The Dynamics of Human Atrial Fibrillation

描述(申请人提供)：房颤(房颤)是最常见的心律失常，仅在美国就有220万人受到影响，是发病率和死亡率的主要原因。目前使用抗心律失常药物和消融来消除房颤的方法仍然不是最理想的，这反映了我们目前对房颤的机制缺乏了解，以及对于有持续性或阵发性房颤的患者，这些方法可能有何不同。本项目验证了这一新的假设，即复极和传导的动态组织属性与结构异质性的相互作用为人类房颤的启动及其不同的临床模式提供了直接机制。这个项目建立在已发表的工作和我们实验室对患者的初步观察基础上。我们有三个具体目标。1)确定包括动作电位时程恢复在内的动态组织属性是否导致心房颤动的启动；2)确定在传导阻滞和折返之后是否发生房颤的启动；3)在为每个患者专门创建的计算机模型中确定是否需要动态组织属性来导致房颤，然后参照观察到的房颤。我们将通过在电生理学研究中获得高分辨率的电生理学和解剖学数据，通过对两个心房的激活进行数值分析，然后通过开发针对患者的计算机模型来实现这些目标。我们将在这个项目中创建的计算机模型将是最详细和与临床相关的模型之一。这个项目具有重要意义，因为它研究了一种新的房颤发生机制。这一机制可作为预测房颤倾向的一种方法。了解这一机制也可能为药物开发和消融治疗提供一种更合理的方法。该项目在电生理学研究期间在患者身上的表现也将使其结果直接转化为实践。最后，我们的特定于患者的计算模型具有临床相关性，因此将为房颤的进一步假设检验提供资源。

DESCRIPTION (provided by applicant): Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting 2.2 million individuals in the United States alone, and is a major cause of morbidity and mortality. Current methods to eliminate AF with anti-arrhythmic drugs and ablation remain suboptimal, reflecting our current lack of understanding of the mechanisms for AF, and how they may differ for patients with presentations such as persistent or paroxysmal AF. This project tests the novel hypothesis that interaction of the dynamic tissue properties of repolarization and conduction with structural heterogeneities provides a direct mechanism for the initiation of human AF and its varying clinical patterns. This project builds upon published work and preliminary observations by our laboratory in patients. We have three specific Aims. 1) To determine whether dynamic tissue properties, including restitution of action potential duration, cause the initiation of atrial fibrillation; 2) To determine whether the initiation of atrial fibrillation follows conduction block and reentry; 3) To determine whether dynamic tissue properties are required to cause AF in computer models created specifically for each patient, then referenced back to observed AF. We will pursue these aims by acquiring high-resolution electrophysiologic and anatomic data at electrophysiologic study, by performing numerical analysis of activation in both atria, then by developing patient-specific computer models. The computer models that we will create in this project will be among the most detailed and clinically-relevant. This project is significant because it studies a novel mechanism for the development of atrial fibrillation in patients. This mechanism may serve as a method to predict the propensity for AF. Understanding this mechanism may also allow a more rational approach both to drug development and ablation therapy. The performance of this project in patients during electrophysiologic study will also allow its results to be translated directly to practice. Finally, our patient-specific computational models are clinically relevant, and will thus provide a resource for further hypothesis testing in AF.