Machine Learning in Atrial Fibrillation

心房颤动中的机器学习

基本信息

批准号：
10347364
负责人：
Sanjiv M Narayan
金额：
$ 74.91万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10347364
关键词：
Ablation Action Potentials Acute Address Affect Algorithms American Anatomy Area Arrhythmia Atrial Fibrillation Biological Biometry Biostatistical Methods Cessation of life Clinical Clinical Data Complex Computer Models Data Data Element Data Set Diagnosis Disease Dizziness Drug Controls Electrophysiology (science)Foundations Funding Genetic Heart Heart Atrium Heart failure Hospitalization Human Image Analysis Individual Label Lesion Link Machine Learning Magnetic Resonance Imaging Maps Measures Methods Noise Outcome Patient Care Patients Pattern Persons Phenotype Physics Physiological Play Public Health Recovery Registries Science Signal Transduction Site Stroke Test Result Testing Tissues Training Uncertainty United States National Institutes of Health Work base computer science convolutional neural network demographics digital experience heart rhythm improved individual patient individualized medicine insight machine learning classifier machine learning method mortality novel personalized medicine personalized strategies prospective prospective test response signal processing success tool translational impact voice recognition voltage

项目摘要

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.

项目概要心房颤动 (AF) 是最常见的心律失常，影响着 200 万美国人可能会导致心跳加快、头晕或中风的人。不幸的是，房颤的治疗方法有限成功，可能是因为 AF 代表了不同疾病之间的异质性和特征不明的疾病实体个人。一个主要的挑战是，目前尚不清楚为什么特定的治疗方法对特定的房颤患者有效。这种不确定性使得开发针对患者的特定方法来定制治疗方案具有挑战性。个性化医疗。该项目的前提是 AF 患者的机械数据越来越多尺度跨越组织、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) 是一种强大的方法，已被证明可以对复杂数据集进行分类、集成数据以解决 3 临床未满足的需求。首先，与有组织的治疗不同，电描记图很少用于指导 AF 治疗。节奏，因为它们很难解释。二、很难理解心律失常是怎么回事与有组织的节律不同，房颤会受到任何特定消融策略的影响。这使得很难改善治疗。第三，很难确定个别患者是否会成功来自 AF 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 AF 治疗策略。我们有 3 个具体目标：(1) 使用经过训练的 ML 来识别 AF 电图的组成部分单相动作电位（MAP）； (2) 识别急性的电学和结构特征 AF 对靠近和远离 PV 的消融的反应； (3) 确定适合接受消融治疗的患者长期不成功或成功，目前很难区分。每个目标都会将 ML 与传统的生物统计学，并使用机器学习的客观可解释性分析来提供机制见解。这项研究有可能产生直接的临床和转化影响。我们将申请我们丰富的多尺度注册表中包含特定的机器学习方法、生物统计学和计算机建模。我们将开发实用且可共享的工具，我们将前瞻性地进行临床测试，以提供有意义的成果组织、整个心脏和患者尺度的结果。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。这个项目很可能会揭示新颖的多尺度 AF 表型以实现个性化治疗。