Machine Learning Predictions of Subjective Function, Symptoms, and Psychological Readiness at 12 Months After ACL Reconstruction Based on Physical Performance in the Early Rehabilitation Stage: Retrospective Cohort Study.

IF 2.4 3区 医学 Q2 ORTHOPEDICS
Orthopaedic Journal of Sports Medicine Pub Date : 2025-03-03 eCollection Date: 2025-03-01 DOI:10.1177/23259671251319512
Ui-Jae Hwang, Jin-Seong Kim, Kyu Sung Chung
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引用次数: 0

Abstract

Background: Anterior cruciate ligament (ACL) reconstruction (ACLR) aims to restore knee stability and function; however, recovery outcomes vary widely, highlighting the need for predictive tools to guide rehabilitation and patient readiness.

Purpose: To identify the most effective machine learning models for predicting the successful recovery of Patient Acceptable Symptom State (PASS) in terms of subjective function, symptoms, and psychological readiness 12 months after ACLR using physical performance measures obtained 3 months after ACLR.

Study design: Cohort study; Level of evidence, 3.

Methods: The authors retrospectively analyzed the data of 113 patients who underwent single-bundle anatomic ACLR. Physical performance measures at 3 months after ACLR included the Y-balance and isokinetic muscle strength tests. The successful recovery of PASS outcomes at 12 months were assessed using the International Knee Documentation Committee (IKDC) and the ACL-Return to Sport after Injury (ACL-RSI) scale. Five machine learning algorithms were assessed: logistic regression, decision tree, random forest, gradient boosting, and support vector machines.

Results: The gradient boosting model demonstrated the highest area under the curve (AUC) scores for predicting SRPAS of the IKDC (AUC, 0.844; F1, 0.889), and the random forest model demonstrated the highest AUC scores for predicting the successful recovery of PASS of the ACL-RSI (AUC, 0.835; F1, 0.732) during test models. Key predictors of the successful recovery of PASS outcomes included young age and low deficits in the 60 deg/s flexor and extensor peak torque for the IKDC, low 180 deg/s extensor and flexor mean power deficit, and low 60 deg/s flexor peak torque deficits for the ACL-RSI.

Conclusion: Machine learning showed that younger age and greater 3-month isokinetic strength at 60 deg/s predicted attainment of the successful recovery of PASS of the IKDC at 1 year after ACL. Greater 3-month isokinetic strength at 180 deg/s was most predictive of attaining the successful recovery of PASS of the ACL-RSI at 12 months.

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来源期刊
Orthopaedic Journal of Sports Medicine
Orthopaedic Journal of Sports Medicine Medicine-Orthopedics and Sports Medicine
CiteScore
4.30
自引率
7.70%
发文量
876
审稿时长
12 weeks
期刊介绍: The Orthopaedic Journal of Sports Medicine (OJSM), developed by the American Orthopaedic Society for Sports Medicine (AOSSM), is a global, peer-reviewed, open access journal that combines the interests of researchers and clinical practitioners across orthopaedic sports medicine, arthroscopy, and knee arthroplasty. Topics include original research in the areas of: -Orthopaedic Sports Medicine, including surgical and nonsurgical treatment of orthopaedic sports injuries -Arthroscopic Surgery (Shoulder/Elbow/Wrist/Hip/Knee/Ankle/Foot) -Relevant translational research -Sports traumatology/epidemiology -Knee and shoulder arthroplasty The OJSM also publishes relevant systematic reviews and meta-analyses. This journal is a member of the Committee on Publication Ethics (COPE).
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