It is unknown whether gap laxities measured in robotic arm-assisted total knee arthroplasty (TKA) correlate to load sensor measurements. The aim of this study was to determine whether symmetry of the maximum medial and lateral gaps in extension and flexion was predictive of knee balance in extension and flexion respectively using different maximum thresholds of intercompartmental load difference (ICLD) to define balance. A prospective cohort study of 165 patients undergoing functionally-aligned TKA was performed (176 TKAs). With trial components in situ, medial and lateral extension and flexion gaps were measured using robotic navigation while applying valgus and varus forces. The ICLD between medial and lateral compartments was measured in extension and flexion with the load sensor. The null hypothesis was that stressed gap symmetry would not correlate directly with sensor-defined soft tissue balance.Aims
Methods
Aims. 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). Methods. 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. Results. The median LOS in the RO TKA group was 76 hours (interquartile range (IQR) 54 to 104) versus 82.5 (IQR 58 to 127) in the CO TKA group (p < 0.001) and 54 hours (IQR 34 to 77) in the RO UKA versus 58 (IQR 35 to 81) in the CO UKA (p = 0.031). Discharge dispositions were comparable between the two groups. A higher percentage of patients undergoing CO TKA required PACU admission (8% vs 5.2%; p = 0.040). Conclusion. Our study showed that
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
