No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data.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
Endoprosthetic reconstruction with a distal femoral arthroplasty (DFA) can be used to treat distal femoral bone loss from oncological and non-oncological causes. This study reports the short-term implant survivorship, complications, and risk factors for patients who underwent DFA for non-neoplastic indications. We performed a retrospective review of 75 patients from a single institution who underwent DFA for non-neoplastic indications, including aseptic loosening or mechanical failure of a previous prosthesis (n = 25), periprosthetic joint infection (PJI) (n = 23), and native or periprosthetic distal femur fracture or nonunion (n = 27). Patients with less than 24 months’ follow-up were excluded. We collected patient demographic data, complications, and reoperations. Reoperation for implant failure was used to calculate implant survivorship.Aims
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