Aims. The principle strategies of fracture-related infection (FRI) treatment are debridement, antimicrobial therapy, and implant retention (DAIR) or debridement, antimicrobial therapy, and
The use of patient-reported outcome measures (PROMs) to assess the outcome after total knee (TKA) and total hip arthroplasty (THA) is increasing, with associated regulatory mandates. However, the robustness and clinical relevance of long-term data are often questionable. It is important to determine whether using long-term PROMs data justify the resources, costs, and difficulties associated with their collection. The aim of this study was to assess studies involving TKA and THA to determine which PROMs are most commonly reported, how complete PROMs data are at ≥ five years postoperatively, and the extent to which the scores change between early and long-term follow-up. We conducted a systematic review of the literature. Randomized controlled trials (RCTs) with sufficient reporting of PROMs were included. The mean difference in scores from the preoperative condition to early follow-up times (between one and two years), and from early to final follow-up, were calculated. The mean rates of change in the scores were calculated from representative studies. Meta-analyses were also performed on the most frequently reported PROMs.Aims
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Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
