Anterior cruciate ligament (ACL) injury is a major medical and financial burden. Despite identification of modifiable risk factors and effective preventive measures, global ACL injury incidence remains largely unaffected. Under the parent grant we identified plausible valgus collapse mechanisms for ACL injury without concomitant medial collateral ligament (MCL) injury. A novel cadaveric testing setup we developed under the funded grant has demonstrated a nearly 90% rate of ACL tear. Our findings show that combined knee abduction moment (KAM), anterior tibial shear force (ATS) and internal tibial rotation moment (ITR) generates significantly greater strain in the ACL relative to the MCL, and reproduces kinematics similar to those observed during ACL injury. While these types of loading, in isolation, increase ACL strain and potentially risk of injury, their combined effects o ACL biomechanics are not well understood. In this competing renewal application we will develop a highly impactful and unique ACL injury risk assessment protocol that accounts for multiplanar biomechanics. The protocol will be developed through a novel, integrative in vivo, in vitro and in silico (in sim) approach.
The Specific Aims are: I) To develop and validate a multiplanar ACL injury risk assessment algorithm that predicts ACL injury risk based on dynamic ACL strain, and II) To integrate in vivo, in vitro and in silico approaches to establish a 'continuum of risk'that accounts for the relative contributions of KAM, ITR, and ATS to ACL rupture. The critical distinction between the two Aims is the biomechanical context:
Aim I will determine how the ACL is strained during non-injurious screening tasks that can be performed in a laboratory or clinical setting.
Aim II will establish a direct link between high strain movemet patterns and the ACL injury mechanism(s). We hypothesize that: I) Peak input values of KAM, ITR, and ATS from in vivo data will accurately predict peak ACL strain when landing biomechanics are reproduced in vitro and in silico, and II) Incremental increases in KAM, ITR and ATS, scaled from 'high-risk'in vivo measures will lead to ACL rupture in vitro and in silico.
In Specific Aim I, multi-planar kinematics and kinetics will be directly used as inputs to our validated, sex-specific, viscoelastic FE knee models and in vitro test protocols to test our hypotheses. We will also aim to identify and validate simple, clinically-based predictors for KAM, ITR, and ATS to maximize the clinical applicability of the protocol.
In Aim II, we will directly examine the roles of KAM, ITR and anterior tibial shear on the likelihood of ACL rupture. High-risk in vivo values for KAM, ATS, and ITR will be incrementally increased until tissue failure is achieved in cadavers, or ACL failure strains are reached in FE models. Furthermore, in Aim II we will optimize our FE modeling approach through validation of a methodology to customize models that accounts for variability in anatomy and tissue mechanics. This research will significantly improve the ability of researchers and clinicians to effectively screen athletes for ACL injury risk, and will increase ACL injury prevention program enrollment and efficacy.

Public Health Relevance

The proposed series of experiments will use live human subjects, cadaveric knees and computer biomechanical models to develop a state-of-the-art knee injury risk analysis tool. This tool will predict knee ligament strain during simulated athletc tasks, and will predict relative anterior cruciate ligament (ACL) injury risk in individuals. The synergy of these techniques will lead to the first scientific study to link high-risk biomechanics, especially in injury prone athletes, to the actual mechanism(s) of ACL injury.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Panagis, James S
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Ohio State University
Schools of Medicine
United States
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Bates, Nathaniel A; Schilaty, Nathan D; Nagelli, Christopher V et al. (2017) Novel mechanical impact simulator designed to generate clinically relevant anterior cruciate ligament ruptures. Clin Biomech (Bristol, Avon) 44:36-44
Schilaty, Nathan D; Nagelli, Christopher; Bates, Nathaniel A et al. (2017) Incidence of Second Anterior Cruciate Ligament Tears and Identification of Associated Risk Factors From 2001 to 2010 Using a Geographic Database. Orthop J Sports Med 5:2325967117724196
Hewett, Timothy E; Webster, Kate E; Hurd, Wendy J (2017) Systematic Selection of Key Logistic Regression Variables for Risk Prediction Analyses: A Five-Factor Maximum Model. Clin J Sport Med :
Sugimoto, Dai; Mattacola, Carl G; Bush, Heather M et al. (2017) Preventive Neuromuscular Training for Young Female Athletes: Comparison of Coach and Athlete Compliance Rates. J Athl Train 52:58-64
Schilaty, Nathan D; Bates, Nathaniel A; Sanders, Thomas L et al. (2017) Incidence of Second Anterior Cruciate Ligament Tears (1990-2000) and Associated Factors in a Specific Geographic Locale. Am J Sports Med 45:1567-1573
Bates, Nathaniel A; McPherson, April L; Nesbitt, Rebecca J et al. (2017) Robotic simulation of identical athletic-task kinematics on cadaveric limbs exhibits a lack of differences in knee mechanics between contralateral pairs. J Biomech 53:36-44
Nagelli, Christopher V; Hewett, Timothy E (2017) Should Return to Sport be Delayed Until 2 Years After Anterior Cruciate Ligament Reconstruction? Biological and Functional Considerations. Sports Med 47:221-232
Schilaty, Nathan D; Bates, Nathaniel A; Krych, Aaron J et al. (2017) How Anterior Cruciate Ligament Injury was averted during Knee Collapse in a NBA Point Guard. Ann Musculoskelet Med 1:008-12
Schilaty, Nathan D; Bates, Nathaniel A; Hewett, Timothy E (2017) Effect of sagittal plane mechanics on ACL strain during jump landing. J Orthop Res 35:1171-1172
Willigenburg, Nienke; Hewett, Timothy E (2017) Performance on the Functional Movement Screen Is Related to Hop Performance But Not to Hip and Knee Strength in Collegiate Football Players. Clin J Sport Med 27:119-126

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