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
Multiligament knee injuries (MLKI) are devastating injuries that can result in significant morbidity and time away from sport. There remains considerable variation in strategies employed for investigation, indications for operative intervention, outcome reporting, and rehabilitation following these injuries. At present no study has yet provided a comprehensive overview evaluating the extent, range, and overall summary of the published literature pertaining to MLKI. Our aim is to perform a methodologically rigorous scoping review, mapping the literature evaluating the diagnosis and management of MLKI. This scoping review will address three aims: firstly, to map the current extent and nature of evidence for diagnosis and management of MLKI; secondly, to summarize and disseminate existing research findings to practitioners; and thirdly, to highlight gaps in current literature. A three-step search strategy as described by accepted methodology will be employed to identify peer-reviewed literature including reviews, technical notes, opinion pieces, and original research. An initial limited search will be performed to determine suitable search terms, followed by an expanded search of four electronic databases (MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, and Web of Science). Two reviewers will independently screen identified studies for final inclusion.Aims
Methods
