Biomechanical study on aortic-mitral coupling in transcatheter aortic valve replacement

 描述(申请人提供)：严重的二尖瓣返流(MR)是严重主动脉狭窄(AS)患者的常见疾病，由于主动脉瓣和二尖瓣是相互依赖的，这些患者的治疗是具有挑战性的。经导管主动脉瓣置换术(TAVR)可以显著减轻高危患者的AS症状，但该手术移位了分隔两个瓣膜的主动脉-二尖瓣帷幕，从而可能对二尖瓣功能产生意想不到的不良影响。研究表明，在TAVR后，大约50%的患者MR严重程度得到改善，但其他患者没有变化，甚至恶化。本研究的目的是建立一个二尖瓣-主动脉-左心室(LV)耦合的计算模型框架，以研究合并MR患者TAVR所涉及的生物力学，以提高我们对二尖瓣-主动脉耦合的认识，并预测TAVR对MR严重程度的影响。为了实现这一目标，将开发二尖瓣-主动脉-左心室耦合计算模型来研究TAVR前的血流动力学，并与临床三维超声心动图(3DE)和心脏磁共振图(CMR)图像数据(目标1)进行验证。这些模型将被用来模拟TAVR装置的部署，以调查TAVR对二尖瓣和MR严重性的影响，并根据TAVR 3DE和CMR后的数据进行验证(AIM 2)。通过参数研究，将通过AIMS 1和AIMS 2(AIMS 3)经过验证的计算方法来研究改善患者预后的最佳TAVR设备和部署策略。本研究的结果可能为改进当前的临床决策过程提供科学的理论依据，包括患者和TAVR设备的选择，TAVR设备的定位和部署策略，以及MR改进的干预方法的选择。

 DESCRIPTION (provided by applicant): Significant mitral regurgitation (MR) is a common ailment among patients with severe aortic stenosis (AS), and the management of these patients is challenging because the aortic and mitral valves are interdependent. Transcatheter aortic valve replacement (TAVR) can significantly reduce AS symptoms in high-risk patients; however, this procedure displaces the aortic-mitral curtain delineating the two valves, and may consequently have unexpected and adverse effects on mitral valve function. Studies have shown that MR severity is improved in approximately 50% of patients following TAVR, but unchanged or even worsened in others. The objective of this study is to develop a coupled mitral-aortic-left ventricle (LV) computational modeling framework to investigate the biomechanics involved in TAVR in patients with concomitant MR, in order to improve our understanding of mitral-aortic coupling and predict the effects of TAVR on MR severity. To achieve this goal, mitral-aortic-LV coupling computational models will be developed to investigate pre-TAVR hemodynamics, and validated against clinical 3D echocardiogram (3DE) and Cardiac Magnectic Resonance (CMR) image data (Aim 1). The models will be used to simulate TAVR device deployment to investigate the impacts of TAVR on the mitral valve and MR severity, and validated against post-TAVR 3DE and CMR data (Aim 2). Through parametric study, the optimal TAVR device and deployment strategies to improve patient outcomes will be investigated through the validated computational methods of Aims 1 and 2 (Aim 3). The results from this study may provide scientific rationale to improve the current clinical decision-making process, including patient and TAVR device selection, TAVR device positioning and deployment strategy, and the selection of intervention methods for MR improvement.