New method to identify athletes at high risk of ACL injury using clinic-based measurements and freeware computer analysis.

Abstract

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. 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%). 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.