Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction

心力衰竭伴保留射血分数(HFpEF)是一个主要的公共卫生问题 随着人口老龄化以及肥胖、糖尿病和糖尿病的流行，这种疾病的患病率正在上升。 高血压。HFpEF约占所有心力衰竭(HF)患者的50% 在美国，至少300万人的HFpEF与高发病率和死亡率有关。在HF之后 住院期间，HFpEF的5年生存率仅为令人沮丧的35%，比大多数癌症都要差。 此外，HFpEF的生活质量与HF一样差，甚至更差，射血分数降低。 (HFrEF)。已经进行了一系列大规模的临床试验，但大多数只提供了 结果为中性，未能证明治疗的有效性。HFpEF的惊人走势与 患者缺乏有效的治疗方法构成了一个重大的公共卫生问题。 最近的研究将这种失败归因于HFpEF综合征独特的全身性 并提出了异质性HFpEF综合征的亚型，这突出了 越来越需要针对特定的HFpEF亚型进行更好的靶向治疗。表面上看起来 不同但相互关联的表型，以及共病、生活方式和 环境因素，使多器官综合征最大限度地受益于大数据方法。 然而，常规研究通常只包括有限的横断面临床症状， 心脏成像的实验室结果和/或肉眼测量以研究HFpEF，俯瞰 电子病历(EHR)中丰富的时间信息和详细的空间信息 在成像中保留。 在本方案中，我们将引入先进的形状分析方法来提取新颖的图像 来自CMR图像的特征和生物标记物，并在总体水平上进行验证(目标1)。到时候我们会的 将图像信息与多维时态EHR数据相结合，共同识别临床 使用最先进的机器学习技术的重要HFpEF子类(即表型) (目标2)。为了达到基于表型的治疗目标，我们将进一步研究最佳 基于深度强化学习(RL)的现有代理治疗策略 在正在进行的试验提供足够证据之前，提供大量的电子健康记录数据以满足迫切需要 关于已证实临床疗效的新药(目标3)。此外，我们还将开发一个在线的、开放的- 获取平台，以促进本研究的代码、数据和知识的共享(目标4)。我们 相信这项研究可以提高我们对HFpEF的理解、表型和管理， 这可能会积极地减轻美国和世界各地的临床和经济负担。

Heart failure with preserved ejection fraction (HFpEF) is a major public health problem that is rising in prevalence with the aging population and the epidemics of obesity, diabetes, and hypertension. HFpEF accounts for around 50% of all heart failure (HF) cases with a prevalence of at least 3 million in the U.S. HFpEF is associated with high morbidity and mortality. After HF hospitalization, the 5-year survival of HFpEF is a dismal 35%, which is worse than most cancers. In addition, quality of life in HFpEF is as poor or worse than HF with reduced ejection fraction (HFrEF). A series of large-scale clinical trials has been conducted, but most of them only provided neutral result and failed to prove the efficacy of treatments. The alarming trend of HFpEF with lack of effective therapies for patients constitutes a major public health problem. Recent studies have attributed this failure to distinct systemic nature of HFpEF syndrome and proposing sub-phenotypes within the heterogeneous HFpEF syndrome, which highlighted the increasing need for better-targeted therapies to specific HFpEF subtypes. The seemingly disparate but complex interrelated phenotypes, along with comorbidities, lifestyle and environmental factors, make the multi-organ syndrome best beneficial from a big data approach. However, conventional studies usually only included limited cross-sectional clinical symptoms, lab results and/or gross measurements on cardiac imaging to investigate HFpEF, overlooking the rich temporal information from electronic health record (EHR) and detailed spatial information reserved in imaging. In this proposal, we will introduce advance shape analysis method to extract novel image features and biomarker from CMR images and validate at population level (Aim 1). We will then combine image information with multi-dimensional temporal EHR data to jointly identify clinically significant HFpEF subclasses (i.e. phenotyping) using state-of-art machine learning technique (Aim 2). Towards therapeutic goals based on phenotyping, we will further investigate optimal treatment strategies with current available agents using deep reinforcement learning (RL) based on massive EHR data to meet the pressing need before ongoing trials provide sufficient evidence on new drugs with proved clinical efficacy (Aim 3). Furthermore, we will develop an online, open- access platform to facilitating the sharing of code, data and knowledge of this study (Aim 4). We believe this research can improve our understanding, phenotyping and management of HFpEF, which might positively ease the clinical and economic burdens in turn both in U.S. and worldwide.