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.

项目摘要/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增加的炎症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（OAC）在降低与AF相关的中风的风险和其他预防性方面非常有效减肥，运动和戒烟等干预措施可能会降低AF和相关的风险并发症。但是，AF通常是无症状的，并且经常是偶发性的，因此可能很困难尽管筛查可以检测到未诊断的AF，但批量筛查方法尚未导致临床结果的有意义改善。当前筛选方法中的主要效率低下是对AF风险相对较低的许多人的筛查，导致效率低下且低收益的筛查干涉。因此，识别患有AF的风险较高的人有至关重要的需求为了优化AF筛查和预防性干预措施的效率，前期。在本提案的目标1中，我们将开发和比较基于深度学习的新型方法，以自动化的方式估算AF风险使用移动单铅心电图。在AIM 2中，我们将进行单个级别的模拟来量化与当前相比，基于风险的AF筛查方法的比较和成本效益基于简单年龄截止的临床标准筛查标准为六年65岁。在AIM 3中，我们将执行飞行员量化潜在AF风险估计的用户可接受性的研究和量化关联估计在18个月的AF风险和真实AF事件。该提议的总体目标是建立自动AF风险估算的可行性和潜在的临床价值，以指导设计的预防干预措施降低由AF及其相关并发症产生的发病率。目的将在旨在为Khurshid博士提供的全面职业发展计划的设定，这是一个早期职业调查员，具有成为专注于的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来改善心律不齐的结果。这个建议一支多学科团队，完成机器学习，决策科学和潜在的专家临床研究，将指导Khurshid博士过渡到科学独立性。