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Updated: Jun 29, 2026
A Mouse Model of Lumbar Spine Instability
Published on: April 23, 2021
Development and validation of a machine learning model utilizing the ACS-NSQIP database for predicting early
Mert Marcel Dagli1, Jaskeerat Gujral1, Connor A Wathen1
1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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.
