Development and validation of a CT-based predictive model for new vertebral compression fractures: the role of vertebral CT attenuation and paraspinal muscle

Abstract

Objectives Osteoporotic vertebral compression fractures (OVCFs) are a major health burden, especially with aging populations. While percutaneous vertebroplasty (PVP) is effective, new vertebral compression fractures (NVCFs) are a common complication. Current NVCF prediction models focus on cement factors or bone density alone, ignoring the critical role of paraspinal muscles. To address this gap, our study integrates preoperative CT imaging of bone density and muscle area with clinical data to develop a visualized early prediction model for NVCF risk, aiming to improve patient outcomes. Methods A total of 423 patients who underwent PVP at Guangzhou University of Chinese Medicine Dongguan hospital were retrospectively analyzed and were allocated into a training set and a validation set in an 8:2 ratio. The study employed multivariate logistic regression analysis and the Least Absolute Shrinkage and Selection Operator (Lasso) to develop predictive models and generate nomogram. Receiver Operating Characteristic (ROC) curves and calibration curves were plotted to evaluate the models’ discriminatory and calibration capabilities, Decision Curve Analysis (DCA) and Clinical Impact Curve(CIC)were used to assess the models’ clinical applicability and utility. Results Lasso regression analysis identified age, albumin (ALB), paravertebral muscle area, bone CT value, low-energy trauma, and single-segment fracture as significant predictors. AUC of the nomogram model in the training set was 0.893 (95% CI: 0.858–0.928) and in the validation set was 0.952 (95% CI: 0.903–1), demonstrating its robust predictive capability. DCA and CIC further suggest that this nomogram model possesses substantial clinical application value. Conclusion The nomogram developed based on conventional clinical data in this study, has undergone internal validation and demonstrated efficacy in predicting NVCF following PVP. It serves as a valuable decision-making tool for clinicians. However, further multicenter studies are required for external validation to confirm its generalizability.