Patient-Directed Computational Analysis of Atrial Fibrillation

房颤患者导向的计算分析 项目摘要 心房颤动（AF）是最常见的心律失常， 目前影响全球3000多万人的公共卫生问题 在美国有超过500万人。如果不治疗，AF会导致 中风、心力衰竭和死亡率增加。不幸的是， 维持AF仍然知之甚少，阻碍了当前 治疗策略然而，最近的研究表明，AF可能是 由旋转或焦点源驱动，并以这些源为目标， 局部消融可以产生有希望的长期结果。由于我们 然而，对房颤的了解不全面，这种靶向消融方法并不 总是成功的。 该项目将测试新的假设，即房颤是持续的局部 具有不同大小和时间稳定性的旋转和焦点源， 在这些源被移除之后，终止不是立即的，而是维持的 非本地机制。我们将使用组合的 计算/临床方法，采用先进的多尺度计算 技术和最先进的临床绘图。该项目将1）量化AF 使用患者特定的几何结构的组织; 2）确定是否一些 旋转源或焦点源比其他源更重要; 3）测试可能 使用患者专用数字计算机分析房颤维持和终止的原因 模型我们将使用来自我们独特的大型患者登记处的数据， 超过500名患者。 这个项目意义重大，因为它将建立一个更深入的了解AF 并可能揭示房颤维持的新机制。我们的研究结果可以 直接转化为实践，可以使发展更好 治疗方案。

Patient-Directed Computational Analysis of Atrial Fibrillation Project Summary Atrial fibrillation (AF) is the most common arrhythmia and is a rapidly-growing public health problem that currently affects over 30 million people world-wide and more than 5 million people in the US. If left untreated, AF leads to an increase in stroke, heart failure and mortality. Unfortunately, the mechanisms that maintain AF remain poorly understood, hindering further improvement of current therapy strategies. Recent studies, however, have revealed that AF may be driven by rotational or focal sources and that targeting these sources using localized ablation can result in promising long-term outcomes. Due to our incomplete understanding of AF, however, this targeted ablation approach is not always successful. This project will test the novel hypothesis that AF is sustained by localized rotational and focal sources with different size and temporal stability and that, after these sources are removed, termination is not immediate but is maintained by non-local mechanisms. We will address this hypothesis using a combined computational/clinical approach that employs advanced multiscale computational techniques and state-of-the-art clinical mapping. The project will 1) quantify AF organization using patient-specific geometries; 2) determine whether some rotational or focal sources are more important than others; 3) test possible causes of AF maintenance and termination using patient-specific digital computer models. We will use data from our unique and large patient registry, currently totaling >500 patients. This project is significant because it will establish a deeper understanding of AF and might reveal novel mechanisms of AF maintenance. Our results can be translated directly to practice and may enable the development of better treatment options.