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
The primary aim of the study was to compare the knee-specific functional outcome of robotic unicompartmental knee arthroplasty (rUKA) with manual total knee arthroplasty (mTKA) for the management of isolated medial compartment osteoarthritis. Secondary aims were to compare length of hospital stay, general health improvement, and satisfaction between rUKA and mTKA. A powered (1:3 ratio) cohort study was performed. A total of 30 patients undergoing rUKA were propensity score matched to 90 patients undergoing mTKA for isolated medial compartment arthritis. Patients were matched for age, sex, body mass index (BMI), and preoperative function. The Oxford Knee Score (OKS) and EuroQol five-dimension questionnaire (EQ-5D) were collected preoperatively and six months postoperatively. The Forgotten Joint Score (FJS) and patient satisfaction were collected six months postoperatively. Length of hospital stay was also recorded.Aims
Methods
