Aims. Day-case knee and hip replacement, in which patients are discharged on the day of surgery, has been gaining popularity during the last two decades, and particularly since the COVID-19 pandemic. This systematic review presents the evidence comparing day-case to inpatient-stay surgery. Methods. A systematic literature search was performed of MEDLINE, Embase, and grey literature databases to include all studies which compare day-case with inpatient knee and hip replacement. Meta-analyses were performed where appropriate using a random effects model. The protocol was registered prospectively (PROSPERO CRD42023392811). Results. A total of 38 studies were included, with a total of 83,888 day-case procedures. The studies were predominantly from the USA and Canada, observational, and with a high risk of bias. Day-case patients were a mean of 2.08 years younger (95% CI 1.05 to 3.12), were more likely to be male (odds ratio (OR) 1.3 (95% CI 1.19 to 1.41)), and had a lower mean BMI and American Society of Anesthesiologists grades compared with inpatients. Overall, day-case surgery was associated with significantly lower odds of readmission (OR 0.83 (95% CI 0.73 to 0.96); p = 0.009), subsequent emergency department attendance (OR 0.62 (95% CI 0.48 to 0.79); p < 0.001), and complications (OR 0.7 (95% CI 0.55 to 0.89) p = 0.004), than inpatient surgery. There were no significant differences in the rates of reoperation or mortality. The overall rate of successful
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:
