Quadriceps Strength Predicts Self-reported Function Post ACL Reconstruction.

Introduction/Purpose: Quadriceps strength is a useful clinical predictor of self-reported function following anterior cruciate ligament reconstruction (ACLR). However, it remains unknown if quadriceps strength normalized to body mass (QBM) or quadriceps strength limb symmetry index (QLSI) is the best predictor of self-reported function in individuals with ACLR. We sought to determine if QBM and QLSI are able to predict individuals with ACLR who self-report high function (>= 90% on the International Knee Documentation Committee Index [IKDC]). METHODS: Ninety-six individuals with a history of a primary unilateral ACLR were recruited for a multi-site cross-sectional descriptive laboratory experiment. Bilateral isometric quadriceps strength was collected at 90[degrees] of knee flexion to calculate QBM and QLSI (ratio of ACLR limb to the contralateral limb). Area under the curve (AUC) values were calculated using Receiver-Operator Characteristic curve analyses to determine the capacity of QBM and QLSI to predict individuals with high self-reported function on the IKDC. RESULTS: QBM displayed high accuracy (AUC= 0.76, 95%CI 0.66-0.86) for identifying participants with an IKDC >= 90%. A QBM cutoff score of 3.10 Nm/kg was found to maximize sensitivity (0.61) and specificity (0.84), and displayed 8.15 (3.09-21.55) times higher odds of reporting high function. QLSI displayed a moderate accuracy (AUC= 0.62,0.50-0.73) for identifying participants with an IKDC >= 90%. A QLSI cutoff score of 96.5% maximized sensitivity (0.55) and specificity (0.70), and represented 2.78 (1.16-6.64) times higher odds reporting high function. CONCLUSION: QBM is a stronger predictor of high self-reported function compared to QLSI in individuals with ACLR. Rehabilitation guidelines may benefit from incorporating the use of QBM measurements for the purpose of predicting participants that may maintain high self-reported function. (C) 2016 American College of Sports Medicine

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