Robotic arm-assisted surgery offers accurate and reproducible guidance in component positioning and assessment of soft-tissue tensioning during knee arthroplasty, but the feasibility and early outcomes when using this technology for revision surgery remain unknown. The objective of this study was to compare the outcomes of robotic arm-assisted revision of unicompartmental knee arthroplasty (UKA) to total knee arthroplasty (TKA) versus primary robotic arm-assisted TKA at short-term follow-up. This prospective study included 16 patients undergoing robotic arm-assisted revision of UKA to TKA versus 35 matched patients receiving robotic arm-assisted primary TKA. In all study patients, the following data were recorded: operating time, polyethylene liner size, change in haemoglobin concentration (g/dl), length of inpatient stay, postoperative complications, and hip-knee-ankle (HKA) alignment. All procedures were performed using the principles of functional alignment. At most recent follow-up, range of motion (ROM), Forgotten Joint Score (FJS), and Oxford Knee Score (OKS) were collected. Mean follow-up time was 21 months (6 to 36).Aims
Methods
In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA). This large-scale, single-institution study included patients of any age undergoing primary TKA (n = 1,375) or UKA (n = 337) for any cause between May 2019 and January 2023. Data extracted included patient demographics, LOS, need for post anaesthesia care unit (PACU) admission, anaesthesia type, readmission within 30 days, and discharge dispositions. Univariate and multivariate logistic regression models were also employed to identify factors and patient characteristics related to delayed discharge.Aims
Methods
