Advertisement for orthosearch.org.uk
Results 1 - 2 of 2
Results per page:
Bone & Joint Open
Vol. 4, Issue 5 | Pages 393 - 398
25 May 2023
Roof MA Lygrisse K Shichman I Marwin SE Meftah M Schwarzkopf R

Aims

Revision total knee arthroplasty (rTKA) is a technically challenging and costly procedure. It is well-documented that primary TKA (pTKA) have better survivorship than rTKA; however, we were unable to identify any studies explicitly investigating previous rTKA as a risk factor for failure following rTKA. The purpose of this study is to compare the outcomes following rTKA between patients undergoing index rTKA and those who had been previously revised.

Methods

This retrospective, observational study reviewed patients who underwent unilateral, aseptic rTKA at an academic orthopaedic speciality hospital between June 2011 and April 2020 with > one-year of follow-up. Patients were dichotomized based on whether this was their first revision procedure or not. Patient demographics, surgical factors, postoperative outcomes, and re-revision rates were compared between the groups.


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.