Multi-faceted Approach to Modeling ACL Injury Mechanisms

前十字韧带损伤是一种主要的医疗和经济负担。尽管确定了 可改变的危险因素和有效的预防措施，全球前交叉韧带损伤发生率仍然很高 不受影响。在父母的资助下，我们确定了可信的外翻崩溃机制，用于前交叉韧带损伤，而不是 合并内侧副韧带(MCL)损伤。一种新的身体测试装置，我们是在 Funded Grant显示出近90%的前交叉韧带撕裂率。我们的研究结果表明，联合膝盖 外展力矩(KAM)、胫骨前剪力(ATS)和胫骨内旋转力矩(ITR)产生 相对于MCL，前交叉韧带中的应变明显更大，并再现了与观察到的运动学相似的运动学 在前交叉韧带损伤期间。虽然这些类型的负荷在单独情况下会增加前交叉韧带应变和潜在的受伤风险， 它们对前交叉韧带生物力学的综合影响还不是很清楚。在这项竞争性续订申请中，我们 将开发一种高度有效和独特的前交叉韧带损伤风险评估方案，该方案将考虑多平面 生物力学。该方案将通过一种新的、一体化的体内、体外和硅胶(在sim中)来开发。 接近。具体目标是：i)开发和验证多平面前交叉韧带损伤风险评估 基于动态ACL应变预测ACL损伤风险的算法，以及ii)整合体内、体外和体内 建立“风险连续体”的电子方法，说明KAM、ITR和 ATS致前交叉韧带断裂。这两个目标之间的关键区别是生物力学背景：目标我将 确定在实验室可执行的非损伤性筛查任务中前交叉韧带如何受到张力 或临床环境。AIM II将在高应变运动模式和前交叉韧带损伤之间建立直接联系 机制(S)。我们假设：i)来自体内数据的KAM、ITR和ATS的峰值输入值将 准确预测落地生物力学在体外和硅胶中复制时的峰值前交叉韧带应变，以及ii) KAM、ITR和ATS的增量增加，从体内的高危措施扩展到将导致前交叉韧带断裂 在体外和硅胶中。在具体目标I中，多平面运动学和动力学将直接用作我们的 经过验证的、特定性别的、粘弹性的FE膝关节模型和体外测试方案来测试我们的假设。我们会 还旨在识别和验证KAM、ITR和ATS的简单、基于临床的预测指标，以最大限度地提高 该方案的临床适用性。在AIM II中，我们将直接研究KAM、ITR和胫骨前部的作用 关于前交叉韧带断裂的可能性的剪力。KAM、ATS和ITR的体内高风险值将递增 增加，直到身体达到组织衰竭，或在FE模型中达到前交叉韧带失效应变。 此外，在AIM II中，我们将通过验证以下方法来优化我们的有限元建模方法 定制模型，解释解剖学和组织力学中的可变性。这项研究将具有重要的意义 提高研究人员和临床医生有效筛查运动员前交叉韧带损伤风险的能力，并将 增加前交叉韧带损伤预防计划的注册人数和有效性。

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 on 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 movement 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.