Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients
Injury. 2025 Dec 9;57(2):112949. doi: 10.1016/j.injury.2025.112949. Online ahead of print.
ABSTRACT
OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequent and severe complication without dedicated predictive tools.
METHODS: Model development used the MIMIC-IV 3.1 database, with external validation in the eICU-CRD 2.0 database and a Chinese TSI cohort. Variables documented within 24 h of postoperative ICU admission were screened using univariable testing and refined through Boruta and Group-Lasso regression to identify the final predictors. Thirteen base learners were trained and combined in a stacking ensemble optimized by fivefold cross-validation and hyperparameter tuning. Performance was assessed using receiver operating characteristic (ROC-AUC), average precision from precision-recall (PR-AP), calibration, decision, and lift curves, along with accuracy, sensitivity, specificity, precision, and F1 scores. Interpretability was evaluated through SHAP analysis.
RESULTS: The development cohort comprised 808 patients, with 461 (57.1 %) sepsis cases, and the external validation cohort consisted of 358 patients, with 86 (24.0 %) events. Twelve predictors entered modeling, with the stacking model achieving an ROC-AUC of 0.918 and PR-AP of 0.938 in training and 0.889 and 0.936 in validation, maintaining close calibration, superior clinical utility confirmed by decision and lift curves, and balanced classification metrics, while most first-level models deteriorated markedly. External validation confirmed consistent performance and effective high-risk stratification. SHAP analysis underscored surgical burden, severity, hemodynamic, renal, and coagulation domains as key contributors, ensuring interpretability at cohort and individual levels.
CONCLUSION: This first validated model for postoperative sepsis in critically ill TSI patients shows relatively robust performance and interpretability, enabling early risk stratification and supporting clinical decision-making.
PMID:41389427 | DOI:10.1016/j.injury.2025.112949












