Background: Periprosthetic fractures following total hip arthroplasty are serious complications occurring in up to 2.4% of primary cases, contributing to significant morbidity, extended hospital stays, and elevated healthcare costs. Predicting these fractures remains a challenge despite advances in surgical techniques and prosthetic materials. Methods: This study analyzed 1,634,615 cases of primary THA from the NIS database (2016-2019) using propensity score matching to compare outcomes between patients with and without intraoperative periprosthetic fractures. Predictive models, including logistic regression, decision tree, and deep neural network, were evaluated for their ability to predict fracture risk. Results: Patients with periprosthetic fractures exhibited a 14-fold increase in pulmonary embolism risk, a 12-fold increase in infections, and a 5-fold increase in hip dislocations. Fractures extended hospital stays (3.8 vs. 2.5 days) and added approximately USD 32,000 in costs per patient. The predictive models yielded low accuracy (AUC max = 0.605), underscoring the complexity of predicting periprosthetic fractures. Conclusions: Intraoperative periprosthetic fractures in THA significantly elevate complication rates, costs, and length of stay. Despite extensive modeling efforts, accurate prediction remains difficult, highlighting the need to focus on preventive strategies, such as improved surgical techniques and real-time intraoperative monitoring.
Keywords: artificial intelligence; big data; periprosthetic fracture; total hip arthroplasty.