Machine Learning in Atrial Fibrillation

项目摘要 房颤(房颤)是最常见的心律失常，在美国有200万人受到影响。 它可能会导致心跳加速、头晕或中风。不幸的是，房颤的治疗有限 成功，可能是因为房颤代表了不同的、特征不佳的疾病实体 个人。一个主要的挑战是，目前还不清楚为什么特定的治疗方法对给定的房颤患者有效。 这种不确定性使得开发一种针对患者的量身定制治疗方法具有挑战性 个性化医疗。 这个项目的前提是房颤患者的机械性数据越来越多， 鳞片横跨组织、整个心脏和患者水平，但很少整合。我们开始用机器 学习(ML)，一种被证明可以对复杂数据集进行分类、将数据集成到地址3的强大方法 临床上未得到满足的需求。首先，与有组织的治疗不同，肌电图很少用于指导房颤的治疗 节奏，因为它们很难解释。其次，很难理解心律失常是如何发生的。 受房颤中任何特定消融策略的影响，不同于有组织的节律。这使得很难 提高治疗水平。第三，很难确定单个患者是否会成功。 房颤消融造成的。我们将机器学习和新颖的客观分析应用于这些问题 制定个性化房颤治疗策略。 我们有三个具体的目标：(1)使用ML训练来识别房颤心电的成分 单相动作电位(MAP)；(2)识别急性心肌梗死患者的电和结构特征 房颤消融前后的反应；(3)识别消融患者 不成功或成功的长期，他们目前分离得很差。每个目标都会将ML与 传统的生物统计学，并使用ML的客观可解释性分析来提供机械性的见解。 这项研究有可能产生立竿见影的临床和翻译影响。我们会申请 我们丰富的多尺度注册表的特定ML方法、生物统计学和计算机建模。我们会 开发实用和可共享的工具，我们将进行前瞻性的临床测试，以提供有意义的 在组织、整个心脏和患者尺度上的结果。我们的团队在电生理学方面经验丰富， 计算机科学、信号处理和生物物理学。这个项目可能会揭示出一种新的 多尺度房颤表型，支持个性化治疗。

Project Summary Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting 2 million Americans in whom it may cause skipped heart beats, dizziness or stroke. Unfortunately, therapy for AF has limited success, likely because AF represents heterogenous and poorly characterized disease entities between individuals. A central challenge is that it is not clear why a specific therapy works in a given AF patient. This uncertainty makes it challenging to develop a patient-specific approach to tailor therapy for personalized medicine. The premise of this project is that mechanistic data is increasingly available in AF patients at scales spanning tissue, whole heart and patient levels, yet rarely integrated. We set out to use machine learning (ML), a powerful approach proven to classify complex datasets, to integrate data to address 3 clinical unmet needs. First, electrograms are rarely used to guide therapy in AF, unlike organized rhythms, because they are difficult to interpret. Second, it is difficult to understand how arrhythmia is affected by any specific ablation strategy in AF, unlike organized rhythms. This makes it difficult to improve therapy. Third, it is difficult to identify whether an individual patient will or will not have success from AF ablation. We applied machine learning and novel objective analyses to these questions to develop strategies for personalized AF therapy. We have 3 specific aims: (1) To identify components of AF electrograms using ML trained to monophasic action potentials (MAP); (2) To identify electrical and structural features of the acute response of AF to ablation near and remote from PVs; (3) To identify patients in whom ablation is unsuccessful or successful long-term, who are poorly separated at present. Each Aim will compare ML to traditional biostatistics, and use objective explainability analysis of ML to provide mechanistic insights. This study has potential to deliver immediate clinical and translational impact. We will apply specific ML approaches, biostatistics, and computer modeling to our rich multiscale registry. We will develop practical and shareable tools, which we will prospectively test clinically, to deliver meaningful outcomes at tissue, whole heart and patient scales. Our team is experienced in electrophysiology, computer science, signal processing and biological physics. This project is likely to reveal novel multiscale AF phenotypes to enable personalized therapy.