Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms

使用原始、广泛可用的多模态数据和人工智能算法，开发一个对射血分数保留的心力衰竭进行深度表型分析的平台

• 批准号: 10683803
• 负责人: Geoffrey H Tison
• 金额: $ 72.08万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683803
• 关键词: Affect; Algorithms; Architecture; California; Categories; Clinical; Data; Data Element; Descriptor; Detection; Development; Diagnosis; Diagnostic; Disease; EFRAC; Echocardiography; Economics; Electrocardiogram; Electronic Health Record; Failure; Future; Goals; Grouping; Heart failure; Heterogeneity; Hospitalization; Human; Hypertrophic Cardiomyopathy; Intervention Trial; Investigation; Machine Learning; Methods; Mission; Modality; Morbidity - disease rate; Multicenter Studies; National Heart, Lung, and Blood Institute; Outcome; Patients; Persons; Phenotype; Physicians; Physiological; Physiology; Public Health; Reproducibility; Research; Resources; Subgroup; Supervision; Testing; Training; United States National Institutes of Health; Universities; Update; algorithm training; artificial intelligence algorithm; base; cardiac amyloidosis; cohort; data warehouse; design; effective therapy; health data; improved; innovation; intracardiac pressure; machine learning algorithm; mortality; multimodal data; multimodality; neural network; neural network algorithm; neural network architecture; novel; novel strategies; phenotyping algorithm; precision medicine; preservation; prospective; screening; targeted treatment; voltage; working group

PROJECT SUMMARY / ABSTRACT The pathophysiologic heterogeneity underlying heart failure with preserved ejection fraction (HFpEF) is poorly- understood and is a major barrier to effective HFpEF treatments, necessitating bold new approaches to HFpEF phenotyping. The long-term goal is to reduce the substantial morbidity and mortality of HFpEF by enabling better detection, understanding and treatment of its phenotypic subtypes. The overall objectives of this application are to (i) develop machine learning algorithms that can sequentially detect HFpEF then identify HFpEF phenotypes using widely-available data; then (ii) validate this detection-phenotyping approach in a large cross-University of California (UC) cohort. The central hypothesis is that machine learning can algorithmically extract physiologically- valuable information from raw multi-modality data to phenotype HFpEF. The rationale is that algorithms to reliably phenotype HFpEF will provide both the framework to investigate phenotype-specific mechanisms and therapies, and the method by which to identify target patients. The first aim will develop algorithms that can reliably detect HFpEF using widely-available electrocardiogram (ECG) data. Neural network algorithms will be trained using ECG data to discriminate HFpEF from heart failure with reduced ejection fraction and patients without heart failure. For the second aim, a novel machine learning architecture will be developed to extract maximal information from multiple diagnostic modalities simultaneously. This architecture will then be used to train algorithms to identify and phenotype HFpEF with widely-available data: ECGs, echocardiograms (echo) and specific electronic health record (EHR) data elements. Once reproducible HFpEF phenotypes are identified using our multi-modal neural network phenogrouping approach, we will characterize physiologic differences between identified phenotypes. The third aim will construct a cross-UC heart failure/HFpEF cohort to externally validate these multi-modal HFpEF algorithms and the identified HFpEF phenotypes. The cross-UC heart failure/HFpEF cohort will be updated regularly and designed to support future prospective multi-center studies. The research proposed in this application is innovative, in the applicant’s opinion, because it develops a novel algorithmic approach to extract maximal information from widely-available data in multiple modalities simultaneously, to more closely mimic how physicians triangulate information to make diagnoses. The proposed research is significant because applying this algorithmic approach to HFpEF is expected to provide a critical phenotypic framework, through which current and future HFpEF therapies can be tested and administered, and which will also support future investigations into underlying disease mechanisms. Ultimately, establishment of reproducible HFpEF phenotypes, and the ability to identify them with widely-available data, would dramatically shift the management and research paradigms in HFpEF, enabling the targeting of phenotype-guided therapies in a precision medicine approach.

项目总结/摘要 射血分数保留性心力衰竭（HFpEF）的病理生理异质性较差- 这是有效HFpEF治疗的主要障碍，需要大胆的HFpEF新方法 表型分析长期目标是通过更好地治疗HFpEF， 其表型亚型的检测、理解和治疗。本申请的总体目标是 （i）开发机器学习算法，可以顺序检测HFpEF，然后识别HFpEF表型 使用广泛可用的数据;然后（ii）在一个大型的跨大学研究中验证这种检测表型方法。 加州（UC）队列。核心假设是机器学习可以通过算法提取生理上的- 从原始多模态数据到表型HFpEF的有价值信息。基本原理是，算法可以可靠地 表型HFpEF将提供研究表型特异性机制和治疗的框架， 以及识别目标患者的方法。第一个目标是开发能够可靠地检测 使用广泛可用的心电图（ECG）数据的HFpEF。神经网络算法将使用 ECG数据用于区分HFpEF与射血分数降低的心力衰竭和无心脏病的患者 失败对于第二个目标，将开发一种新的机器学习架构来提取最大的 同时从多种诊断模式中获取信息。这个架构将用于训练 使用广泛可用的数据识别和表型HFpEF的算法：ECG、超声心动图（echo）和 电子健康记录（EHR）数据元素。一旦可重现的HFpEF表型被鉴定， 我们的多模态神经网络表型分组方法，我们将表征生理差异之间 鉴定的表型。第三个目标是构建一个交叉UC心力衰竭/HFpEF队列，以进行外部验证 这些多模态HFpEF算法和鉴定的HFpEF表型。交叉UC心力衰竭/HFpEF 队列将定期更新，旨在支持未来的前瞻性多中心研究。研究 在申请人看来，本申请中提出的方法是创新的，因为它开发了一种新颖的算法， 一种同时从多种模态的广泛可用数据中提取最大信息的方法， 更接近地模仿医生如何三角信息作出诊断。拟议的研究是 这是重要的，因为将这种算法方法应用于HFpEF预期将提供关键的表型 框架，通过该框架可以测试和管理当前和未来的HFpEF疗法，并且该框架将 也支持未来对潜在疾病机制的研究。最终，建立可重复的 HFpEF表型，以及用广泛可用的数据鉴定它们的能力，将极大地改变人类的免疫功能。 HFpEF的管理和研究范式，使表型指导治疗的靶向， 精准医疗方法