The Dynamics of Human Atrial Fibrillation

描述（由申请人提供）：房颤（AF）是最常见的心律失常，仅在美国就影响220万人，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术消除AF的方法仍不理想，这反映了我们目前对AF机制缺乏了解，以及它们在持续性或阵发性房颤患者中的差异。该项目测试了新的假设，即复极和传导的动态组织特性与结构异质性的相互作用为人类房颤的启动提供了直接机制。AF及其不同的临床模式。该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)确定动态组织特性（包括动作电位时程的恢复）是否导致房颤的发生; 2）确定房颤的发生是否伴随传导阻滞和折返; 3）为了确定在为每个患者专门创建的计算机模型中是否需要动态组织特性来引起AF，我们将通过在电生理研究中获取高分辨率的电生理和解剖数据，通过对两个心房的激活进行数值分析，然后通过开发患者特定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临床相关的。该项目意义重大，因为它研究了患者发生房颤的新机制。这一机制可以作为一种方法来预测房颤的倾向。了解这一机制也可以让一个更合理的方法，无论是药物开发和消融治疗。在电生理研究期间，该项目在患者中的表现也将使其结果直接转化为实践。最后，我们的患者特异性计算模型具有临床相关性，因此将为AF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.