Aims. This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted
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 same-day discharge for day-case surgery was 85% (95% CI 81 to 88). Patient-reported outcome measures and cost-effectiveness were either equal or favoured day-case. Conclusion. Within the limitations of the literature, in particular the substantial risk of selection bias, the outcomes following day-case knee and hip replacement appear not to be inferior to those following an inpatient stay. The evidence is more robust for
The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and
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:
