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 replacements from the Swedish Arthroplasty Register - Alagha,MA; Cobb,J; Liddle,A; Malchau,H; Rolfson,O; Mohaddes,M (Bone & Joint Research, 2025)
Introduction While cementless fixation offers potential advantages, such as a shorter operative 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 re-operation after elective THR.Methods We included 154,519 primary elective THRs from the Swedish Arthroplasty Register(SAR), encompassing twenty-one patient-,surgical-,and implant-specific features, for model derivation and validation in predicting 30-,60-,90-day and 1-year revision and re operation due to PPFF. Model performance was tested using the area under the curve(AUC), and features importance were identified in the best performing algorithm.Results The Lasso regression excelled in predicting 30-day revisions(AUC=0.85), while the Gradient Boosting Machine(GBM) model outperformed other models by a slight margin for all remaining endpoints(AUC range:0.79-0.86). Predictive factors for revision and re-operation were identified, with patient features such as increasing age, higher ASA grade(> 3), and obesity classes II-III were associated with elevated risks. A pre-operative 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 re-operation risks.Conclusion This is the first study to develop machine learning models to forecast the risk of periprosthetic femoral fracture necessitating re-do surgery. Future studies are required to externally validate our algorithm and assess its applicability in clinical practice.