Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique

Abstract Background Machine learning (ML) has been widely applied to predict the outcomes of numerous diseases. The current study aimed to develop a prognostic prediction model using machine learning algorithms and identify risk factors associated with residual back pain in patients with osteoporotic vertebrae compression fracture (OVCF) following percutaneous vertebroplasty (PVP). Methods A total of 863 OVCF patients who underwent PVP surgery were enrolled and analyzed. One month following surgery, a Visual Analog Scale (VAS) score of ≥ 4 was deemed to signify residual low back pain following the operation and patients were grouped into a residual pain group and pain-free group. The optimal feature set for both machine learning and statistical models was adjusted based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were then calculated to evaluate the predictive performance of each model. Results In our current study, two main findings were observed: (1) Compared with statistical models, ML models exhibited superior predictive performance, with SVM demonstrating the highest prediction accuracy; (2) several variables were identified as the most predictive factors by both the machine learning and statistical models, including bone cement volume, number of fractured vertebrae, facet joint violation, paraspinal muscle degeneration, and intravertebral vacuum cleft. Conclusion Overall, the study demonstrated that machine learning classifiers such as SVM can effectively predict residual back pain for patients with OVCF following PVP while identifying associated predictors in a multivariate manner.