Development Of A Clinic Based Prediction Tool To Identify High ACL Injury Risk Female Athletes

Abstract

Prospective measures of high knee abduction moment (>25.25Nm; HIGHLOAD) during landing identify female athletes at high risk for ACL injury. Laboratory driven (LAB based) measurements predict HIGHLOAD with high sensitivity (85%) and specificity (93%). PURPOSE: To develop a "clinician friendly" landing assessment tool (CLINIC based) for increased ACL injury risk. We hypothesized that clinically obtainable correlates derived from the highly predictive LAB based models would demonstrate high accuracy to determine HIGHLOAD status. METHODS: Female basketball and soccer players (N=744) from a single county public school district were tested for anthropometrics, strength and landing biomechanics. Logistic regression was used to examine predictors of HIGHLOAD (used as a surrogate for ACL injury risk) and to develop the clinic based prediction nomogram (Figure 1).FIGURE 1: NO caption available.RESULTS: The prediction model, which included (Odds Ratio: 95% confidence interval) knee valgus motion (1.43:1.30-1.59 cm), knee flexion range of motion (.98:.96-1.01 deg), body mass (1.04: 1.02-1.06 kg), tibia length (1.38: 1.25-1.52 cm) and quadriceps to hamstring ratio (1.70: 1.06-2.70 %) predicted HIGHLOAD status with 73% sensitivity and 70% specificity with a C statistic of 0.81. CONCLUSIONS: Clinically feasible measurements predict HIGHLOAD with high sensitivity and specificity. Female athletes who demonstrate HIGHLOAD landing mechanics are at increased risk for ACL injury and are more likely to benefit from neuromuscular training targeted to this risk factor. Utilization of the developed CLINIC based assessment tool may facilitate high-risk athletes' entry into appropriate interventions that will have greater potential to reduce their injury risk.