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Intraoperative passive knee kinematics during total knee arthroplasty surgery
Authors:Kathryn L. Young  Michael J. Dunbar  Glen Richardson  Janie L. Astephen Wilson
Affiliation:1. School of Biomedical Engineering, Dalhousie University, Dentistry Building, city, Halifax, NS, Canada;2. Department of Surgery, Dalhousie University, Halifax, NS, Canada
Abstract:
Surgical navigation systems for total knee arthroplasty (TKA) surgery are capable of capturing passive three‐dimensional (3D) angular joint movement patterns intraoperatively. Improved understanding of patient‐specific knee kinematic changes between pre and post‐implant states and their relationship with post‐operative function may be important in optimizing TKA outcomes. However, a comprehensive characterization of the variability among patients has yet to be investigated. The objective of this study was to characterize the variability within frontal plane joint movement patterns intraoperatively during a passive knee flexion exercise. Three hundred and forty patients with severe knee osteoarthritis (OA) received a primary TKA using a navigation system. Passive kinematics were captured prior to (pre‐implant), and after prosthesis insertion (post‐implant). Principal component analysis (PCA) was used to capture characteristic patterns of knee angle kinematics among patients, to identify potential patient subgroups based on these patterns, and to examine the subgroup‐specific changes in these patterns between pre‐ and post‐implant states. The first four extracted patterns explained 99.9% of the diversity within the frontal plane angle patterns among the patients. Post‐implant, the magnitude of the frontal plane angle shifted toward a neutral mechanical axis in all phenotypes, yet subtle pattern (shape of curvature) features of the pre‐implant state persisted. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 33:1611–1619, 2015.
Keywords:surgical navigation  computer‐assisted surgery  knee kinematics  total knee arthroplasty  principal component analysis
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