Development and validation of a predictive model for spinal fracture risk in osteoporosis patients.

Background: Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine, increasing the risk of fractures. Given its high incidence, especially among older populations, it is critical to have accurate and effective predictive models for fracture risk. Traditionally, clinicians have relied on a combination of factors such as demographics, clinical attributes, and radiological characteristics to predict fracture risk in these patients. However, these models often lack precision and fail to include all potential risk factors. There is a need for a more comprehensive, statistically robust prediction model that can better identify high-risk individuals for early intervention.

Aim: To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.

Methods: The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined. The patients were selected according to strict criteria and categorized into two groups: Those with fractures ( = 40) and those without fractures ( = 40). Demographics, clinical attributes, biochemical indicators, bone mineral density (BMD), and radiological characteristics were collected and compared. A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model's performance.

Results: Factors significantly associated with fracture risk included age, sex, body mass index (BMI), smoking history, BMD, vertebral trabecular alterations, and prior vertebral fractures. The final risk-prediction model was developed using the formula: (logit [P] = -3.75 + 0.04 × age - 1.15 × sex + 0.02 × BMI + 0.83 × smoking history + 2.25 × BMD - 1.12 × vertebral trabecular alterations + 1.83 × previous vertebral fractures). The AUROC of the model was 0.93 (95%CI: 0.88-0.96, < 0.001), indicating strong discriminatory capabilities.

Conclusion: The fracture risk-prediction model, utilizing accessible clinical, biochemical, and radiological information, offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis. The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.