Development and validation of a nomogram model for predicting postoperative nonunion in femoral shaft fractures.

Objective: To identify independent risk factors for nonunion following femoral shaft fracture surgery and develop a clinically applicable nomogram model for personalized risk prediction.

Methods: A retrospective cohort study included 804 patients with femoral shaft fractures treated at Xijing Hospital (2014-2020). Patients were divided into development (n = 561) and validation (n = 243) cohorts. Variables were screened via LASSO regression, and a nomogram was constructed using multivariate logistic regression. Model performance was assessed using ROC curves, calibration plots, Hosmer-Lemeshow tests, and decision curve analysis (DCA).

Results: Five independent predictors of nonunion were identified: smoking. (OR = 3.094, 95% CI:1.790-5.350), high-energy injury (OR = 2.454, 95% CI:1.167-5.159), multiple injuries (OR = 2.897, 95% CI:1.580-5.312), internal fixation method (OR = 1), and fixation failure (OR = 3.437, 95% CI:1.51'. 9-7.778). The nomogram demonstrated excellent discrimination. (AUC = 0.828 in development, 0.835 in validation cohorts) and calibration (Hosmer-Lemeshow P = 0.463 and P = 0.858, respectively). DCA confirmed clinical utility at threshold probabilities > 15%.

Conclusion: This nomogram provides a practical tool for predicting nonunion risk in femoral shaft fractures, enabling early intervention for high-risk patients.