Elsevier

Clinical Biomechanics

Volume 25, Issue 7, August 2010, Pages 693-699
Clinical Biomechanics

Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm

https://doi.org/10.1016/j.clinbiomech.2010.04.016Get rights and content

Abstract

Background

Prospective measures of high knee abduction moment during landing identify female athletes at high risk for non-contact anterior cruciate ligament injury. Biomechanical laboratory measurements predict high knee abduction moment landing mechanics with high sensitivity (85%) and specificity (93%). The purpose of this study was to identify correlates to laboratory-based predictors of high knee abduction moment for use in a clinic-based anterior cruciate ligament injury risk prediction algorithm. The hypothesis was that clinically obtainable correlates derived from the highly predictive laboratory-based models would demonstrate high accuracy to determine high knee abduction moment status.

Methods

Female basketball and soccer players (N = 744) were tested for anthropometrics, strength and landing biomechanics. Pearson correlation was used to identify clinically feasible correlates and logistic regression to obtain optimal models for high knee abduction moment prediction.

Findings

Clinical correlates to laboratory-based measures were identified and predicted high knee abduction moment status with 73% sensitivity and 70% specificity. The clinic-based prediction algorithm, including (Odds Ratio: 95% confidence interval) knee valgus motion (1.43:1.30–1.59 cm), knee flexion range of motion (0.98:0.96–1.01°), 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 high knee abduction moment status with C statistic 0.81.

Interpretation

The combined correlates of increased knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio predict high knee abduction moment status in female athletes with high sensitivity and specificity.

Clinical Relevance

Utilization of clinically obtainable correlates with the prediction algorithm facilitates high non-contact anterior cruciate ligament injury risk athletes' entry into appropriate interventions with the greatest potential to prevent injury.

Introduction

Female athletes are reported to be four to six times more likely than males to sustain a sports related non-contact anterior cruciate ligament (ACL) injury (Arendt and Dick, 1995, Malone et al., 1993). Several investigators have demonstrated that female athletes exhibit high knee abduction moment (KAM) related landing mechanics more often than males during landing and pivoting movements (Ford et al., 2003, Ford et al., 2006, Malinzak et al., 2001, Hewett et al., 2004, Hewett et al., 2006b, Chappell et al., 2002, Mclean et al., 2004a, Kernozek et al., 2005, Zeller et al., 2003, Pappas et al., 2007). These altered neuromuscular strategies or decreased neuromuscular control of the lower extremity during the execution of sports movements may underlie the increased risk of ACL injury in female athletes (Ford et al., 2003, Ford et al., 2005, Hewett et al., 2005, Mclean et al., 2004b, Chappell et al., 2002, Myer et al., 2006b). Females often demonstrate knee landing alignments associated with high KAM at the time of injury, in validation of these laboratory findings (Olsen et al., 2004, Krosshaug et al., 2007, Boden et al., 2000). In addition, prospective measures related to knee abduction moment measured during drop vertical jump also predict ACL injury risk in young female athletes (Hewett et al., 2005) and in military cadets (Padua et al., 2009).

Calculation of KAM, through inverse dynamics, requires complex laboratory-based three-dimensional kinematic and kinetic measurement techniques. However, a recent report has isolated biomechanical measures that contribute to nearly 80% of the measured variance in KAM during landing (Myer et al., in press-a). These biomechanical predictors of KAM, which include increased knee abduction angle, increased relative quadriceps recruitment and decreased knee flexion range of motion (RoM), concomitant with increased tibia length and mass normalized to body height that accompanies growth, are also measurements that have all been related to increased risk of ACL injury in previous prospective and retrospective epidemiological reports (Boden et al., 2000, Uhorchak et al., 2003, Hewett et al., 2005, Padua et al., 2009). Unfortunately, expensive biomechanical laboratories, with the costly and labor intensive measurement tools to test individual athletes, are required to acquire these measurements. This restricts the potential to perform athlete risk assessments on a large scale, in particular limiting the potential to target high injury risk athletes with the appropriate intervention strategies. If simpler assessment tools are developed that can be administered in a clinic or field testing environment, which are validated by the highly accurate laboratory-based assessment, screening for ACL injury risk can be performed on a more widespread basis. The purpose of the current study was to identify potential correlates to laboratory-based predictors of high KAM for use in clinic-based ACL injury risk prediction algorithm. The hypothesis tested was that clinically obtainable correlates to measures used in the highly predictive laboratory-based models would demonstrate high accuracy in determination of high KAM status.

Section snippets

Subjects

Between 2004 and 2008, all sixth through twelfth grade female athlete participants in basketball and soccer were recruited from a county public school district with five middle schools and three high schools to participate in a prospective longitudinal study. The goal of the study was to determine potential underlying mechanisms that increase ACL injury risk. First time visits for 744 subjects' were designated for inclusion into the current analyses. Subjects were excluded (n = 46) from the study

Results

Mean and 95% CI for independent variables used in the model development are presented in Table 1, together with the correlation coefficients of the clinic-based surrogate predictor to its laboratory-based principal (Myer et al., in press-a) independent variable. The initial prediction of high KAM, was performed using logistic regression analysis in the training dataset (N = 598). The final logistic regression model, which included the independent predictors: knee valgus motion, knee flexion RoM,

Discussion

The purpose of the current study was to develop a “clinician friendly” landing assessment tool derived from the highly predictive laboratory-based measurements that would be easy to use and would facilitate the potential for widespread use in clinical and field settings. A nomogram was developed from the logistic regression analyses that can be used to predict high KAM (> 21.74 Nm KAM) based on clinically measured tibia length, knee valgus motion, knee flexion RoM, body mass and quadriceps to

Conclusion

ACL injury leads to significant short-term disability and currently there is no treatment that effectively prevents the long-term debilitation associated with osteoarthritis that follows this injury. Thus, prevention of ACL injuries is crucial. The current study addresses the increased potential to reduce ACL injury and potentially the long term osteoarthritis risk via identification of simple clinical measures that can be used to asses high KAM landing mechanics. Specifically, we have defined

Acknowledgements

The authors would like to acknowledge funding support from the National Institutes of Health/NIAMS Grants R01-AR049735, R01-AR05563 and R01-AR056259. The authors would like to thank Boone County Kentucky, School District, especially School Superintendent Randy Poe, for participation in this study. We would also like to thank Mike Blevins, Ed Massey, Dr. Brian Blavatt and the athletes of Boone County public school district for their participation in this study. The authors would also like to

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