This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
Methods
The ongoing COVID-19 pandemic has disrupted and delayed medical and surgical examinations where attendance is required in person. Our article aims to outline the validity of online assessment, the range of benefits to both candidate and assessor, and the challenges to its implementation. In addition, we propose pragmatic suggestions for its introduction into medical assessment. We reviewed the literature concerning the present status of online medical and surgical assessment to establish the perceived benefits, limitations, and potential problems with this method of assessment.Aims
Methods
Optimal exposure through the direct anterior approach (DAA) for total hip arthroplasty (THA) conducted on a regular operating theatre table is achieved with a standardized capsular releasing sequence in which the anterior capsule can be preserved or resected. We hypothesized that clinical outcomes and implant positioning would not be different in case a capsular sparing (CS) technique would be compared to capsular resection (CR). In this prospective trial, 219 hips in 190 patients were randomized to either the CS (n = 104) or CR (n = 115) cohort. In the CS cohort, a medial based anterior flap was created and sutured back in place at the end of the procedure. The anterior capsule was resected in the CR cohort. Primary outcome was defined as the difference in patient-reported outcome measures (PROMs) after one year. PROMs (Harris Hip Score (HHS), Hip disability and Osteoarthritis Outcome Score (HOOS), and Short Form 36 Item Health Survey (SF-36)) were collected preoperatively and one year postoperatively. Radiological parameters were analyzed to assess implant positioning and implant ingrowth. Adverse events were monitored.Aims
Methods
The anterior cruciate ligament (ACL) is known to have a poor wound healing capacity, whereas other ligaments outside of the knee joint capsule such as the medial collateral ligament (MCL) apparently heal more easily. Plasmin has been identified as a major component in the synovial fluid that varies among patients. The aim of this study was to test whether plasmin, a component of synovial fluid, could be a main factor responsible for the poor wound healing capacity of the ACL. The effects of increasing concentrations of plasmin (0, 0.1, 1, 10, and 50 µg/ml) onto the wound closing speed (WCS) of primary ACL-derived ligamentocytes (ACL-LCs) were tested using wound scratch assay and time-lapse phase-contrast microscopy. Additionally, relative expression changes (quantitative PCR (qPCR)) of major LC-relevant genes and catabolic genes were investigated. The positive controls were 10% fetal calf serum (FCS) and platelet-derived growth factor (PDGF).Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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