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 aim of this study was to determine the impact of the severity of anaemia on postoperative complications following total hip arthroplasty (THA) and total knee arthroplasty (TKA). A retrospective cohort study was conducted using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database. All patients who underwent primary TKA or THA between January 2012 and December 2017 were identified and stratified based upon hematocrit level. In this analysis, we defined anaemia as packed cell volume (Hct) < 36% for women and < 39% for men, and further stratified anaemia as mild anaemia (Hct 33% to 36% for women, Hct 33% to 39% for men), and moderate to severe (Hct < 33% for both men and women). Univariate and multivariate analyses were used to evaluate the incidence of multiple adverse events within 30 days of arthroplasty.Aims
Methods
