Machine Learning for Ventricular Arrhythmias

项目摘要 室性心动过速(VT)和纤颤是导致心脏骤停、头晕、 晕厥和住院在美国和世界各地。然而，对患者的管理 尽管从基础科学到人口科学都有科学发现，但VT的风险仍然不是最理想的。在……里面 特别是，目前还没有框架来估计哪些室性心动过速患者可能对抗病毒药物有反应。 心律失常药物或消融术。因此，治疗是经验性的。使用分析是非常令人兴奋的 使用“大数据”来个性化VT治疗，但这并没有改善结果。 该项目开发了一种新的计算方法来个性化VT治疗，它结合了 使用计算模型的大型注册表中的机器学习。机器学习将应用于数据 跨越床边、实验室和非侵入性成像的生物尺度，以预测 患者很可能对治疗有反应。计算机模型将被用来估计给定患者的 心脏可能在治疗前和治疗后支持室速。我们将在大型外部环境中验证结果 来自不同机构的注册表。我们有三个具体目标：(1)开发一个计算管道来 使用床边、实验室和非侵入性成像来预测VT消融的反应；(2)使用机器 了解临床数据和无创成像以确定哪些室性心动过速患者将对抗 大型数据库中的心律失常药物；(3)结合计算方法来估计 给定患者对各种形式的治疗有反应的相对可能性。每个目标的结果都将 在独立的外部登记处进行测试。我们将探索计算模型以确定临床 可以在床边应用的表型。 该项目将为室性心动过速患者提供即时临床影响。我们将联合机器 在斯坦福大学和外部中心的大型注册表中使用基于物理的计算机模型进行学习。我们会 使用公平的方法(可查找、可访问、可互操作和可重用)减少计算偏差， 并根据2018年NIH数据科学战略计划免费提供工具。我们的团队包括 临床和基础电生理学、成像、机器学习、生物工程和统计学方面的专家。 这个项目是非常可行的。

Project Summary Ventricular tachycardia (VT) and fibrillation are leading causes of cardiac arrest, dizziness, syncope and hospitalization in the United States and worldwide. However, the management of patients at risk for VT remains suboptimal despite scientific discoveries from basic to population science. In particular, there is no framework to estimate which patients with VT are likely to respond to anti- arrhythmic medications or ablation. Therapy is thus empirical. There is great excitement to use analysis of “big data” to personalize VT therapy, but this has not yet improved outcomes. This project develops a novel computational approach to personalize VT therapy that combines machine learning in large registries with computational models. Machine learning will be applied to data across biological scales that span bedside, laboratory and non-invasive imaging, to predict which patients are likely to respond to therapy. Computer models will be used to estimate if a given patient's heart is likely to support VT before versus after therapy. We will validate results in large external registries from different Institutions. We have 3 specific aims: (1) To develop a computational pipeline to predict response to VT ablation using bedside, laboratory and non-invasive imaging; (2) To use machine learning of clinical data and non-invasive imaging to identify which patients with VT will respond to anti- arrhythmic medications in a large database; (3) To combine computational approaches to estimate the relative likelihood that a given patient will respond to various forms of therapy. Results from each Aim will be tested in independent external registries. We will probe computational models to identify clinical phenotypes that could be applied at the bedside. This project will provide immediate clinical impact for patients with VT. We will combine machine learning with physics-based computer models in large registries at Stanford and External centers. We will reduce computational bias using FAIR methods (Findable, Accessible, Interoperable, and Reusable), and make tools freely available per the 2018 NIH Strategic Plan for Data Science. Our team comprises experts in clinical and basic electrophysiology, imaging, machine learning, bioengineering and statistics. The project is very feasible.