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Bone & Joint Open
Vol. 4, Issue 6 | Pages 399 - 407
1 Jun 2023
Yeramosu T Ahmad W Satpathy J Farrar JM Golladay GJ Patel NK

Aims

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.

Methods

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.


The Bone & Joint Journal
Vol. 96-B, Issue 10 | Pages 1333 - 1338
1 Oct 2014
Gustke KA Golladay GJ Roche MW Jerry GJ Elson LC Anderson CR

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: Bone Joint J 2014;96-B:1333–8