deep learning models for: (1) screening patients for prediction of the need for RRT after their first 12 hours in the ICU; (2) real-time dynamic prediction of impending RRT initiation; (3) prediction of RRT duration; and (4) prediction of mortality following RRT onset. Results: Here, we summarize the results of the first model aimed at screening patients for early prediction of RRT. In internal validation, area under the receiver operating characteristics curve (AUROC) was 0.90 (95% confidence interval [CI] 0.886–0.906) and area under the precision-recall curve (AUPRC) 0.53 was (95% CI 0.496–0.564) in MIMIC-III. Results were similar with MIMIC-IV and eICU. External validation on ICU patients admitted during the COVID period yielded an AUROC of 0.90 (95% CI 0.894–0.913) and an AUPRC of 0.57 (95% CI 0.534–0.602). Additional hospital-level external validation across eight individual eICU hospitals showed AUROCs ranging from 0.86 to 0.91 and AUPRCs ranging from 0.35 to 0.52 Discussion: By accurately identifying patients at high risk for RRT within the first 12 hours of ICU admission, the early prediction model could serve as a triage tool to prompt closer nephrology evaluation or optimization of fluid and hemodynamic management before overt renal failure develops.