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
The aim of this prospective multicentre study
was to report the patient satisfaction after total knee replacement (TKR),
undertaken with the aid of intra-operative sensors, and to compare
these results with previous studies. A total of 135 patients undergoing
TKR were included in the study. The soft-tissue balance of each
TKR was quantified intra-operatively by the sensor, and 18 (13%)
were found to be unbalanced. A total of 113 patients (96.7%) in
the balanced group and 15 (82.1%) in the unbalanced group were satisfied
or very satisfied one year post-operatively (p = 0.043). A review of the literature identified no previous study with
a mean level of satisfaction that was greater than the reported
level of satisfaction of the balanced TKR group in this study. Ensuring
soft-tissue balance by using intra-operative sensors during TKR
may improve satisfaction. Cite this article:
