Abstract: Objective To investigate the risk factors for fragility fractures in patients with osteoporosis and to construct a nomogram prediction model, thereby providing a reference for preventative and interventional strategies. Methods Patients diagnosed with osteoporosis at the Jiangbei Campus of Zhongda Hospital Affiliated with Southeast University between January 2022 and June 2024 were selected for this study. Relevant patient data were collected, and baseline characteristics were balanced using propensity score matching. Univariate and multivariate logistic regression analyses were employed to identify the factors influencing the occurrence of fragility fractures, and a nomogram prediction model was subsequently constructed. Results Following propensity score matching, baseline characteristics were well-balanced between the case group (n=73) and the control group (n=292). Multivariate logistic analysis identified several significant risk factors associated with fragility fractures in patients with osteoporosis (all P<0.05): monthly household income per capita <1 000 RMB (OR=3.160), excessive alcohol consumption (OR=2.264), a history of falls within the last year (OR=2.749), a history of previous fractures (OR=5.458), use of glucocorticoids for ≥3 months (OR=3.103), low level of physical activity (OR=3.165), poor balance (OR=2.844), frequency of nocturia (once/night, OR=2.124; ≥twice/night, OR=3.498), serum procollagen type I N-terminal propeptide (P1NP) levels (OR=1.451), daily calcium intake ≥600 mg (OR=0.260), and serum 25-hydroxyvitamin D [25(OH)D] levels (OR=0.644). A nomogram prediction model was developed based on these factors. The results demonstrated that the model possessed good discrimination and calibration. Furthermore, decision curve analysis indicated that the model yielded a favorable net benefit across a threshold probability range of 0 to 1. Conclusions Multiple factors, including monthly household income per capita, alcohol consumption, and a history of falls, are significantly associated with the incidence of fragility fractures in patients with osteoporosis. The nomogram prediction model constructed based on these factors demonstrates good discrimination and calibration, as well as a significant net benefit within a specific threshold range. This model provides a robust tool for the early clinical identification of high-risk populations and facilitates the development of personalized prevention and intervention strategies, ultimately contributing to a reduction in fracture risk.

Key words: Osteoporosis, Fragility fractures, Risk factors, Nomogram prediction model, prevention