Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study
Year: 2023
Type: article
Abstract: ABSTRACT Objective To 1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over two years in individuals without or with early knee osteoarthritis and 2) identify influential predictors in the model and quantify their effect on cartilage worsening. Design An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure statistic, and their marginal effect on the outcome was quantified by g-computation. Results Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5 th -97.5 th percentile) AUC across the 100 held-out test sets was 0.73 (0.65-0.79). Presence of baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying, and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Conclusions An ensemble machine learning approach incorporating gait, physical activity, and clinical/demographic features showed good performance for predicting cartilage worsening over two years. While identifying potential intervention targets from the model is challenging, these results suggest that lateral ground reaction force impulse, time spent lying, and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening. Summary box What are the findings? Machine learning models predicted cartilage worsening in persons without or with early knee osteoarthritis from gait, physical activity, and clinical and demographic characteristics with a median AUC of 0.73 across 100 held-out test sets. High lateral ground reaction force impulse, more time spent lying, and low vertical ground reaction force unloading rate were associated with increased risk of cartilage worsening over two years. How might it impact on clinical practice in the future? Gait and physical activity are some of the only modifiable risk factors for knee osteoarthritis; this study identified three potential intervention targets to slow early knee osteoarthritis progression.
Institutions: Boston University, University of Florida, VA Boston Healthcare System, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsklinikum Erlangen, University of Iowa, University of Kansas Medical Center, University of Alabama at Birmingham, University of California, San Francisco
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funders: U.S. Department of Health and Human Services, American Heart Association, Rheumatology Research Foundation, National Institutes of Health, University of California, San Francisco, National Institute on Aging, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Center for Advancing Translational Sciences
Awards: Effect of femoroacetabular impingement (FAI) on hip motion in young adults, G1457068928, G1484819271, G1542614369, G1616118084, G1711849593, G1933960382, G2371294767, G2768209352, G2980328272, G3206704046, Mechanisms of drug-coated balloon therapy, Integrated Digital Technology Platform for Optimization of Precision Brain Health, G4343320590, PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learning, Multicenter Osteoarthritis Study (MOST) Second Renewal - UAB Clinical Center, G477853788, G4993145128, G501991607, Multicenter Osteoarthritis Study (MOST) Second Renewal, Machine learning for analysis of walking patterns and physical activity in knee osteoarthritis, Dynamic Treatment Regimens of Physical Activity for Persons with Osteoarthritis, G5724853671, Cognitive Heterogeneity in those with high Alzheimer's Disease Risk, Mind Your Walk Intervention for Community-Based Management of Knee OA: A Feasibility Study, G8067279463, G826477285, Multicenter Osteoarthritis Study (MOST) Second Renewal - Boston University, G9000024889, G9000039886
OpenAlex ID: W4323067006