Three-Dimensional Motion Analysis Validation of a Clinic-Based Nomogram Designed to Identify High ACL Injury Risk in Female Athletes
Aims: Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at increased risk for anterior cruciate ligament (ACL) injury. Laboratory-driven measurements predict high KAM with 90% accuracy. This study aimed to validate the clinic-based variables against 3-dimensional motion analysis measurements. Methods: Twenty female basketball, soccer, and volleyball players (age, 15.9 ± 1.3 years; height, 163.6 ± 9.9 cm; body mass, 57.0 ± 12.1 kg) were tested using 3-dimensional motion analysis and clinic-based techniques simultaneously. Multiple logistic regression models have been developed to predict high KAM (a surrogate for ACL injury risk) using both measurement techniques. Clinic-based measurements were validated against 3-dimensional motion analysis measures, which were recorded simultaneously, using within- and between-method reliability as well as sensitivity and specificity comparisons. Results: The within-variable analysis showed excellent inter-rater reliability for all variables using both 3-dimensional motion analysis and clinic-based methods, with intraclass correlation coefficients (ICCs) that ranged from moderate to high (0.60-0.97). In addition, moderate-to-high agreement was observed between 3-dimensional motion analysis and clinic-based measures, with ICCs ranging from 0.66 to 0.99. Bland-Altman plots confirmed that each variable provided no systematic shift between 3-dimensional motion analysis and clinic-based methods, and there was no association between difference and average. A developed regression equation also supported model validity with > 75% prediction accuracy of high KAM using both the 3-dimensional motion analysis and clinic-based techniques. Conclusion: The current validation provides the critical next step to merge the gap between laboratory identification of injury risk factors and clinical practice. Implementation of the developed prediction tool to identify female athletes with high KAM may facilitate the entry of female athletes with high ACL injury risk into appropriate injury-prevention programs.
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