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. Cite this article: Bone Joint Res 2025;14(1):46–57

Total hip replacement causes a short-term increase in the risk of mortality. It is important to quantify this and to identify modifiable risk factors so that the risk of post-operative mortality can be minimised. We performed a systematic review and critical evaluation of the current literature on the topic. We identified 32 studies published over the last 10 years which provide either 30-day or 90-day mortality data. We estimate the pooled incidence of mortality during the first 30 and 90 days following hip replacement to be 0.30% (95% CI 0.22 to 0.38) and 0.65% (95% CI 0.50 to 0.81), respectively. We found strong evidence of a temporal trend towards reducing mortality rates despite increasingly co-morbid patients. The risk factors for early mortality most commonly identified are increasing age, male gender and co-morbid conditions, particularly cardiovascular disease. Cardiovascular complications appear to have overtaken fatal pulmonary emboli as the leading cause of death after hip replacement. Cite this article: Bone Joint Res 2014;3:175–82