Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty: a simplified and validated model based on 154,519 total hip arthroplasties from the Swedish Arthroplasty Register

Abdulhadi Alagha, M., Cobb, Justin, Liddle, Alexander D, Malchau, Henrik, Rolfson, Ola and Mohaddes, Maziar (2025) Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty: a simplified and validated model based on 154,519 total hip arthroplasties from the Swedish Arthroplasty Register. Bone and Joint Research, 14 (1). 46 - 57. ISSN 2046-3758

Text (2046-3758.141.BJR-2024-0134.R1) - Published Version Available under License Creative Commons Attribution. Download (3MB)

• Scopus publication

Abstract

Aims While cementless fixation offers potential advantages over cemented fixation, such as a shorter operating time, concerns linger over its higher cost and increased risk of periprosthetic fractures. If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA). Methods We included 154,519 primary elective THAs from the Swedish Arthroplasty Register (SAR), encompassing 21 patient-, surgical-, and implant-specific features, for model derivation and validation in predicting 30-day, 60-day, 90-day, and one-year revision and reoperation due to PPFF. Model performance was tested using the area under the curve (AUC), and feature importance was identified in the best-performing algorithm. Results The Lasso regression excelled in predicting 30-day revisions (area under the receiver operating characteristic curve (AUC) = 0.85), while the Gradient Boosting Machine (GBM) model outperformed other models by a slight margin for all remaining endpoints (AUC range: 0.79 to 0.86). Predictive factors for revision and reoperation were identified, with patient features such as increasing age, higher American Society of Anesthesiologists grade (> III), and World Health Organization obesity classes II to III associated with elevated risks. A preoperative diagnosis of idiopathic necrosis increased revision risk. Concerning implant design, factors such as cementless femoral fixation, reverse-hybrid fixation, hip resurfacing, and small (< 35 mm) or large (> 52 mm) femoral heads increased both revision and reoperation risks. Conclusion This is the first study to develop machine-learning models to forecast the risk of PPFF necessitating secondary surgery. Future studies are required to externally validate our algorithm and assess its applicability in clinical practice.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Data Science Institute
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > R Medicine (General) R Medicine > RD Surgery
Date Deposited:	06 Feb 2025 12:48
Last Modified:	24 Apr 2025 22:34
URI:	http://eprints.lse.ac.uk/id/eprint/127198

Actions (login required)

View Item