ORAKLE: Optimal Risk prediction for mAke30 in patients with acute Kidney injury using deep LEarning

BackgroundMajor Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). The existing prediction models for MAKE30 are static and overlook dynamic changes in clinical status. In this study, we introduce ORAKLE, a novel deep-learning model that utilizes evolving time-series data to predict MAKE30, enabling personalized, patient-centered approaches to AKI management and outcome improvement. MethodsWe conducted a retrospective study using three publicly available critical care databases: MIMIC-IV, SICdb, and eICU-CRD. Among these, MIMIC-IV was divided into 80% training and 20% internal test sets, whereas SiCdb and eICU-CRD were used as external validation cohorts. Patients with sepsis-3 criteria who developed AKI within 48 hours of intensive care unit admission were identified. Our primary outcome was MAKE30, defined as a composite of death, new dialysis or persistent kidney dysfunction within 30 days of ICU admission. We developed ORAKLE using Dynamic DeepHit framework for time-series survival analysis and its performance against Cox models using AUROC and AUPRC. We further assessed model calibration using Brier score. ResultsWe analyzed 16,671 patients from MIMIC-IV, 2,665 from SICdb, and 11,447 from eICU-CRD. ORAKLE outperformed the Cox models in predicting MAKE30, achieving AUROCs of 0.84 (95% CI: 0.83-0.86) vs. in MIMIC-IV internal test set 0.80 (95% CI: 0.78-0.82), 0.83 (95% CI: 0.81-0.85) vs. 0.79 (95% CI: 0.77-0.81) in SICdb, and 0.85 (95% CI: 0.84-0.85) vs. 0.81 (95% CI: 0.80-0.82) in eICU-CRD. The AUPRC values for ORAKLE were also significantly better than that of Cox models. The Brier score for ORAKLE was 0.21 across the internal test set, SICdb, and eICU-CRD, suggesting good calibration. ConclusionsORAKLE is a robust deep-learning model for predicting MAKE30 in critically ill patients with AKI that utilizes evolving time series data. By incorporating dynamically changing time series features, the model captures the evolving nature of kidney injury, treatment effects, and patient trajectories more accurately. This innovation facilitates tailored risk assessments and identifies varying treatment responses, laying the groundwork for more personalized and effective management approaches.