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.

项目摘要/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。早期意识到心力衰竭和及时 干预措施可以帮助减轻个人和整体医疗保健系统的负担。然而， 超过一半的HF患者是HF，保留了左心室射血分数（HFPEF），而大多数 现有的HF治疗是针对左心室射血分数（HFREF）减少的HF。这是因为 HFPEF是一种异质综合征，其病因尚未得到很好的理解。一项新的NIH资助计划， 心脏夏星研究，旨在填补这一知识差距，以识别HFPEF的亚型 使用深层表型，多词和机器学习方法的治疗选择。但是，仍然有 需要低成本和可访问工具1）筛查大量患者人群以获得HFPEF风险以支持 预防风险修改策略和2）识别HFPEF亚型以帮助有针对性的治疗剂。 这项辅助研究的目的是通过人工使用低成本和无障碍心电图（ECG）数据 智力（AI）预测事件HFPEF风险和普遍HFPEF的亚型。 我们和其他人表明，应用于ECG数据的AI可以区分减少和保存的患者 EF精度高[1-5]。我们最近开发并验证了基于ECG的10年HF风险预测 使用人工智能（AI）[6，7]模型。这些发现使我们假设AI应用于ECG数据 可以预测HFPEF风险并确定特定的HFPEF亚型。这项辅助研究的目的是测试我们 通过利用回顾性心电图和临床数据的假设：a）具有黄金标准的NIH资助的研究 hfpefand的确定b）来自三个大型医疗系统的现实世界心电图和临床数据（WFU- 北卡罗来纳州温斯顿·塞勒姆的韦克森林大学；孟菲斯田纳西州健康科学中心的UT-大学， TN;芝加哥卢克洛洛伊拉大学（Luc-Loyola University）和c）来自心脏研究的数据。在我们的专业知识的基础上，我们 通过完成三个 目标： 目标1。使用来自NIH资助的研究的数据开发事件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数据，以开发AI模型，以预测事件HFPEF的风险。 目标2。使用现实世界电子健康记录（EHR）开发事件HFPEF预测模型 - 派生数据：我们将首先利用非常大的基于EHR的现实世界数据来开发事件 HFPEF风险预测模型。然后，我们将通过转移学习将其与基于NIH-DATA的模型协调。 目标3。开发，测试和实施基于ECG的HFPEF表型。这个目标将利用来自 普遍的HFPEF患者对HFPEF亚型进行分类。