The Dynamics of Human Atrial Fibrillation

项目摘要 心房颤动（AF）是世界范围内的一种主要心律失常，可引起心悸、中风和死亡， 影响了200万到500万美国人不幸的是，消除AF的治疗成功有限。在 在上一个融资周期，我们专注于本地化驱动程序作为潜在的AF机制。驱动程序映射 现在已经通过人类AF的并发光学映射进行了验证，并且它们的特征已经被 在患者中通过其他几种方法验证。然而，这些和其他的消融结果 所提出的肺静脉外AF的机制是混合的。目前尚不清楚这是否反映了 AF标测困难或患者之间的机制不同。 该项目将为AF开发一种新的机制框架，通过以下方式简化现有指数： 基于科学共识，即组织性房颤更容易治疗，而紊乱性房颤更严重， 预后这一概念涵盖许多现有指数，可能有助于协调这些指数。We have 3 具体目标：（1）确定消融的影响是否取决于周围组织的程度。 消融部位;（2）建立有组织和无组织AF区的候选机制， 具有特定特征的个体患者，使用应用于已知病例的机器学习， 在我们的大型登记研究中，消融成功。这包括消融期间和迷宫后的详细AF标测图 手术、临床数据和结局。(3)使用新的临床工具来预测患者是否会 根据靶区域是否控制较大心房，对肺静脉隔离、其他消融或迷宫手术有反应 地区及其位置。 这项研究将产生直接的转化和临床影响，并直接使 房颤消融的个性化药物。我们在患者中使用详细的临床标测， 和计算机建模，以开发新型机制框架和广泛适用的临床工具。 我们将使用包括机器学习和统计在内的工具，根据结果对机制进行分类 在个别患者中进行消融。我们将在线提供我们的数据和代码。我们的团队是 在电生理学、计算机科学、机器学习、生物物理学和统计学方面经验丰富。 因此，这一建议是非常可行的。

Project Summary Atrial fibrillation (AF) is a major arrhythmia worldwide, causing palpitations, stroke and mortality, and affecting 2-5 million Americans. Unfortunately, therapy to eliminate AF has had limited success. In our last funding cycle, we focused on localized drivers as potential AF mechanisms. Mapping of drivers has now been validated by concurrent optical mapping of human AF, and their features and have been validated by several other methods in patients. Nevertheless, ablation results for these and other proposed mechanisms for AF outside the pulmonary veins are mixed. It is unclear if this reflects difficulties of AF mapping, or different mechanisms between patients. The project will develop a novel mechanistic framework for AF that simplifies existing indices by building on scientific consensus that organized AF is easier to treat, and disorganized AF has worse prognosis. This concept spans many existing indices and may help to reconcile them. We have 3 specific aims: (1) To define if the impact of ablation depends on the extent of organizing surrounding the ablation site; (2) To establish candidate mechanisms for organized and disorganized AF zones in individual patients with specific profiles, using machine learning applied to known cases with and without ablation success in our large registry. This comprises detailed AF maps during ablation and after Maze surgery, clinical data and outcomes. (3) To use novel clinical tools to predict whether patients will respond to PVI, other ablation or Maze surgery based on whether targeted regions control larger atrial areas and their locations. This study will deliver immediate translational and clinical impact, and directly enable personalized medicine for AF ablation. We use detailed clinical mapping in patients, signal processing and computer modeling to develop a novel mechanistic framework and widely applicable clinical tools. We will use tools including machine learning and statistics to classify mechanisms based upon outcomes from ablation in individual patients. We will make our data and code available online. Our team is experienced in electrophysiology, computer science, machine learning, biological physics and statistics. The proposal is thus highly feasible.