Background High knee abduction moment (KAM) landing mechanics, measured in the biomechanics laboratory, can successfully identify female athletes at increased risk for anterior cruciate ligament (ACL) injury.
Methods The authors validated a simpler, clinic-based ACL injury prediction algorithm to identify female athletes with high KAM measures. The validated ACL injury prediction algorithm employs the clinically obtainable measures of knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps-to-hamstrings ratio. It predicts high KAMs in female athletes with high sensitivity (77%) and specificity (71%).
Conclusion This report outlines the techniques for this ACL injury prediction algorithm using clinic-based measurements and computer analyses that require only freely available public domain software.
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Funding The authors would like to acknowledge funding support from National Institutes of Health Grant R01-AR049735, R01-AR055563 and R01-AR056259. The Cincinnati Children's Hospital Medical Center and Rocky Mountain University of Health Professions Institutional Review Boards approved this study.
Competing interests None.
Patient consent Obtained.
Ethics approval Ethics approval was provided by the Cincinnati Children's Hosptial and Rocky Mountain University.
Provenance and peer review Not commissioned; externally peer reviewed.
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