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Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation

基于心电图的深度学习和决策分析改善房颤风险评估

基本信息

批准号：
10722762
负责人：
Shaan Khurshid
金额：
$ 19.62万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
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：
2023-08-15 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10722762
关键词：
Advisory Committees Affect Age Alcohols Anticoagulation Arrhythmia Atrial Fibrillation Body Weight decreased Calibration Cardiac Cardiology Cardiomyopathies Circulation Clinical Clinical Data Clinical Research Clinical effectiveness Complex Conflict (Psychology)Custom Data Data Science Data Scientist Data Set Decision Analysis Decision Modeling Development Diagnosis Diagnostic tests Discrimination Disease Early Diagnosis Electrocardiogram Electrophysiology (science)European Exercise Fellowship Future General Hospitals Goals Guidelines Health Healthcare Heart failure Impaired cognition Incidence Individual Intervention Intervention Studies Lead Learning Learning Skill Machine Learning Manuscripts Mass Screening Massachusetts Mentored Patient-Oriented Research Career Development Award Mentors Methods Modeling Monitor Morbidity - disease rate Oral Outcome Patients Peer Review Persons Pilot Projects Positioning Attribute Preventive measure Primary Care Program Development Public Health Publishing Research Personnel Research Training Residencies Risk Risk Estimate Risk Factors Risk Reduction Sampling Science Stroke Stroke prevention Surveys Testing Training United States Work cardiovascular disorder risk career career development clinical risk comparative effectiveness cost effectiveness deep learning deep learning model design disorder risk experience handheld mobile device health care settings heart rhythm implementation science improved improved outcome interest machine learning model medical schools model building models and simulation multidisciplinary novel population based predictive tools preventive intervention primary care patient prospective risk prediction risk prediction model screening sex simulation skills therapy design tool trial comparing virtual

项目摘要

Project Summary/Abstract Atrial fibrillation (AF) is a major public health problem resulting in preventable strokes and increased incidence of heart failure and early cognitive decline. AF is expected to affect nearly 12 million people in the United States by 2030. Oral anticoagulation (OAC) is highly effective in reducing risk of AF-related stroke, and other preventive interventions such as weight loss, exercise, and alcohol cessation may reduce risk of AF and associated complications. However, AF is commonly asymptomatic and is frequently episodic, and therefore may be difficult to diagnose. Although screening can detect undiagnosed AF, mass screening approaches have not resulted in meaningful improvements in clinical outcomes. A major inefficiency inherent within current screening approaches is the screening of many individuals at relatively low risk for AF, leading to an inefficient and low-yield screening intervention. Therefore, there is a critical unmet need to identify individuals at elevated risk of developing AF upfront, in order to optimize the efficiency of AF screening and preventive interventions. In Aim 1 of this proposal, we will develop and compare novel deep learning-based methods to estimate AF risk in an automated fashion using mobile single-lead electrocardiograms. In Aim 2, we will conduct an individual-level simulation to quantify the comparative and cost-effectiveness of a risk-based approach to AF screening, as compared to the current clinical standard of AF screening based on the simple age cutoff of ³65 years. In Aim 3, we will perform a pilot study to quantify the user acceptability of prospective AF risk estimation and quantify associations between estimated AF risk and true AF incidence at 18 months. The overall goal of this proposal is to establish the feasibility and potential clinical value of automated AF risk estimation to guide preventive interventions designed to reduce the morbidity resulting from AF and its associated complications. The aims will be executed in the setting of a comprehensive career development program designed to provide Dr. Khurshid, an early career investigator, with the skills and experience required to become an independent clinician investigator focused on the improvement of outcomes in cardiac arrhythmias through the use of disease risk prediction. This proposal impanels a multi-disciplinary team comprising experts in machine learning, decision science, and prospective clinical studies, who will guide Dr. Khurshid in his transition to scientific independence.

项目总结/摘要心房颤动（AF）是一个主要的公共卫生问题，导致可预防的中风和发病率增加心力衰竭和早期认知能力下降。AF预计将影响美国近1200万人到2030年口服抗凝剂（OAC）在降低AF相关卒中风险方面非常有效，减肥、锻炼和戒酒等干预措施可能会降低房颤及相关疾病的风险。并发症然而，房颤通常无症状，且经常是发作性的，因此可能很困难。诊断。虽然筛查可以检测出未确诊的房颤，但大规模筛查方法并没有导致临床结果有意义的改善。当前筛选方法中固有的主要低效率对许多房颤风险相对较低的个体进行筛查，导致筛查效率低且产量低干预因此，识别房颤风险升高的个体是一个关键的未满足需求为了优化房颤筛查和预防干预的效率。在本提案的目标1中，我们将开发和比较新的基于深度学习的方法，以自动化的方式估计AF风险使用移动的单导联心电图。在目标2中，我们将进行个人层面的模拟，与目前的AF筛查方法相比，基于风险的AF筛查方法的比较和成本效益房颤筛查的临床标准基于简单的年龄截止值≥ 65岁。在目标3中，我们将执行一个试点研究旨在量化前瞻性AF风险估计的用户可接受性，并量化 18个月时的估计AF风险和真实AF发生率。本提案的总体目标是建立自动化AF风险估计的可行性和潜在临床价值，以指导设计的预防性干预措施降低房颤及其相关并发症的发病率。这些目标将在制定一个全面的职业发展计划，旨在为Khurshid博士提供早期的职业生涯，研究者，具有成为独立临床研究者所需的技能和经验，通过使用疾病风险预测改善心律失常的结局。这项建议组建了一个由机器学习、决策科学和前瞻性科学专家组成的多学科团队，他将指导Khurshid博士向科学独立过渡。