The increasing prevalence of periprosthetic femoral fractures, specifically in the vicinity of the hip, has emerged as a significant issue in recent times. Consequently, there is a need for a thorough examination to enhance the effectiveness of management and treatment approaches. The findings emphasized a significant disparity in the occurrence and characteristics of these fractures, as multiple studies have highlighted the efficacy of various treatment strategies, such as open reduction and internal fixation, as well as the utilization of cortical strut allografts. Furthermore, the research has identified potential risk factors that have an impact on the characteristics of fractures, providing valuable insights that could be crucial in the development of preventive strategies. This research provides a thorough examination of periprosthetic femoral fractures, highlighting the importance of a cohesive treatment algorithm to improve the handling of such fractures. Moreover, it promotes the need for a collaborative endeavor in conducting research in this field, cultivating a more profound comprehension that has the potential to drive progress in therapeutic approaches, ultimately enhancing patient results over an extended period of time. It is crucial that forthcoming research endeavors persist in expanding upon these discoveries, striving towards a unified methodology in tackling this substantial clinical obstacle.

Background: due to increase in total hip arthroplasties (THAs), the incidence of periprosthetic hip fractures (PPHFs) is forecast to raise considerably in the next decades, with Vancouver B1 fractures (VB1) accounting for one third of total cases. Femur fixation with cerclages (with or without screws) is considered the current treatment option for intraoperative VB1. Methods: the study retrospectively includes data from patients who developed VB1 PPHFs during THAs from December 3rd 2020 to November 30th 2022. The primary outcome of this study was to identify the reintervention-free survival rate. The secondary aim was to determine clinical and radiographic assessment at follow-up, based on Harris hip score (HHS) and limb length discrepancy (LLD). Results: 37 patients with mean age 60.03 ± 15.49 (22 to 77) years old were included. Overall, the Kaplan-Meier estimated a re-operation free survival rate of 99% (CI 95%) at 6 months. The mean limb length discrepancy (LLD) improved from -3.69±6.07 (range -27.9 to 2.08) mm to 0.10±0.67 (range -1.07 to 1.20) mm. The mean HHS improved from 42.72±14.37 (range 21.00-96.00) to 94.40±10.32 (range 56.00-100.00).

Background: Periprosthetic joint infection (PJI) following total hip and knee arthroplasty remains an extremely challenging and relatively high complication. This study aims to develop, validate and evaluate the use of machine learning (ML) algorithm to predict PJI in patients undergoing revision arthroplasties. Methods: A comprehensive review of patients undergoing hip or knee revision arthroplasty from 1 January 2015 to 31 March 2021 was conducted. Clinical data coming from preoperative patients history, laboratory analysis and demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting PJI preoperatively. Results: 1360 patients were enrolled, 1141 in the aseptic cohort and 219 in the infected cohort were included. ML demonstrated good discriminatory performance in predicting PJI in the selected patients (area under the curve 0.770 ± 0.006 in the training set and 0.720 ± 0.057 in the test set), and identified 3 significant predictors of PJI. Conclusion: ML algorithm trained using preoperative clinical data accurately predicted PJI. The incorporation of ML models into preoperative assessment of patients undergoing prosthetic revision procedures are useful in providing specific risk assessment to aid individualised counselling, shared decision making and presurgical optimization.