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This multicenter prospective observational study aims to evaluate whether preoperative clinical variables and wearable sensor-derived gait features can predict postoperative improvement after total knee arthroplasty (TKA). Participants will undergo standardized gait assessments using instrumented insoles and complete validated patient-reported outcome measures (PROMs). Prediction models including linear regression, random forest, and deep neural networks will be applied and their performance compared.
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This study investigates predictive factors associated with postoperative improvement in both patient-reported outcomes and performance-based mobility measures among individuals undergoing total knee arthroplasty. Participants from two centers-Yongin Severance Hospital and Gachon University Gil Medical Center-will undergo preoperative collection of demographic data, body composition, muscle strength, comorbidity profiles, radiographic Kellgren-Lawrence grade, and wearable insole-derived gait data during the Timed Up and Go Test (TUGT). Postoperative outcomes at six weeks will include Western Ontario and McMaster Universities Arthritis Index (WOMAC) and TUGT.
Prediction models (linear regression, random forest, and deep neural network models) will be applied using consistent input variables. Model performance will be evaluated using accuracy and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). This study does not involve any therapeutic intervention and poses minimal risk.
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200 participants in 1 patient group
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Seung Ick Choi; Na Young Kim, MD, PhD
Data sourced from clinicaltrials.gov
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