Methods: Retrospective intensive care unit (ICU) cohorts from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD) were expanded into rolling 12-h windows of routinely collected features, a 2-h gap to prevent leakage during training, and a 24-h prediction horizon, updated every 6 hours.Adult patients without baseline AKI or renal replacement therapy were included; stays were censored at first AKI.Extreme gradient-boosted trees (XGBoost) with prespecified monotonic constraints were trained under 10-fold, stay-level cross-validation; probabilities were calibrated with Platt scaling.Two prespecified validation sets tested transportability: temporal (MIMIC-IV 2020-2022, COVID-19 era) and geographic external (eICU Northeast region hospitals).Discrimination, calibration, decision-curve analysis (DCA), feature importance, and subgroup bias checks were performed.The full pipeline was fixed and packaged for further use in accordance with TRIPOD guidance.Results: After exclusions, development included 44,066 ICU stays; temporal and external validations included 3,346 and 2,993 stays, respectively.Event rates were 11.7% (development), 17.1% (temporal), and 14.2% (external).AUROC was 0.88 (95% CI 0.84-0.90) in development, 0.84 (0.80-0.87) in temporal validation, and 0.82 (0.80-0.85) externally; AUPRC was 0.60 (0.57-0.62), 0.60 (0.56-0.64), and 0.53 (0.50-0.57), respectively.Using a prespecified threshold, the temporal cohort achieved sensitivity 0.76 (0.73-0.80), specificity 0.79 (0.76-0.83), precision 0.47 (0.43-0.54); external validation achieved sensitivity 0.73 (0.67-0.78), specificity 0.86 (0.83-0.89), precision 0.48 (0.42-0.54).This means roughly 3 in 4 impending AKI cases can be anticipated early enough for intervention.Calibration was strong (Brier 0.07, 0.10, 0.09); post-calibration slopes 1.0 with minimal intercept shift (temporal slope 1.03, calibration-in-the-large CITL 0.008; external slope 1.00, CITL 0.057).DCA showed consistent net benefit versus treat-all and treat-none strategies, with durable advantage up to high thresholds.Subgroups showed stable discrimination without material differences by sex or race.SHAP explanations highlighted actionable drivers-declining mean arterial pressure, low urine output, rising creatinine and blood urea nitrogen, deteriorating SpO 2 and heart rate trajectories.No high-frequency waveforms or bespoke data feeds were required.Conclusion: A transparent, reproducible EWS anticipates ICU AKI within 24 h, generalizes across time (COVID-19 era) and geography, and remains well-calibrated with proven clinical utility.At a prespecified threshold, it identifies approximately 3 in 4 impending AKI cases while maintaining high specificity, enabling early actions within routine workflows (optimize perfusion/volume, reassess nephrotoxins, reconsider contrast).Transportable design and interpretable outputs support immediate, pragmatic deployment and local recalibration.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
Miyazaki et al. (Wed,) studied this question.