INFLUENCE OF LOWER EXTREMITY STATIC ALIGNMENT ON DYNAMIC KNEE VALGUS IN ADOLESCENTS FOLLOWING ACL RECONSTRUCTION. Issue 4 (28th April 2020)

• Record Type: Journal Article
• Authors: VandenBerg, Curtis D.; Mueske, Nicole M.; Abousamra, Oussama; Feifer, Daniel; Sarkisova, Natalya; Katzel, Mia J.; Pace, J. Lee; Wren, Tishya A. L.
• Is Part Of: Orthopaedic journal of sports medicine. Volume 8:Issue 4(2020)Supplement 3
• Publication Date: 2020-04-28
• Subjects: Sports medicine -- Periodicals; Orthopedics -- Periodicals; Arthroscopy -- Periodicals; Arthroplasty -- Periodicals; Knee -- Surgery -- Periodicals; 616.7
• DOI: 10.1177/2325967120S00144
• Languages: English
• ISSNs: 2325-9671

Abstract

Background: Dynamic limb valgus, particularly high knee abduction moments, are a known risk factor for anterior cruciate ligament (ACL) injury. High knee abduction moments may result from poor static anatomic limb alignment, faulty biomechanics, or a combination of both. The distinction is important because anatomic limb alignment is difficult to change, while dynamic factors can be addressed through neuromuscular or biomechanical training. Hypothesis/Purpose: This study assessed the influence of static (lower extremity anatomic alignment) and dynamic (kinematic and kinetic) factors on external knee abduction moments during side-step cutting in uninjured adolescent athletes. Methods: This retrospective study included 43 adolescents with recent unilateral ACL reconstruction (mean age 15.3 years, SD 2.0, range 10-21; 17/43 female; 3-12 months post-surgery, mean 6.5, SD 2.1). Frontal plane hip to ankle imaging (EOS) was used to measure mechanical axis deviation (perpendicular distance from the center of the femoral condyles to the mechanical axis line connecting the center of the femoral head to the center of the talar dome) and tibial-femoral angle. Femoral anteversion was measured during physical examination. 3D motion capture provided lower extremity kinematics and kinetics during quiet standing and during the loading phase (initial contact to peak knee flexion) of an anticipated 45° side-step cut, with 2-3 trials per limb averaged for analysis. Relationships among imaging, static motion capture, and dynamic motion capture measures were investigated using correlation, and backward stepwise linear regression was used to evaluate potential predictors of average dynamic knee abduction moment. Results: Dynamic knee abduction moment was best predicted by a combination of dynamic measures: knee and hip abduction, external knee rotation, lateral trunk lean towards the planting foot, and ankle inversion during cutting (Table 1.1). Although EOS frontal plane tibial-femoral angle was correlated with dynamic knee abduction moment (r=0.24, p=0.02), no static/anatomic variables entered the model once the dynamic measures were included. Conclusion: Knee abduction moments during side-step cutting were related to dynamic factors reflecting frontal plane trunk, hip, knee, and ankle motion, as well as external knee rotation. Static (anatomic) lower limb alignment did not influence knee abduction moments once these dynamic factors were considered. Knee abduction moments and ACL injury risk are therefore not dictated by anatomic alignment and can be altered through neuromuscular/biomechanical training. Table 1.1. Multivariable prediction of average knee abduction moment during cutting (R 2 =0.69) β P Dynamic ankle eversion -0.35 <0.001 Dynamic knee external rotation 0.69 <0.001 Dynamic hip abduction 0.18 0.004 Dynamic trunk contralateral lean 0.17 0.007 Dynamic knee abduction 0.39 <0.001