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

 描述（由申请人提供）：严重二尖瓣返流（MR）是重度主动脉瓣狭窄（AS）患者的常见疾病，由于主动脉瓣和二尖瓣相互依赖，因此这些患者的管理具有挑战性。经导管主动脉瓣置换术（TAVR）可显著减少高危患者的AS症状;然而，该手术会移位描绘两个瓣膜的二尖瓣-二尖瓣屏障，因此可能对二尖瓣功能产生意外的不良影响。研究表明，TAVR后约50%的患者二尖瓣返流严重程度改善，但其他患者二尖瓣返流严重程度不变甚至恶化。本研究的目的是开发一个二尖瓣-主动脉-左心室（LV）耦合计算建模框架，以研究合并MR患者中TAVR涉及的生物力学，以提高我们对二尖瓣-主动脉耦合的理解并预测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-LV耦合计算模型，以研究TAVR前的血流动力学，并根据临床3D超声心动图（3DE）和心脏磁共振（CMR）图像数据进行确认（目标1）。这些模型将用于模拟TAVR器械展开，以研究TAVR对二尖瓣和二尖瓣返流严重度的影响，并根据TAVR后3DE和CMR数据进行确认（目标2）。通过参数研究，将通过目标1和2（目标3）的经确认计算方法研究改善患者结局的最佳TAVR器械和展开策略。本研究的结果可为改善当前临床决策过程提供科学依据，包括患者和TAVR器械选择、TAVR器械定位和展开策略以及MR改善的介入方法选择。

 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.