Development of Machine Learning Models for Predicting Surgical Site Infection After Spinal Surgery
TL;DR
Machine learning models showed acceptable performance for predicting postoperative SSI after spinal surgery, suggesting that conventional statistical approaches may remain clinically useful in structured datasets.
Abstract
Background/Objectives: Surgical site infection (SSI) remains a clinically important complication after spinal surgery. This study developed and assessed machine learning approaches for predicting postoperative SSI using routinely collected preoperative clinical variables, with emphasis on calibration and clinical applicability. Methods: In this retrospective single-center study, four prediction models were developed in patients undergoing spinal surgery: logistic regression, random forest, gradient boosting, and XGBoost. Model training used five-fold stratified cross-validation, and performance was evaluated using a hold-out internal test set. Performance was assessed using the area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity, precision, F1 score, Brier score, and calibration slope. SHAP analysis was performed to evaluate model interpretability. Results: The incidence of SSI was 16.6%. In cross-validation, discrimination performance was broadly comparable across models, with logistic regression showing the highest observed AUC (0.814) and AUPRC (0.484). In the hold-out test set, the same model showed the highest AUC (AUC 0.806, 95% CI 0.757–0.852) and the highest sensitivity (0.758). Calibration performance varied across models. SHAP analysis identified C-reactive protein, hemoglobin, albumin, and white blood cell count as the most influential predictors. Perioperative variables provided only modest incremental predictive value. Conclusions: Machine learning models showed acceptable performance for predicting SSI after spinal surgery. Logistic regression demonstrated performance comparable to that of the evaluated machine learning models, suggesting that conventional statistical approaches may remain clinically useful in structured datasets. Preoperative clinical and laboratory variables were the major contributors to prediction, supporting their use for routine preoperative risk stratification.