Development and validation of a nomogram prediction model for osteoporotic vertebral compression re-fracture after percutaneous kyphoplasty based on lumbar local fat parameters.

Abstract

Objective: The aim of this study was to investigate the predictive value of lumbar local fat parameters for osteoporotic vertebral compression re-fracture (OVCRF) after percutaneous kyphoplasty (PKP) and to develop a nomogram that could provide novel strategies for the prevention of OVCRF.

Methods: We included patients who underwent PKP at Zhongda Hospital between January 2012 and December 2021. The cohort was randomly divided into training and validation cohorts in a 7:3 ratio. Data collection encompassed general patient information, lumbar local fat parameters, and additional imaging data. Lumbar local fat parameters included intramuscular fat, subcutaneous fat, and epidural fat. Patients were classified into re-fracture and non-re-fracture groups based on the occurrence of OVCRF within two years post-PKP. A nomogram was developed utilizing LASSO-logistic regression, and model evaluation was performed through receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results: A total of 452 patients were included in this study. LASSO-logistic regression analysis identified age, bone mineral density (BMD), alkaline phosphatase (ALP), the fat infiltration ratio of paravertebral muscle (PVM-FIR), subcutaneous fat thickness (SFT), and the difference in local kyphotic angle (dLKA) between preoperative and postoperative periods as independent predictive factors for OVCRF. The evaluation curves demonstrated that the model exhibited strong predictive ability and clinical utility.

Conclusion: This study established a nomogram for predicting the occurrence of OVCRF following PKP based on lumbar local fat parameters. The model offers a valuable reference for the prediction and prevention of OVCRF.