The Utility of Machine Learning Algorithms for the Prediction of Early Revision Surgery After Primary Total Hip Arthroplasty.

Background: Revision total hip arthroplasty (THA) is associated with increased morbidity, mortality, and healthcare costs due to a technically more demanding surgical procedure when compared with primary THA. Therefore, a better understanding of risk factors for early revision THA is essential to develop strategies for mitigating the risk of patients undergoing early revision. This study aimed to develop and validate novel machine learning (ML) models for the prediction of early revision after primary THA.

Cross-sectional imaging in evaluation of soft tissue reactions secondary to metal debris.

An integral component of systematic treatment algorithms to optimize evaluation and management of patients with MoM hip arthroplasty recommend the use of cross-sectional imaging to diagnose the presence of adverse local tissue reactions. Cross-sectional imaging studies such as ultrasound is a useful screening tool to detect the presence of a soft-tissue mass adjacent to a MoM implant. MARS MRI is a useful diagnostic test for assessing MoM hip arthroplasty and modular taper corrosion for adverse tissue reactions.