Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin ( 65 years of age), and body mass index (BMI) of < 25 kg/m2), 5-item modified frailty index (5MFI), and 6MFI.

Total knee arthroplasty (TKA) is an excellent surgical option for patients who have end-stage knee osteoarthritis. While rates of major postoperative complications have steadily decreased with modern implants and operative techniques, contemporary outcome data for patients who have Ehlers-Danlos syndrome (EDS) are scarce. The goal of this study was to compare complication rates after primary TKA in patients who have EDS versus matched controls.

The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA.

Many patients suffer from osteoarthritis (OA) in multiple joints, possibly resulting in multiple joint arthroplasties (MJAs). Primarily, we determined the cumulative incidence (Cin) of MJA in hip and knee joints up to 10 years. Secondly, we calculated the mean time between the first and subsequent joint arthroplasty, and evaluated the different MJA trajectories. Lastly, we compared patient characteristics and outcomes (functionality and pain) after surgery between MJA patients and single hip arthroplasty or knee arthroplasty (HA and KA) patients.

Wearable devices provide the ability for clinical teams to continuously monitor patients’ rehabilitation progress with objective data. Understanding expected recovery patterns following total knee arthroplasty (TKA) enables prompt identification of patients failing to meet these milestones. The aim of this study was to establish normative values for daily functional recovery in the first 6 weeks after TKA using a wearable device.