Abstract
BACKGROUND: In recent years, with the gaining popularity and wide application of total hip arthroplasty (THA), the incidence rate of periprosthetic femoral fractures (PFF) has increased. The treatment of PFF is difficult and has many related complications. Herein, we aimed to construct a nomogram model to predict occurrence of PFF after THA, in order to identify high-risk populations. METHODS: In this retrospective analysis, we selected 2,528 patients who underwent THA at Wuhan Fourth Hospital from January 2014 to August 2022. Patients were randomly divided into a training cohort (n = 1,770) and an internal validation cohort (n = 758) in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and logistic regression analysis were used to perform feature analysis and convert them into a nomogram model. The model was externally validated in 1,383 THA patients at Renmin Hospital of Wuhan University. RESULTS: Six independent risk factors for predicting PFF were identified, namely age, female sex, hip revision, non-cemented prosthesis, history of trauma, and osteoporosis. The nomogram demonstrated sufficient predictive accuracy, with area under the curve (AUC) values of 0.798 (95% confidence interval [CI]: 0.725-0.872), 0.877 (0.798-0.957), and 0.804 (0.710-0.897) in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed good consistency between the predicted risk of the model and the actual risk. CONCLUSIONS: The nomogram model for postoperative PFF after THA established in this study has good predictive value and helps identify high-risk populations.