Patients with proximal femoral fractures (PFFs) are often multimorbid, thus unplanned readmissions following surgery are common. We therefore aimed to analyze 30-day and one-year readmission rates, reasons for, and factors associated with, readmission risk in a cohort of patients with surgically treated PFFs across Austria. Data from 11,270 patients with PFFs, treated surgically (osteosyntheses, n = 6,435; endoprostheses, n = 4,835) at Austrian hospitals within a one-year period (January to December 2021) was retrieved from the Leistungsorientierte Krankenanstaltenfinanzierung (Achievement-Oriented Hospital Financing). The 30-day and one-year readmission rates were reported. Readmission risk for any complication, as well as general medicine-, internal medicine-, and surgery/injury-associated complications, and factors associated with readmissions, were investigated.Aims
Methods
To describe the epidemiology of acetabular fractures including patient characteristics, injury mechanisms, fracture patterns, treatment, and mortality. We retrieved information from the Swedish Fracture Register (SFR) on all patients with acetabular fractures, of the native hip joint in the adult skeleton, sustained between 2014 and 2020. Study variables included patient age, sex, injury date, injury mechanism, fracture classification, treatment, and mortality.Aims
Methods
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
