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
Researchers continue to seek easier ways to evaluate the quality of bone and screen for osteoporosis and osteopenia. Until recently, radiographic images of various parts of the body, except the distal femur, have been reappraised in the light of dual-energy X-ray absorptiometry (DXA) findings. The incidence of osteoporotic fractures around the knee joint in the elderly continues to increase. The aim of this study was to propose two new radiographic parameters of the distal femur for the assessment of bone quality. Anteroposterior radiographs of the knee and bone mineral density (BMD) and T-scores from DXA scans of 361 healthy patients were prospectively analyzed. The mean cortical bone thickness (CBTavg) and the distal femoral cortex index (DFCI) were the two parameters that were proposed and measured. Intra- and interobserver reliabilities were assessed. Correlations between the BMD and T-score and these parameters were investigated and their value in the diagnosis of osteoporosis and osteopenia was evaluated.Objectives
Methods
Previous genome-wide association studies (GWAS) have reported significant association of the single nucleotide polymorphism (SNP) rs8044769 in the fat mass and obesity-associated gene (FTO) with osteoarthritis (OA) risk in European populations. However, these findings have not been confirmed in Chinese populations. We systematically genotyped rs8044769 and evaluated the association between the genetic variants and OA risk in a case-controlled study including 196 OA cases and 442 controls in a northern Chinese population. Genotyping was performed using the Sequenom MassARRAY iPLEX platform.Objectives
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