Exploring pathophysiologic mechanisms to assess risk and optimize treatment outcomes in patients with mitral regurgitation

项目摘要 二尖瓣病变非常普遍，而且还在增加。尽管普遍存在，但梅杰无人接听 关于现有治疗策略的有效性和安全性以及我们是否有能力 在其自然病史的关键点为不同的患者亚组确定最佳治疗方案 疾病。随着时间的推移，心胸外科试验网络(CTSN)已经开发出一个独特的平台 瓣膜心脏病的研究，包括二尖瓣脱垂(MVP)患者的尖端试验 和缺血型MR 在这项申请中，该网络建议以两种方式加强这一研究议程。一种是添加 多模式成像和基因表达研究(使用组织标本)到我们最近推出的 外科二尖瓣修补术(MVR)与经导管边缘对边缘修补术(TEER)的初步比较 退行性先生的患者这些研究提供了一个机会，以获得更早的洞察 MVP使患者面临心律失常和猝死风险的病理生理过程，以及 找出针对这些异常的治疗方法。 第二个建议是确定二尖瓣疾病患者的表型，包括退行性和 缺血性，通过将机器学习和人工智能方法应用于临床和 在我们的二尖瓣试验中，数百名患者的影像数据被登记和跟踪。的分类 将患者分为不同的表型组将有助于识别可能接受MV干预的个体，网址为 他们的疾病自然病史的早期阶段比目前临床实践指南中建议的要早。 此外，我们将分析大型数据集，以评估MVR的年度利用率和结果的差异 在特定的社会人口群体中使用MVP和TEER。最近的证据表明，女性和种族 和种族不同的群体，可能会出现得更晚，结果会更糟。我们将提供急需的 MVP的诊断和治疗中存在差异的确凿证据 这些代表不足的亚群，这可能导致不同的结果。

Project Summary Disorders of the mitral valve are highly prevalent and increasing. Despite their prevalence, major unanswered questions remain regarding the effectiveness and safety of available treatment strategies and our ability to identify optimal treatment options for different patient sub-groups at critical points in the natural history of their disease. Over time, the Cardiothoracic Surgical Trials Network (CTSN) has developed a unique platform of research in valvular heart disease that includes cutting-edge trials in patients with mitral valve prolapse (MVP) and ischemic MR. In this application, the Network proposes to build on this research agenda in two ways. One is to add multimodality imaging and gene expression studies (using tissue specimens) to our recently launched PRIMARY trial comparing surgical mitral valve repair (MVr) to transcatheter-edge-to-edge repair (TEER) in patients with degenerative MR. These studies offer an opportunity to garner earlier insights into the pathophysiological process by which MVP puts patients at risk for arrhythmias and sudden death, and to identify treatments that target these abnormalities. The second proposal is to identify phenotypes among patients with mitral valve disease, both degenerative and ischemic, by applying machine learning and artificial intelligence methods to a repository of clinical and imaging data from hundreds of patients enrolled and followed in our mitral valve trials. Categorization of patients into groups of phenotypes will help identify individuals who may be candidates for MV intervention at earlier stages in the natural history of their disease than currently recommended in clinical practice guidelines. In addition, we will analyze large datasets to evaluate differences in annual utilization and outcomes of MVr and TEER in specific socio-demographic groups with MVP. Recent evidence suggests that women, and racial and ethnically diverse groups, may present later and are at worse outcomes. We will provide much-needed confirmatory evidence about the existence of variations in the diagnosis and management of MVP among these underrepresented subgroups, which may result in differential outcomes.