To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
Return to sport following undergoing total (TKA) and unicompartmental knee arthroplasty (UKA) has been researched with meta-analyses and systematic reviews of varying quality. The aim of this study is to create an umbrella review to consolidate the data into consensus guidelines for returning to sports following TKA and UKA. Systematic reviews and meta-analyses written between 2010 and 2020 were systematically searched. Studies were independently screened by two reviewers and methodology quality was assessed. Variables for analysis included objective classification of which sports are safe to participate in postoperatively, time to return to sport, prognostic indicators of returning, and reasons patients do not.Aims
Methods
