TY - JOUR
T1 - Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm
AU - Myer, Gregory D.
AU - Ford, Kevin R.
AU - Khoury, Jane
AU - Succop, Paul
AU - Hewett, Timothy E.
N1 - Funding Information:
The authors would like to acknowledge funding support from the National Institutes of Health/NIAMS Grants R01-AR049735 , R01-AR05563 and R01-AR056259 . The authors would like to thank Boone County Kentucky, School District, especially School Superintendent Randy Poe, for participation in this study. We would also like to thank Mike Blevins, Ed Massey, Dr. Brian Blavatt and the athletes of Boone County public school district for their participation in this study. The authors would also like to acknowledge the Sports Medicine Biodynamics Team who worked together to make large data collection sessions possible. Finally, the authors would like to acknowledge Sam Wordeman for his assistance with the use of R-project software and Dr. Mitch Rauh for his helpful advice throughout this investigation. All authors are independent of any commercial funder, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
PY - 2010/8
Y1 - 2010/8
N2 - 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.
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.
KW - ACL injury prevention
KW - ACL injury risk factors
KW - Assessment tools
KW - Biomechanics correlated to increased ACL injury risk
KW - Clinician friendly
KW - Targeted neuromuscular training
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U2 - 10.1016/j.clinbiomech.2010.04.016
DO - 10.1016/j.clinbiomech.2010.04.016
M3 - Article
C2 - 20554101
AN - SCOPUS:77955654875
SN - 0268-0033
VL - 25
SP - 693
EP - 699
JO - Clinical Biomechanics
JF - Clinical Biomechanics
IS - 7
ER -