Decision Tree Learning Algorithm for Classifying Knee Injury Status Using Return-to-Activity Criteria

Abstract

Anterior cruciate ligament (ACL) injury rates in female adolescents are increasing. Irrespective of treatment options, approximately 1/3 will suffer secondary ACL injuries following their return to activity (RTA). Despite this, there are no evidence-informed RTA guidelines to aid clinicians in deciding when this should occur. The first step towards these guidelines is to identify relevant and feasible measures to assess the functional status of these patients. The purpose of this study was therefore to evaluate tests frequently used to assess functional capacity following surgery using a Reduced Error Pruning Tree (REPT). Thirty-six healthy and forty-two ACLinjured adolescent females performed a series of functional tasks. Motion analysis along with spatiotemporal measures were used to extract thirty clinically relevant variables. The REPT reduced these variables down to two limb symmetry measures (maximum anterior hop and maximum lateral hop), capable of classifying injury status between the healthy and ACL injured participants with a 69% sensitivity, 78% specificity and kappa statistic of 0.464. We, therefore, conclude that the REPT model was able to evaluate functional capacity as it relates to injury status in adolescent females. We also recommend considering these variables when developing RTA assessments and guidelines. Clinical Relevance-Our results indicate that spatiotemporal measures may differentiate ACL-injured and healthy female adolescents with moderate confidence using a REPT. The identified tests may reasonably be added to the clinical evaluation process when evaluating functional capacity and readiness to return to activity.

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