Abstract
BACKGROUND: Postoperative re-fractures in elderly osteoporotic vertebral compression fracture (OVCF) patients with comorbidities pose a major clinical challenge, with rates up to 52%. Traditional risk models overlook complex underlying diseases interactions in elderly patients. This study pioneers a machine learning (ML) framework for this high-risk group, integrating multidimensional factors to predict re-fractures and identify novel predictors. METHODS: We analyzed 560 OVCF patients with comorbidities who underwent percutaneous vertebroplasty (PVP). Fourteen characteristic variables-including scoliosis, chronic kidney disease (CKD), mental disorders, and cardiovascular comorbidities-were selected using feature engineering. Six ML models [Random Forest (RF), XGBoost, support vector machine (SVM), etc.,] were trained and validated. Model performance was rigorously assessed via AUC-ROC, precision-recall curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values provided interpretable risk quantification. RESULTS: The RF model achieved superior predictive performance (test AUC = 0.88, sensitivity = 0.77, specificity = 0.87), outperforming conventional approaches. Notably, we identified scoliosis (SHAP = 0.14), mental disorders (0.12), and CKD (0.10) as the three top risk factors, with biomechanical and comorbidity interactions playing pivotal roles. DCA confirmed high clinical utility, with RF providing the greatest net benefit across risk thresholds. CONCLUSION: This pioneering study establishes ML as a transformative tool for re-fracture prediction in OVCF patients with underlying diseases, uncovering previously underappreciated risk factors. Our findings highlight the critical need for integrated management of spinal deformity, mental health, and renal function in this vulnerable population. This ML framework offers a paradigm shift in personalized risk stratification and postoperative care.