Journal Article Validation Study Ppublish
PubMed: 42845144 DOI: 10.1002/jor.70283
Development and External Validation of Machine Learning Models Using Lower-Extremity Musculoskeletal Assessments and Patient-Reported Outcomes to Classify ACL Reconstruction Status.
J Orthop Res 44(10):e70283
Authors: Trinity A Morrow, Joe M Hart, Amanda E Nelson, Liubov Arbeeva, Devin K Kelly
Affiliations
- School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA. Department of Orthopaedics, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA.
- Department of Orthopaedics, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA.
- School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA.
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract
Recovery following anterior cruciate ligament reconstruction (ACLR) is multifaceted, encompassing strength, functional, and patient-reported outcome measures (PROMs). Determining which factors are most important for characterizing recovery and informing return-to-activity (RTA) decisions remains a clinical challenge. This study aimed to develop and externally validate a supervised machine learning model to classify individuals post-ACLR from healthy controls using comprehensive lower-extremity musculoskeletal assessments. Data from 863 participants (659 post-ACLR, 204 controls) at Site 1 were used to train and internally test Random Forest, Gradient Boosting Machine, and Extreme Gradient Boosting (XGBoost) models, with an 80/20 training-testing split and inner fivefold cross-validation for hyperparameter optimization. Model performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC), and external validation was performed on an independent Site 2 cohort (N = 174; 133 post-ACLR, 41 controls). The XGBoost model was selected for further analysis (internal test AUC = 0.98, Accuracy = 0.95), and maintained good discrimination on external validation (AUC = 0.83, Accuracy = 0.82). The most informative features included the Knee Osteoarthritis Outcome Score quality-of-life subscale, limb symmetry index for peak knee extension torque at 90°/s, and the International Knee Documentation Committee score, which were consistent across age and sex subgroups. These findings demonstrate that machine learning can effectively integrate strength, function, and PROM data to identify key indicators of ACLR status and support development of streamlined, data-driven protocols for post-ACLR recovery monitoring and RTA decision-making. STATEMENT OF CLINICAL SIGNIFICANCE: The Knee Osteoarthritis Outcome Score quality-of-life subscale, limb symmetry index for peak knee extension torque at 90°/s, and the International Knee Documentation Committee score most effectively distinguish individuals following ACLR from healthy controls. Clinicians may focus on these tests to determine ACLR recovery status as a feasible streamlined assessment battery or when results from a comprehensive testing battery are conflicting or inconclusive.
MeSH
Research Design, Anterior Cruciate Ligament Reconstruction, Recycling, Machine Learning, Orthopedics, Patient Reported Outcome Measures, Learning