Machine Learning for Ventricular Arrhythmias

室性心律失常的机器学习

基本信息

批准号：
10658931
负责人：
Sanjiv M Narayan
金额：
$ 65.76万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658931
关键词：
Ablation Affect Age Anti-Arrhythmia Agents Area Under Curve Basic Science Big Data Biological Biomedical Engineering Caring Classification Clinical Clinical Data Clinical Sciences Clinical Trials Combined Modality Therapy Computer Models Computers Data Data Science Data Set Databases Diabetes Mellitus Diagnostic Diffusion Disease Dizziness Electrophysiology (science)FAIR principles Fiber Fibrosis Gadolinium Genetic Genomics Heart Heart Arrest Hospitalization Image Individual Institution Intelligence Interruption Label Laboratories Learning Lesion Life Machine Learning Magnetic Resonance Magnetic Resonance Imaging Methods Modeling Natural Language Processing Patients Pharmaceutical Preparations Physicians Physics Population Population Sciences Predisposition Recurrence Registries Risk Strategic Planning Syncope Testing Training Translational Research United States United States National Institutes of Health Ventricular Arrhythmia Ventricular Fibrillation Ventricular Tachycardia clinical imaging clinical phenotype clinical predictors comorbidity complex data computational pipelines data visualization diverse data heart rhythm improved improved outcome individual patient machine learning prediction mortality non-invasive imaging novel personalized medicine predicting response predictive modeling prevent response statistics structural determinants success tool

项目摘要

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.

项目概要室性心动过速 (VT) 和颤动是心脏骤停、头晕、然而，美国和世界各地的晕厥和住院治疗患者的管理。尽管从基础科学到人口科学都有科学发现，但患有 VT 的风险仍然不理想。特别是，没有框架来估计哪些 VT 患者可能对抗药物有反应因此，心律失常药物或消融治疗是经验性的，因此使用分析非常令人兴奋。的“大数据”来个性化VT治疗，但这尚未改善结果。该项目开发了一种新颖的计算方法来个性化 VT 治疗，该方法结合了具有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，以预测哪些患者可能对治疗有反应计算机模型将用于估计特定患者的反应。治疗前与治疗后心脏可能支持 VT 我们将验证大型外部结果。我们有 3 个具体目标：(1) 开发一个计算管道使用床边、实验室和非侵入性成像预测 VT 消融的反应； (2) 使用机器了解临床数据和非侵入性成像，以确定哪些 VT 患者会对抗药物有反应大型数据库中的心律失常药物；（3）结合计算方法来估计特定患者对各种形式的治疗产生反应的相对可能性每个目标的结果。我们将在独立的外部注册中心进行测试，以识别临床。可以应用于床边的表型。该项目将为 VT 患者提供直接的临床影响，我们将结合机器。我们将在斯坦福大学和外部中心的大型注册中心使用基于物理的计算机模型进行学习。使用公平方法（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、生物工程和统计学专家。该项目非常可行。