首页> 外文期刊>Journal of athletic training >A Predictive Model to Estimate Knee-Abduction Moment: Implications for Development of a Clinically Applicable Patellofemoral Pain Screening Tool in Female Athletes
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A Predictive Model to Estimate Knee-Abduction Moment: Implications for Development of a Clinically Applicable Patellofemoral Pain Screening Tool in Female Athletes

机译:一种估计模型,以估计膝关节外展力矩:对女运动员临床适用的ello股骨疼痛筛查工具的发展意义

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Context: Prospective measures of high external knee-abduction moment (KAM) during landing identify female athletes at increased risk of patellofemoral pain (PFP). A clinically applicable screening protocol is needed. Objective: To identify biomechanical laboratory measures that would accurately quantify KAM loads during landing that predict increased risk of PFP in female athletes and clinical correlates to laboratory-based measures of increased KAM status for use in a clinical PFP injury-risk prediction algorithm. We hypothesized that we could identify clinical correlates that combine to accurately determine increased KAM associated with an increased risk of developing PFP. Design: Descriptive laboratory study. Setting: Biomechanical laboratory. Patients or Other Participants: Adolescent female basketball and soccer players (n = 698) from a single-county public school district. Main Outcome Measure(s): We conducted tests of anthropometrics, maturation, laxity, flexibility, strength, and landing biomechanics before each competitive season. Pearson correlation and linear and logistic regression modeling were used to examine high KAM (>15.4 Nm) compared with normal KAM as a surrogate for PFP injury risk. Results: The multivariable logistic regression model that used the variables peak knee-abduction angle, center-of-mass height, and hip rotational moment excursion predicted KAM associated with PFP risk (>15.4 NM of KAM) with 92% sensitivity and 74% specificity and a C statistic of 0.93. The multivariate linear regression model that included the same predictors accounted for 70% of the variance in KAM. We identified clinical correlates to laboratory measures that combined to predict high KAM with 92% sensitivity and 47% specificity. The clinical prediction algorithm, including knee-valgus motion (odds ratio [OR] = 1.46, 95% confidence interval [CI] = 1.31, 1.63), center-of-mass height (OR = 1.21, 95% CI = 1.15, 1.26), and hamstrings strength/body fat percentage (OR = 1.80, 95% CI = 1.02, 3.16) predicted high KAM with a C statistic of 0.80. Conclusions: Clinical correlates to laboratory-measured biomechanics associated with an increased risk of PFP yielded a highly sensitive model to predict increased KAM status. This screening algorithm consisting of a standard camcorder, physician scale for mass, and handheld dynamometer may be used to identify athletes at increased risk of PFP.
机译:背景:降落期间高外部膝关节外展力矩(KAM)的前瞻性措施可确定女性运动员of股股骨疼痛(PFP)的风险增加。需要临床适用的筛选方案。目的:确定能够准确量化着陆过程中KAM负荷的生物力学实验室措施,这些措施可预测女运动员PFP的风险增加,并与基于实验室的KAM状况增加的实验室措施相关,以用于临床PFP损伤风险预测算法。我们假设,我们可以确定临床相关因素,以准确确定增加的KAM与发生PFP的风险相关。设计:描述性实验室研究。地点:生物力学实验室。患者或其他参与者:来自单县公立学区的青少年女子篮球和足球运动员(n = 698)。主要指标:在每个比赛季节之前,我们进行了人体测量学,成熟度,松弛度,柔韧性,力量和降落生物力学测试。与普通KAM相比,Pearson相关性以及线性和逻辑回归模型用于检查高KAM(> 15.4 Nm)作为PFP损伤风险的替代指标。结果:使用变量峰值膝关节外展角度,重心高度和髋关节旋转力矩偏移的多变量logistic回归模型以92%的敏感性和74%的敏感性预测了与PFP风险(> 15.4 NM的KAM)相关的KAM。特异性为%,C统计值为0.93。包含相同预测变量的多元线性回归模型占KAM方差的70%。我们确定了与实验室措施相关的临床相关因素,这些措施结合起来可以预测高KAM,敏感性为92%,特异性为47%。临床预测算法,包括膝外翻运动(比值比[OR] = 1.46,95 %置信区间[CI] = 1.31、1.63),质心高度(OR = 1.21,95 %CI = 1.15) ,1.26)和绳肌力量/身体脂肪百分比(OR = 1.80,95%CI = 1.02,3.16)预测了较高的KAM,C统计量为0.80。结论:临床与实验室测量的生物力学相关,与PFP风险增加有关,产生了一种高度敏感的模型来预测KAM状态的增加。此筛选算法由标准便携式摄录机,质量用医生秤和手持测功机组成,可用于识别运动员中PFP风险增加。

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