Aims. The principle strategies of fracture-related infection (FRI) treatment are debridement, antimicrobial therapy, and implant retention (DAIR) or debridement, antimicrobial therapy, and implant removal/exchange. Increasing the period between fracture fixation and FRI revision surgery is believed to be associated with higher failure rates after DAIR. However, a clear time-related cut-off has never been scientifically defined. This systematic review analyzed the influence of the interval between fracture fixation and FRI revision surgery on success rates after DAIR. Methods. A systematic literature search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in PubMed (including MEDLINE), Embase, and Web of Science Core Collection, investigating the outcome after DAIR procedures of long bone FRIs in clinical studies published until January 2020. Results. Six studies, comprising 276 patients, met the inclusion criteria. Data from this review showed that with a short duration of infection (up to three weeks) and under strict preconditions, retention of the implant is associated with high success rates of 86% to 100%. In delayed infections with a fracture fixation-FRI revision surgery interval of three to ten weeks, absence of recurrent infection was reported in 82% to 89%. Data on late FRIs, with a fracture fixation-FRI revision surgery interval of more than ten weeks, are scarce and a success rate of 67% was reported. Conclusion. Acute/early FRI, with a short duration of infection, can successfully be treated with DAIR up to ten weeks after osteosynthesis. The limited available data suggest that chronic/late onset FRI treated with DAIR may be associated with a higher rate of recurrence. Successful outcome is dependent on managing all aspects of the infection. Thus, time from fracture fixation is not the only factor that should be considered in treatment planning of FRI. Due to the heterogeneity of the available data, these conclusions have to be interpreted with caution. Cite this article: Bone Joint J 2021;103-B(2):213–221

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: Bone Joint J 2022;104-B(12):1292–1303.