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

Gregory D. Myer, Kevin R. Ford, Jane Khoury, Paul Succop, Timothy Hewett

Research output: Contribution to journalArticle

99 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)245-252
Number of pages8
JournalBritish Journal of Sports Medicine
Volume45
Issue number4
DOIs
StatePublished - 2011
Externally publishedYes

Fingerprint

Biomechanical Phenomena
Athletes
Knee
Logistic Models
Anterior Cruciate Ligament Injuries
Tibia
Body Mass Index
Basketball
Sensitivity and Specificity
Soccer
Articular Range of Motion
Linear Models

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation

Cite this

Biomechanics laboratory-based prediction algorithm to identify female athletes with high knee loads that increase risk of ACL injury. / Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy.

In: British Journal of Sports Medicine, Vol. 45, No. 4, 2011, p. 245-252.

Research output: Contribution to journalArticle

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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.",
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