Patient-Directed Computational Analysis of Atrial Fibrillation

心房颤动的患者导向计算分析 项目摘要 心房颤动(房颤)是最常见的心律失常，也是一种快速增长的 目前影响全球3000多万人的公共卫生问题 美国有500多万人。如果不治疗，房颤会导致 中风、心力衰竭和死亡率的增加。不幸的是，这些机制 维持房颤仍然知之甚少，阻碍了当前的进一步改善 治疗策略。然而，最近的研究表明，房颤可能是 由旋转源或焦点源驱动，并使用 局部消融可以带来有希望的长期结果。由于我们的 然而，对房颤的不完全理解，这种靶向消融方法并不是 总是很成功。 该项目将检验房颤持续存在的新假设。 具有不同大小和时间稳定性的旋转源和焦源， 在删除这些源之后，不会立即终止，但会保持终止 通过非本地机制。我们将使用组合的 采用先进的多尺度计算的计算/临床方法 技术和最先进的临床绘图。该项目将1)量化AF 使用特定于患者的几何形状的组织；2)确定是否有些 旋转或焦点震源比其他震源更重要；3)可能进行测试 使用患者专用数字计算机维持和终止房颤的原因 模特们。我们将使用来自我们唯一且庞大的患者注册表的数据，目前 总计500名患者。 这个项目意义重大，因为它将建立对AF的更深层次的了解 并可能揭示房颤维持的新机制。我们的结果可以是 直接转化为实践，可能会使更好的发展 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.