A Predictive Model to Estimate Knee-Abduction Moment: Implications for Development of a Clinically Applicable Patellofemoral Pain Screening Tool in Female Athletes

作者：Myer Gregory D^*; Ford Kevin R; Foss Kim D Barber; Rauh Mitchell J; Paterno Mark V; Hewett Timothy E

来源：Journal of Athletic Training, 2014, 49(3): 389-398.

DOI：10.4085/1062-6050-49.2.17

• high-risk biomechanics
• patellofemoral risk factors
• targeted neuromuscular training
• knee injury prevention
• assessment tools

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. %26lt;br%26gt;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. %26lt;br%26gt;Design: Descriptive laboratory study. %26lt;br%26gt;Setting: Biomechanical laboratory. %26lt;br%26gt;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 (%26gt;15.4 Nm) compared with normal KAM as a surrogate for PFP injury risk. %26lt;br%26gt;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 (%26gt;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. %26lt;br%26gt;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.

• 出版日期2014-6