Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

Abstract

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. 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. 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. 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.