Can TKA outcomes be predicted with computational simulation? Generation of a patient specific planning tool.

Autor: Twiggs J; 360MedCare, Sydney 2073, Australia. Electronic address: Joshua@kneesystems.com., Miles B; 360MedCare, Sydney 2073, Australia., Roe J; North Sydney Orthopaedic and Sports Medicine Centre, The Mater Hospital, North Sydney 2060, Australia., Fritsch B; Sydney Orthopaedic Research Institute, Sydney 2067, Australia., Liu D; Gold Coast Centre for Bone and Joint Surgery, Gold Coast 4221, Australia., Parker D; Sydney Orthopaedic Research Institute, Sydney 2067, Australia., Dickison D; Peninsula Orthopaedics, Sydney 2099, Australia., Shimmin A; Melbourne Orthopaedic Group, Melbourne 3181, Australia., BarBo J; Melbourne Orthopaedic Group, Melbourne 3181, Australia., McMahon S; Malabar Orthopaedic Clinic, Melbourne 3181, Australia., Solomon M; Sydney Orthopaedic Specialists, Sydney 2031, Australia., Boyle R; Boyle Orthopaedics, Sydney 2042, Australia., Walter L; 360MedCare, Sydney 2073, Australia.
Jazyk: angličtina
Zdroj: The Knee [Knee] 2021 Dec; Vol. 33, pp. 38-48. Date of Electronic Publication: 2021 Sep 17.
DOI: 10.1016/j.knee.2021.08.029
Abstrakt: Background: Computer simulations of knee movement allow Total Knee Arthroplasty (TKA) dynamic outcomes to be studied. This study aims to build a model predicting patient reported outcome from a simulation of post-operative TKA joint dynamics.
Methods: Landmark localisation was performed on 239 segmented pre-operative computerized tomography (CT) scans to capture patient specific soft tissue attachments. The pre-operative bones and 3D implant files were registered to post-operative CT scans following TKA. Each post-operative knee was simulated undergoing a deep knee bend with assumed ligament balancing of the extension space. The kinematic results from this simulation were used in a Multivariate Adaptive Regression Spline algorithm, predicting attainment of a Patient Acceptable Symptom State (PASS) score in captured 12 month post-operative Knee Injury and Osteoarthritis Outcome Scores (KOOS). An independent series of 250 patients was evaluated by the predictive model to assess how the predictive model behaved in a pre-operative planning context.
Results: The generated predictive algorithm, called the Dynamic Knee Score (DKS) contained features, in order of significance, related to tibio-femoral force, patello-femoral motion and tibio-femoral motion. Area Under the Curve for predicting attainment of the PASS KOOS Score was 0.64. The predictive model produced a bimodal spread of predictions, reflecting a tendency to either strongly prefer one alignment plan over another or be ambivalent.
Conclusion: A predictive algorithm relating patient reported outcome to the outputs of a computational simulation of a deep knee bend has been derived (the DKS). Simulation outcomes related to tibio-femoral balance had the highest correlation with patient reported outcome.
Competing Interests: Declaration of Competing Interest The authors declare that two of the authors, JT and BM, are employees and shareholders in 360 MedCare, a commercial entity with an interest in commercialisation of simulation driven surgical planning technology.
(Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: