Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm

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

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

Background: Prospective measures of high knee abduction moment during landing identify female athletes at high risk for non-contact anterior cruciate ligament injury. Biomechanical laboratory measurements predict high knee abduction moment landing mechanics with high sensitivity (85%) and specificity (93%). The purpose of this study was to identify correlates to laboratory-based predictors of high knee abduction moment for use in a clinic-based anterior cruciate ligament injury risk prediction algorithm. The hypothesis was that clinically obtainable correlates derived from the highly predictive laboratory-based models would demonstrate high accuracy to determine high knee abduction moment status. Methods: Female basketball and soccer players (N = 744) were tested for anthropometrics, strength and landing biomechanics. Pearson correlation was used to identify clinically feasible correlates and logistic regression to obtain optimal models for high knee abduction moment prediction. Findings: Clinical correlates to laboratory-based measures were identified and predicted high knee abduction moment status with 73% sensitivity and 70% specificity. The clinic-based prediction algorithm, including (Odds Ratio: 95% confidence interval) knee valgus motion (1.43:1.30-1.59 cm), knee flexion range of motion (0.98:0.96-1.01°), body mass (1.04:1.02-1.06 kg), tibia length (1.38:1.25-1.52 cm) and quadriceps to hamstring ratio (1.70:1.06-2.70) predicted high knee abduction moment status with C statistic 0.81. Interpretation: The combined correlates of increased knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio predict high knee abduction moment status in female athletes with high sensitivity and specificity. Clinical Relevance: Utilization of clinically obtainable correlates with the prediction algorithm facilitates high non-contact anterior cruciate ligament injury risk athletes' entry into appropriate interventions with the greatest potential to prevent injury.

Original languageEnglish (US)
Pages (from-to)693-699
Number of pages7
JournalClinical Biomechanics
Volume25
Issue number7
DOIs
StatePublished - Aug 2010
Externally publishedYes

Fingerprint

Knee
Athletes
Articular Range of Motion
Tibia
Sensitivity and Specificity
Anterior Cruciate Ligament Injuries
Basketball
Soccer
Mechanics
Biomechanical Phenomena
Logistic Models
Odds Ratio
Confidence Intervals
Wounds and Injuries

Keywords

  • ACL injury prevention
  • ACL injury risk factors
  • Assessment tools
  • Biomechanics correlated to increased ACL injury risk
  • Clinician friendly
  • Targeted neuromuscular training

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Biophysics

Cite this

Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm. / Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy.

In: Clinical Biomechanics, Vol. 25, No. 7, 08.2010, p. 693-699.

Research output: Contribution to journalArticle

Myer, Gregory D. ; Ford, Kevin R. ; Khoury, Jane ; Succop, Paul ; Hewett, Timothy. / Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm. In: Clinical Biomechanics. 2010 ; Vol. 25, No. 7. pp. 693-699.
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abstract = "Background: Prospective measures of high knee abduction moment during landing identify female athletes at high risk for non-contact anterior cruciate ligament injury. Biomechanical laboratory measurements predict high knee abduction moment landing mechanics with high sensitivity (85{\%}) and specificity (93{\%}). The purpose of this study was to identify correlates to laboratory-based predictors of high knee abduction moment for use in a clinic-based anterior cruciate ligament injury risk prediction algorithm. The hypothesis was that clinically obtainable correlates derived from the highly predictive laboratory-based models would demonstrate high accuracy to determine high knee abduction moment status. Methods: Female basketball and soccer players (N = 744) were tested for anthropometrics, strength and landing biomechanics. Pearson correlation was used to identify clinically feasible correlates and logistic regression to obtain optimal models for high knee abduction moment prediction. Findings: Clinical correlates to laboratory-based measures were identified and predicted high knee abduction moment status with 73{\%} sensitivity and 70{\%} specificity. The clinic-based prediction algorithm, including (Odds Ratio: 95{\%} confidence interval) knee valgus motion (1.43:1.30-1.59 cm), knee flexion range of motion (0.98:0.96-1.01°), body mass (1.04:1.02-1.06 kg), tibia length (1.38:1.25-1.52 cm) and quadriceps to hamstring ratio (1.70:1.06-2.70) predicted high knee abduction moment status with C statistic 0.81. Interpretation: The combined correlates of increased knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio predict high knee abduction moment status in female athletes with high sensitivity and specificity. Clinical Relevance: Utilization of clinically obtainable correlates with the prediction algorithm facilitates high non-contact anterior cruciate ligament injury risk athletes' entry into appropriate interventions with the greatest potential to prevent injury.",
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AB - Background: Prospective measures of high knee abduction moment during landing identify female athletes at high risk for non-contact anterior cruciate ligament injury. Biomechanical laboratory measurements predict high knee abduction moment landing mechanics with high sensitivity (85%) and specificity (93%). The purpose of this study was to identify correlates to laboratory-based predictors of high knee abduction moment for use in a clinic-based anterior cruciate ligament injury risk prediction algorithm. The hypothesis was that clinically obtainable correlates derived from the highly predictive laboratory-based models would demonstrate high accuracy to determine high knee abduction moment status. Methods: Female basketball and soccer players (N = 744) were tested for anthropometrics, strength and landing biomechanics. Pearson correlation was used to identify clinically feasible correlates and logistic regression to obtain optimal models for high knee abduction moment prediction. Findings: Clinical correlates to laboratory-based measures were identified and predicted high knee abduction moment status with 73% sensitivity and 70% specificity. The clinic-based prediction algorithm, including (Odds Ratio: 95% confidence interval) knee valgus motion (1.43:1.30-1.59 cm), knee flexion range of motion (0.98:0.96-1.01°), body mass (1.04:1.02-1.06 kg), tibia length (1.38:1.25-1.52 cm) and quadriceps to hamstring ratio (1.70:1.06-2.70) predicted high knee abduction moment status with C statistic 0.81. Interpretation: The combined correlates of increased knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio predict high knee abduction moment status in female athletes with high sensitivity and specificity. Clinical Relevance: Utilization of clinically obtainable correlates with the prediction algorithm facilitates high non-contact anterior cruciate ligament injury risk athletes' entry into appropriate interventions with the greatest potential to prevent injury.

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