Biomechanics laboratory-based prediction algorithm to identify female athletes with high knee loads that increase risk of ACL injury

Objective Knee abduction moment (KAM) during landing predicts non-contact anterior cruciate ligament (ACL) injury risk with high sensitivity and specificity in female athletes. The purpose of this study was to employ sensitive laboratory (lab-based) tools to determine predictive mechanisms that underlie increased KAM during landing. Methods Female basketball and soccer players (N=744) from a single county public school district were recruited to participate in testing of anthropometrics, maturation, laxity//flexibility, strength and landing biomechanics. Linear regression was used to model KAM, and logistic regression was used to examine high (> 25.25 Nm of KAM) versus low KAM as surrogate for ACL injury risk. Results The most parsimonious model included independent predictors (beta +/- 1 SE) (1) peak knee abduction angle (1.78 +/- 0.05; p < 0.001), (2) peak knee extensor moment (0.17 +/- 0.01; p < 0.001), (3) knee flexion range of motion (0.15 +/- 0.03; p < 0.01), (4) body mass index (BMI) Z-score (-1.67 +/- 0.36; p < 0.001) and (5) tibia length (-0.50 +/- 0.14; p < 0.001) and accounted for 78% of the variance in KAM during landing. The logistic regression model that employed these same variables predicted high KAM status with 85% sensitivity and 93% specificity and a C-statistic of 0.96. Conclusions Increased knee abduction angle, quadriceps recruitment, tibia length and BMI with decreased knee flexion account for 80% of the measured variance in KAM during a drop vertical jump.