A novel web-based online nomogram was used to predict postoperative function in patients with femoral shaft fractures treated with intramedullary nails

Objective: To investigate factors influencing postoperative functional recovery after intramedullary nailing (IMN) for femoral shaft fractures and to develop a clinical prediction model. Methods: Patients who underwent IMN for femoral shaft fractures at the Third Hospital of Hebei Medical University between January 1, 2019, and December 31, 2023, were enrolled. Data were retrospectively collected. Patients treated in 2021 formed the validation cohort; patients treated in other years formed the modeling cohort. Least absolute shrinkage and selection operator (LASSO) regression identified factors influencing postoperative functional outcomes. Multivariable logistic regression analysis identified independent predictors, and a predictive model was constructed. Developed an interactive web-based calculator via R Shiny to implement the predictive model for postoperative recovery after intramedullary nailing of femoral shaft fractures. Results: This study included 672 patients who underwent IMN for femoral shaft fractures. The modeling cohort comprised 545 patients and the validation cohort 127 patients. Overall, 546 patients (81.2%) achieved good functional scores and exhibited satisfactory fracture healing. The mean age was 50.22 ± 17.73 years; males predominated (455 [67.70%]) over females (217 [32.30%]), yielding a male-to-female ratio of 2.10:1. LASSO regression identified significant predictors of postoperative recovery. Multivariable analysis revealed age 29-58 years, BMI 18.5-27.9 kg/m², and early appropriate weight-bearing as protective factors for good recovery. AO/OTA type C fracture, diabetes mellitus (DM), osteoporosis, and open reduction were independent risk factors for adverse outcomes. The model demonstrated strong discriminatory power, with C-indices of 0.895 (95% CI: 0.8654–0.9245) for the training cohort and 0.7848 (95% CI: 0.6947–0.8749) for the validation cohort. Hosmer-Lemeshow (H-L) testing indicated good model calibration. Decision curve analysis (DCA) showed optimal clinical utility when the threshold probability ranged from 0.19 to 1.00. The interactive web calculator developed via R Shiny is accessible at: https://femoralshaftfracture.shinyapps.io/DynNomapp1/. Conclusion: The developed predictive tool provides realistic, personalized postoperative outcome expectations for patients undergoing IMN for femoral shaft fractures. It can also aid clinicians in formulating appropriate surgical plans, ultimately improving patient prognosis.