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

Abstract

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 (β±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.

Clinical relevance: Females who demonstrate increased KAM are more responsive and more likely to benefit from neuromuscular training. These findings should significantly enhance the identification of those at increased risk and facilitate neuromuscular training targeted to this important risk factor (high KAM) for ACL injury.

Figure 1

Scatter plot of data (N=399) utilised to define the cut-point used to classify the dependent variable high or low knee abduction moment status. Data were treated with logistic regression analyses to determine the value that would provide the maximal sensitivity and specificity for prediction of anterior cruciate ligament (ACL) injury risk during a drop vertical jump.

Figure 2

Videographic depiction of athlete with kinematic pattern that is likely to demonstrate a high knee abduction moment.

Figure 3

Motion analysis depiction of athlete with kinematic and kinetic pattern indicative of a high knee abduction moment.

Figure 4

Receiver operating characteristic (ROC) curve determined in training dataset (N=598) for prediction of high knee abduction moment. The cross bar indicates the maximum sensitivity and specificity of the prediction model. Sn, sensitivity; Sp specificity.