The Dynamics of Human Atrial Fibrillation

项目摘要 房颤是世界范围内的一种主要心律失常，可导致心悸、中风和死亡。 并影响到200-500万美国人。不幸的是，消除房颤的治疗效果有限。在……里面 在我们的上一个融资周期中，我们专注于将本地化的驱动因素作为潜在的房颤机制。驱动因素的映射 现已通过人体房颤的并行光学标测得到验证，其特征和已被 在患者身上得到了其他几种方法的验证。然而，这些和其他的消融结果 肺静脉外房颤的发病机制各有不同。目前还不清楚这是否反映了 房颤标测困难，或患者机制不同。 该项目将为AF开发一个新的机械框架，通过以下方式简化现有的索引 基于科学共识，有组织的房颤更容易治疗，而无组织的房颤更糟糕 预后。这一概念涵盖了许多现有的指数，可能有助于协调这些指数。我们有3个 具体目标：(1)确定消融的影响是否取决于周围组织的程度 消融地点；(2)建立有组织和无组织房颤区域的候选机制 具有特定特征的单个患者，使用机器学习应用于已知的有无病例 在我们的大型注册表中成功消融。这包括消融期间和迷宫后的详细AF图 手术、临床数据和结果。(3)使用新的临床工具来预测患者是否会 根据靶区是否控制较大的心房，对PVI、其他消融或迷宫手术做出反应 区域及其位置。 这项研究将立即带来翻译和临床影响，并直接支持 房颤消融的个性化药物。我们使用患者详细的临床标测，信号处理 和计算机建模，以开发一种新的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来根据结果对机制进行分类 来自个别病人的消融。我们将在网上提供我们的数据和代码。我们的团队是 具有电生理学、计算机科学、机器学习、生物物理学和统计学方面的经验。 因此，这项建议是非常可行的。

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.