Myer Gregory D, Ford Kevin R, Khoury Jane, Succop Paul, Hewett Timothy E
Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.
Clin Biomech (Bristol). 2010 Aug;25(7):693-9. doi: 10.1016/j.clinbiomech.2010.04.016.
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.
着陆时高膝外展力矩的前瞻性测量可识别非接触性前交叉韧带损伤风险较高的女性运动员。生物力学实验室测量对高膝外展力矩着陆力学的预测具有较高的敏感性（85%）和特异性（93%）。本研究的目的是确定与基于实验室的高膝外展力矩预测指标相关的因素，以用于基于临床的前交叉韧带损伤风险预测算法。假设是，从高度预测性的基于实验室的模型中得出的临床可获得的相关因素将具有高精度来确定高膝外展力矩状态。
对744名女子篮球和足球运动员进行人体测量、力量和着陆生物力学测试。使用Pearson相关性来识别临床可行的相关因素，并使用逻辑回归来获得高膝外展力矩预测的最佳模型。
确定了与基于实验室测量的临床相关因素，并以73%的敏感性和70%的特异性预测高膝外展力矩状态。基于临床的预测算法，包括（比值比：95%置信区间）膝内翻运动（1.43：1.30 - 1.59厘米）、膝关节屈伸范围（0.9
