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

项目摘要/摘要 射血分数保留的心力衰竭(HFpEF)的病理生理异质性很差。 理解并成为有效的HFpEF治疗的主要障碍，需要对HFpEF采取大胆的新方法 表型鉴定。长期目标是通过使更好的 其表型亚型的检测、理解和治疗。此应用程序的总体目标是 (I)开发机器学习算法，可顺序检测HFpEF，然后识别HFpEF表型 使用广泛可用的数据；然后(Ii)在一个大型的交叉大学中验证这种检测-表型方法 加州(UC)队列。中心假设是，机器学习可以通过算法从生理上提取- 从原始多通道数据到表型HFpEF的有价值的信息。其基本原理是算法能够可靠地 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到 HFpEF使用广泛可用的心电数据。神经网络算法的训练将使用 心电数据用于鉴别射血分数降低的心力衰竭和无心脏患者 失败了。对于第二个目标，将开发一种新的机器学习结构来提取最大值 同时来自多个诊断模式的信息。然后，该架构将用于培训 利用广泛可用的数据识别和表型HFpEF的算法：心电图、超声心动图(ECO)和 特定的电子健康记录(EHR)数据元素。一旦确定了可重复的HFpEF表型 我们的多模式神经网络表型分组方法，我们将表征生理上的差异 已鉴定的表型。第三个目标将构建一个跨UC心力衰竭/HFpEF队列，以进行外部验证 这些多模式HFpEF算法和识别的HFpEF表型。跨UC心力衰竭/HFpEF 队列将定期更新，并设计为支持未来的多中心研究。这项研究 在申请人看来，本申请中提出的是创新的，因为它开发了一种新的算法 一种同时从多个模态的广泛可用的数据中提取最大信息的方法，以 更接近地模仿医生如何三角测量信息来做出诊断。拟议的研究是 意义重大，因为将此算法方法应用于HFpEF预计将提供关键的表型 框架，通过该框架可以测试和管理当前和未来的HFpEF疗法，并将 还支持未来对潜在疾病机制的调查。最终，建立可重现的 HFpEF表型，以及用广泛可用的数据识别它们的能力，将极大地改变 HFpEF中的管理和研究范式，使表型导向疗法能够在 精准医学方法。

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.