Component position and overall limb alignment following Total Knee Arthroplasty (TKA) have been shown to influence device survivorship and clinical outcomes. However current methods for measuring post-operative alignment through 2D radiographs and CTs may be prone to inaccuracies due to variations in patient positioning, and certain anatomical configurations such as rotation and flexion contractures. The purpose of this paper is to develop a new vector based method for overall limb alignment and component position measurements using CT. The technique utilizes a new mathematical model to calculate prosthesis alignment from the coordinates of anatomical landmarks. The hypothesis is that the proposed technique demonstrated good accuracy to surgical plan, as well as low intra and inter-observer variability. This study received institutional review board approval. A total of 30 patients who underwent robotic assisted TKA (RATKA) at four different sites between March 2017 and January 2018 were enrolled in this prospective, multicenter, non-randomized clinical study. CT scans were performed prior to and 4–6 weeks post-operatively. Each subject was positioned headfirst supine with the legs in a neutral position and the knees at full extension. Three separate CT scans were performed at the anatomical location of the hip, knee, and ankle joint. Hip, knee, and ankle images were viewed in 3D software and the following vertices were generated using anatomical landmarks: Hip Center (HC), Medial Epicondyle Sulcus (MES), Lateral Epicondyle (LE), Femur Center (FC), Tibia Center (TC), Medial Malleolus (MM), Lateral Malleolus (LM), Femur Component Superior (FCS), Femur Component Inferior (FCI), Coronal Femoral Lateral (CFL), Coronal Femoral Medial (CFM), Coronal Tibia Lateral (CTL), and Coronal Tibia Medial (CTM). Limb alignment and component positions were calculated from these vertices using a new mathematical model. The measurements were compared to the surgeons’ operative plan and component targeted positions for accuracy analysis. Two analysts performed the same measurements separately for inter-observer variability analysis. One of the two analysts repeated the measurements at least 30 days apart to assess intra-observer variability. Correlation analysis was performed on the intra-observer analysis, while Bland Altman analysis was performed on the inter-observer analysis.Introduction
Methods
