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

项目总结/摘要 在美国，超过600万成年人患有心力衰竭。心力衰竭与 高死亡率，同时也降低了生活质量。及早识别心力衰竭并及时 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，在这方面， 超过一半的HF患者是左心室射血分数（HFpEF）保留的HF，而大多数HF患者 现有的HF治疗中的大多数是针对具有降低的左心室射血分数（HFrEF）的HF。这是因为 HFpEF是一种异质性综合征，其病因尚不清楚。一项由NIH资助的新计划， HeartShare研究旨在填补这一知识空白，以识别可能具有不同 使用深度表型分析、多组学和机器学习方法的治疗选择。但仍有 需要低成本和可获得的工具1）筛查大量患者人群的HFpEF风险，以支持 预防性风险调整策略和2）用于鉴定HFpEF亚型以辅助靶向治疗。 本辅助研究的目标是通过人工心电图（ECG）数据， 用于预测HFpEF事件风险和流行HFpEF亚型的智能（AI）。 我们和其他人已经证明，应用于ECG数据的AI可以区分患者的减少和保留 精度高[1-5]。我们最近开发并验证了一种基于ECG的10年HF风险预测 使用人工智能（AI）模型[6，7]。这些发现使我们假设AI应用于ECG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 假设利用回顾性ECG和临床数据，来自：a）NIH资助的金标准研究 HFpEF的确定和B）来自三个大型医疗保健系统（WFU-2000）的真实世界ECG和临床数据。 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， 和LUC-Loyola大学芝加哥）和c）来自HeartShare研究的数据。基于我们的专业知识，我们 建议通过完成以下三项工作，开发基于ECG的HFpEF亚型风险预测和分类 目的： 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，以开发预测HFpEF事件风险的AI模型。 目标2.使用真实世界的电子健康记录（EHR）开发事件HFpEF预测模型- 衍生数据：我们将首先利用非常大的和多样化的基于EHR的真实的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数据的模型相协调。 目标3.开发、测试和实施基于ECG的HFpEF表型分析。这一目标将利用来自 流行的HFpEF患者进行HFpEF亚型分类。

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.