Linear Discriminant Analysis Successfully Predicts Knee Injury Outcome From Biomechanical Variables.

The American Journal of Sports Medicine
Nathan D SchilatyTimothy E Hewett

Abstract

The most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). Controlled laboratory study. In vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysi...Continue Reading

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