Prediction Models for Prognosis of Femoral Neck-Fracture Patients 6 Months after Total Hip Arthroplasty

Purpose: To establish prediction models for 6-month prognosis in femoral neck-fracture patients receiving total hip arthroplasty (THA). Patients and Methods: In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage-selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results: The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971-1), 0.925 (95% CI 0.862-0.988), 0.983 (95% CI 0.951-1.016), 0.984 (95% CI 0.953-1.014), 0.922 (95% CI 0.856-0.988), and 0.953 (95% CI 0.916-0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885-1), 0.767 (95% CI 0.615-0.918), 1 (95% CI 1-1), 1 (95% CI 1-1), 0.781 (95% CI 0.638-0.924), and 0.873 (95% CI 0.785-0.961), respectively. Conclusion: The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck-fracture patients receiving THA.