Machine learning–driven prediction of in-hospital cardiac arrest among critically ill patients with traumatic spinal cord injury: a multicenter cohort study

Background In-hospital cardiac arrest (IHCA) represents a devastating complication among critically ill patients with traumatic spinal cord injury (TSCI), driven by profound autonomic dysfunction and systemic physiological instability. Despite the high mortality associated with these events, early recognition remains challenging, and dedicated risk stratification tools tailored to this vulnerable population are lacking. Methods Patients from the US-based MIMIC-IV 3.1 and eICU-CRD 2.0 databases were randomly divided 7:3 into training and internal validation sets. Predictors recorded within 24 h of ICU admission were selected exclusively in the training set using univariable screening, Boruta, and group lasso. Eleven base learners were optimized by fivefold cross-validation and integrated into an L2-penalized stacking ensemble using out-of-fold probabilities. After internal validation, the best-performing model was locked and evaluated unchanged in an independent six-center Chinese cohort to assess transportability across populations and clinical settings differing in ethnic composition, geography, and practice patterns. Performance was assessed in terms of discrimination (ROC-AUC and PR-AP), calibration (calibration-in-the-large, calibration slope, Brier score, and expected calibration error), classification metrics, and clinical utility through decision-curve and lift analyses, while model predictions were interpreted using SHAP values. Results The development cohort included 996 patients with 57 IHCA events, and the external cohort included 558 patients with 51 events. The stacking model with ten predictors retained good discrimination across the training, internal validation, and external validation cohorts, with ROC-AUCs of 0.953, 0.938, and 0.894 and corresponding PR-APs of 0.848, 0.794, and 0.774, respectively. Calibration was acceptable across all cohorts, with Brier scores of 0.013–0.020 and expected calibration errors of 0.020–0.033, and the 95% CIs for the external calibration-in-the-large (0.541) and calibration slope (1.318) included their respective ideal values, supporting the decision not to undertake post hoc recalibration. Decision-curve and lift analyses indicated favorable clinical utility, while SHAP analysis identified integrated patterns of autonomic dysfunction, perfusion compromise, metabolic disturbance, and overall physiological burden as the main contributors to prediction. Conclusion The stacking model maintained discrimination and calibration across US and Chinese cohorts and may support interpretable early IHCA risk stratification and anticipatory management, although prospective evaluation is required before clinical implementation.