The Same Day Discharges following Primary Total Knee Arthroplasty: A Single Surgeon, Propensity Score-Matched Cohort Analysis.

This is a retrospective study. As new surgical techniques and improved perioperative care approaches have become available, the same-day discharge in selected total knee arthroplasty (TKA) patients was introduced to decrease health care costs without compromising outcomes. This study aimed to compare clinical and functional outcomes between same-day discharge TKA patients and inpatient-discharge TKA patients.

Can machine learning models predict failure of revision total hip arthroplasty?

Introduction: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty.

Artificial Neural Networks Can Predict Early Failure of Cementless Total Hip Arthroplasty in Patients With Osteoporosis.

Background: Total hip arthroplasty (THA) done in the aging population is associated with osteoporosis-related complications. The altered bone density in osteoporotic patients is a risk factor for revision surgery. This study aimed to develop and validate machine learning (ML) models to predict revision surgery in patients with osteoporosis after primary noncemented THA.

Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty.

Background: Despite advancements in total hip arthroplasty (THA) and the increased utilization of tranexamic acid, acute blood loss anemia necessitating allogeneic blood transfusion persists as a post-operative complication. The prevalence of allogeneic blood transfusion in primary THA has been reported to be as high as 9%. Therefore, this study aimed to develop and validate novel machine learning models for the prediction of transfusion rates following primary total hip arthroplasty.

The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs.

Introduction: The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty.

Purpose: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA.

Methods: A total of 2512 consecutive patients who underwent revision TKA were evaluated.