Integrating 72-h BUN/Cr trajectories with interpretable machine learning for 28-day mortality after acute stroke: a dual-center landmark study.

Background: The blood urea nitrogen-to-creatinine ratio (BUN/Cr) may reflect renal perfusion and systemic stress, but its early course after acute stroke is unclear. We identified 72-h BUN/Cr trajectories and assessed their role in interpretable machine-learning evaluation of 28-day mortality. Methods: This dual-center landmark study included 2,805 ICU patients with acute ischemic stroke, nontraumatic intracerebral hemorrhage, or nontraumatic subarachnoid hemorrhage from the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 3.1. Before modeling, patients were split into a training set ( Results: A quadratic three-class model identified low/stable, high U-shaped, and rising-to-plateau patterns, with mortality rates of 21.6, 35.9, and 39.7%. Adjusted hazard ratios versus Class 1 were 1.87 (95% CI 1.25-2.79) for Class 2 and 1.73 (1.31-2.29) for Class 3. The training-selected Light Gradient Boosting Machine (LightGBM) achieved an AUROC of 0.734 (95% CI 0.695-0.773), a calibration slope of 0.894, and an O/E ratio of 1.015 in the internal test set. In the Qinghai external validation cohort, the AUROC was 0.624 (95% CI 0.559-0.689), the calibration slope was 0.459, and the O/E ratio was 1.920. Adding trajectory class to a model already containing routine clinical and static renal variables changed the AUROC by 0.003 in the internal test set ( Conclusion: BUN/Cr trajectories were associated with post-landmark mortality and summarized the three-day renal course. Training-selected LightGBM showed moderate internal discrimination and underwent frozen external evaluation. Trajectory class added modest information beyond routine clinical and static renal variables, while the combined approach provides an interpretable framework for 72-h reassessment.