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
Normal sagittal spine-pelvis-lower extremity alignment is crucial in humans for maintaining an ergonomic upright standing posture, and pathogenesis in any segment leads to poor balance. The present study aimed to investigate how this sagittal alignment can be affected by severe knee osteoarthritis (KOA), and whether associated changes corresponded with symptoms of lower back pain (LBP) in this patient population. Lateral radiograph films in an upright standing position were obtained from 59 patients with severe KOA and 58 asymptomatic controls free from KOA. Sagittal alignment of the spine, pelvis, hip and proximal femur was quantified by measuring several radiographic parameters. Global balance was accessed according to the relative position of the C7 plumb line to the sacrum and femoral heads. The presence of chronic LBP was documented. Comparisons between the two groups were carried by independent samples Objectives
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