Machine Learning in Atrial Fibrillation

项目摘要 心房纤颤（AF）是最常见的心律失常，影响200万美国人， 可能导致心跳加速头晕或中风不幸的是，房颤的治疗 成功，可能是因为AF代表了介于 个体一个核心挑战是，目前尚不清楚为什么特定的治疗在给定的AF患者中有效。 这种不确定性使得开发针对患者的方法来定制治疗变得具有挑战性 个性化医疗 该项目的前提是，在房颤患者中， 范围涵盖组织、整个心脏和患者水平，但很少整合。我们开始使用机器 学习（ML），一种功能强大的方法，被证明可以对复杂数据集进行分类，整合数据以解决3 未满足的临床需求。首先，电描记图很少用于指导房颤治疗， 节奏，因为它们很难解释。二是难以理解心律失常是怎么回事 受房颤中任何特定消融策略的影响，不像有组织的节律。这使得难以 改善治疗。第三，很难确定单个患者是否会成功 房颤消融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3个具体目标：（1）使用经过培训的ML识别AF电描记图的组成部分， 单相动作电位（MAP）：（2）确定急性心肌梗死的电和结构特征， 房颤对肺静脉附近和远处消融的反应;（3）识别消融的患者 不成功或成功的长期，谁是目前分离不良。每个目标将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见解。 这项研究有可能产生直接的临床和翻译影响。我们将应用 具体的机器学习方法，生物统计学和计算机建模，我们丰富的多尺度注册表。我们将 开发实用和可共享的工具，我们将在临床上进行前瞻性测试， 组织、整个心脏和患者规模的结果。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。该项目可能会揭示新的 多尺度AF表型，以实现个性化治疗。

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.