ECG-AI Based Prediction and Phenotyping of Heart Failure with Preserved Ejection Fraction

基于 ECG-AI 的射血分数保留的心力衰竭预测和表型分析

• 批准号: 10717312
• 负责人: Oguz Akbilgic
• 金额: $ 71.14万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10717312
• 关键词: Acute; Adult; Ancillary Study; Artificial Intelligence; Cardiology; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Chicago; Chronic; Classification; Clinical Data; Data; Data Set; Detection; Diagnosis; EFRAC; Early Diagnosis; Elderly; Electrocardiogram; Electronic Health Record; Enrollment; Epidemiology; Etiology; Funding; Goals; Health Sciences; Healthcare Systems; Heart failure; Individual; Intervention; Knowledge; Left Ventricular Ejection Fraction; Machine Learning; Methods; Modality; Modeling; Modification; Morbidity - disease rate; Multiomic Data; National Heart, Lung, and Blood Institute; Older Population; Participant; Patients; Phenotype; Preventive; Prognosis; Quality of life; Research; Risk; Risk Reduction; Site; Standardization; Syndrome; Tennessee; Testing; Training; Treatment Failure; United States; United States National Institutes of Health; Universities; Validation; Woman; Work; adjudication; artificial intelligence method; burden of illness; cohort; cost; deep learning; deep learning model; diagnostic criteria; electronic health data; forest; hands-on learning; improved; learning strategy; model building; mortality; multiple omics; novel; patient population; predictive modeling; preservation; repository; risk prediction; risk prediction model; screening; standard of care; targeted treatment; therapeutic development; tool; transfer learning; treatment optimization

Project Summary/Abstract More than 6 million adults are suffering from heart failure in the United States. Heart failure is associated with high mortality rate while also reducing the quality of life. Early recognition of heart failure and timely interventions can help reducing the disease burden to individuals and to overall healthcare system. However, more than half of HF patients are HF with preserved left ventricular ejection fraction (HFpEF) while the majority of existing HF treatments are for HF with reduced left ventricular ejection fraction (HFrEF). This is because HFpEF is a heterogenous syndrome, and its etiology is not well understood. A new NIH-funded initiative, HeartShare Study, aims to fill this knowledge gap to identify subtypes of HFpEF potentially with different treatment options using deep phenotyping, multi-omics, and machine learning approach. However, there is still a need for low cost and accessible tools 1) for screening large patient populations for HFpEF risk to support preventive risk modification strategies and 2) for identifying HFpEF subtypes to assist targeted therapeutics. The goal of this ancillary study is to utilize low cost and accessible electrocardiogram (ECG) data via artificial intelligence (AI) for prediction of incident HFpEF risk and subtyping of prevalent HFpEF. We and others have shown that AI applied to ECG data can discriminate patients with reduced and preserved EF with high accuracy [1-5]. We recently developed and validated an ECG-based 10-year HF risk prediction model using artificial intelligence (AI) [6, 7]. These findings led us to hypothesize that AI applied to ECG data can predict HFpEF risk and identify specific HFpEF subtypes. The goal of this ancillary study is to test our hypothesis by leveraging retrospective ECG and clinical data from: a) NIH-funded studies with gold standard ascertainment of HFpEFand b) real-world ECG and clinical data from three large healthcare systems (WFU- Wake Forest University, Winston-Salem, NC; UT-University of Tennessee Health Science Center, Memphis, TN; and LUC-Loyola University Chicago) and c) data from the HeartShare Study. Building on our expertise, we propose developing ECG-based risk prediction and classification of HFpEF subtypes by completing three Aims: Aim 1. Develop an incident HFpEF prediction model using data from NIH-funded studies: We will utilize high quality and accurate data from NIH-funded studies to develop AI model predicting risk for incident HFpEF. Aim 2. Develop an incident HFpEF prediction model using real-world Electronic Health Records (EHR)- derived data: We will first utilize very larger and diverse EHR-based real world data to develop incident HFpEF risk prediction model. We will then harmonize it with the NIH-data based model via transfer learning. Aim 3. Develop, test and implement ECG-based HFpEF phenotyping. This aim will utilize data from prevalent HFpEF patients to classify HFpEF subtypes.

项目概要/摘要 在美国有超过 600 万成年人患有心力衰竭。心力衰竭与 死亡率高的同时也降低了生活质量。及早发现心力衰竭并及时治疗 干预措施有助于减轻个人和整个医疗保健系统的疾病负担。然而， 超过一半的心力衰竭患者患有左心室射血分数保留 (HFpEF) 的心力衰竭，而大多数 现有的心力衰竭治疗主要针对左心室射血分数 (HFrEF) 降低的心力衰竭。这是因为 HFpEF 是一种异质性综合征，其病因尚不清楚。 NIH 资助的一项新举措， HeartShare 研究旨在填补这一知识空白，以识别 HFpEF 可能具有不同亚型的亚型 使用深度表型分析、多组学和机器学习方法的治疗选择。然而，仍然有 需要低成本且易于使用的工具 1) 用于筛查大量患者人群的 HFpEF 风险以支持 预防性风险调整策略；2) 识别 HFpEF 亚型以协助靶向治疗。 这项辅助研究的目标是通过人工方式利用低成本且易于获取的心电图 (ECG) 数据 用于预测 HFpEF 事件风险和流行 HFpEF 亚型的情报 (AI)。 我们和其他人已经证明，应用于心电图数据的人工智能可以区分减少和保留的患者 EF 具有高精度[1-5]。我们最近开发并验证了基于心电图的 10 年心力衰竭风险预测 使用人工智能 (AI) 的模型 [6, 7]。这些发现使我们假设人工智能应用于心电图数据 可以预测 HFpEF 风险并识别特定的 HFpEF 亚型。这项辅助研究的目的是测试我们的 通过利用回顾性心电图和临床数据来提出假设：a) NIH 资助的金标准研究 确定 HFpEF 和 b) 来自三个大型医疗保健系统 (WFU- 维克森林大学，北卡罗来纳州温斯顿塞勒姆；田纳西大学健康科学中心，孟菲斯， 田纳西州；和芝加哥洛约拉大学 (LUC-Loyola University Chicago)) 和 c) 来自 HeartShare 研究的数据。凭借我们的专业知识，我们 建议通过完成三个项目来开发基于心电图的 HFpEF 亚型风险预测和分类 目标： 目标 1. 使用 NIH 资助的研究数据开发事件 HFpEF 预测模型：我们将利用 来自 NIH 资助的研究的高质量和准确的数据，用于开发预测 HFpEF 事件风险的 AI 模型。 目标 2. 使用现实世界的电子健康记录 (EHR) 开发事件 HFpEF 预测模型 - 派生数据：我们将首先利用更大且多样化的基于 EHR 的现实世界数据来开发事件 HFpEF 风险预测模型。然后，我们将通过迁移学习将其与基于 NIH 数据的模型进行协调。 目标 3. 开发、测试和实施基于 ECG 的 HFpEF 表型分析。该目标将利用来自 流行的 HFpEF 患者对 HFpEF 亚型进行分类。