Influencing factors of fracture in elderly ankle fracture patients in the emergency department and the construction of nomogram prediction model.

UNLABELLED: The aim of this study was to analyze the independent risk factors of elderly ankle fracture patients in the emergency department and to construct the corresponding prediction model in order to improve the ability of the clinic to identify the high-risk individuals, so as to carry out the preventive and interventional work more effectively.

METHODS

This study employed a retrospective cohort design, including 300 elderly ankle fracture patients who visited the emergency department of our hospital between January 2020 and December 2024 (observation group), as well as 300 non-fractured elderly patients from the same period (control group), matched according to predefined criteria. Possibly relevant risk factors were screened by univariate and multivariate statistical analyses, and a nomogram prediction model was built accordingly. In order to assess the discriminative power, calibration and clinical utility of the model, the receiver operating characteristic curve was plotted and the area under the curve was calculated, and the goodness-of-fit of the model was evaluated using the Hosmer-Lemeshow test. To improve the stability of the prediction tool, Bootstrap method and ten-fold cross-validation were introduced in the study for internal validation, and the calibration graph and decision curve were combined to assess its potential for application in actual clinical practice.

RESULTS

multifactorial logistic regression analysis showed that living alone (OR, 2.914; 95% CI, 1.336-8.847), osteoporosis (OR, 4.044; 95% CI, 1.416-11.127), body mass index <18.5kg/m2 or body mass index >24kg/m2 (OR, 3.599; 95% CI, 2.047-9.490), cognitive dysfunction (OR, 3.901; 95% CI, 1.612-7.829), and fracture history (OR, 4.291; 95% CI, 1.310-8.046) were all independent risk factors for emergency geriatric ankle fractures.

CONCLUSION

The prediction model constructed in this study has good accuracy and operability, and can be used as an intuitive tool for clinicians to conduct individualized risk assessment, which helps to identify high-risk elderly patients at an early stage and promote the implementation of targeted prevention and treatment measures.