Development and validation of a machine learning model utilizing the ACS-NSQIP database for predicting early postoperative Re-operation in lumbar microdiscectomy.

INTRODUCTION

Early re-operation secondary to postoperative surgical site infection (SSI) in lumbar microdiscectomy, although rare, poses significant morbidity and healthcare burden.

RESEARCH QUESTION: Given the variable performance of traditional statistical and machine learning (ML) models in predicting this outcome, this study aimed to develop and validate a ML model to accurately predict 30-day re-operation following SSI in patients undergoing lumbar microdiscectomy.

MATERIAL AND

METHODS

De-identified patient data were obtained from American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), focusing on patients with microdiscectomy. After applying inclusion criteria, 79,870 eligible cases were analyzed, of which 462 (0.6%) experienced re-operation following SSI. A nested cross-validation pipeline with Bayesian optimization was implemented using extreme gradient boosting (XGBoost) combined with Synthetic Minority Over-sampling Technique (SMOTE), and multiple classification thresholds were evaluated. Model interpretability was assessed through SHapley Additive Explanations (SHAP) to identify the most influential predictors.

RESULTS

The final model achieved an area under the receiver operating characteristic curve of 0.996, with accuracy consistently at 0.993 and sensitivity of 0.924-0.985 across thresholds. Specificity remained at 0.993, while the positive predictive value fluctuated modestly (0.436-0.437), and the negative predictive value was 1.000. SHAP analysis highlighted any SSI, race, smoking status, diabetic status, and revised Risk Analysis Index scores as top predictors influencing re-operation risk.

DISCUSSION AND

CONCLUSION

An optimized ML approach incorporating synthetic data augmentation yielded high predictive performance for an infrequent yet critical complication of lumbar microdiscectomy. These findings underscore the potential of data-driven models to enhance perioperative risk stratification and support targeted preventive strategies.