Integration of Spinal Musculoskeletal System Parameters for Predicting OVCF in the Elderly: A Comprehensive Predictive Model

Study Design Systematic literature review. Objectives To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes. Methods A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model’s performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation. Results The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSA PS ), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFI ES+MF and FMFI ES+MF ), FMFI PS , and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit. Conclusions This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.