Can TKA outcomes be predicted with computational simulation? Generation of a patient specific planning tool
| Autor: | S McMahon, David Parker, David Liu, Len Walter, Justin P. Roe, Brad Miles, Michael Solomon, David Dickison, Andrew Shimmin, J Twiggs, Brett Fritsch, Richard Boyle, Jonathan BarBo |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Multivariate statistics Knee Joint business.industry Context (language use) Kinematics Osteoarthritis Osteoarthritis Knee medicine.disease Outcome (probability) Regression Biomechanical Phenomena Physical medicine and rehabilitation medicine Humans Orthopedics and Sports Medicine Patient-reported outcome Computer Simulation Range of Motion Articular business Arthroplasty Replacement Knee Balance (ability) |
| Zdroj: | The Knee. 33 |
| ISSN: | 1873-5800 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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