Article

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

Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.
Clinical biomechanics (Bristol, Avon) (Impact Factor: 1.76). 08/2010; 25(7):693-9. DOI: 10.1016/j.clinbiomech.2010.04.016
Source: PubMed

ABSTRACT 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.
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.
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 degrees ), 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.
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.
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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