Hypertension Prediction and Identification in All of Us

项目摘要 此应用程序旨在使用All of Us Researcher中的现有数据来提高我们的能力， 确定高血压（HTN）患者出现明显难治性高血压（aTRH）的风险增加 和不良心血管结局。难治性高血压描述了一个子集的高血压个体 尽管使用了三种或更多种抗高血压药物，但血压（BP）不受控制，或BP 控制需要四种或更多种抗高血压药物。术语aTRH用于耐药 不能排除假性抵抗（如药物不依从、白色被膜效应）时的高血压。 在服用抗高血压药物的成人中，最近估计aTRH的患病率约为17- 19 药物治疗HTN是不良心血管结局（如急性心肌梗死）的主要风险因素， 梗塞中风和心力衰竭此外，与控制高血压患者相比， aTRH患者发生不良心血管结局、靶器官损伤和全因 mortality.虽然有许多一线抗高血压药物可以降低血压， 预防不良心血管结局，抗高血压药物的患者间变异性很大 反应目前尚不清楚为什么患者对同一种药物的反应不同，为什么有些患者 发展aTRH，以及为什么一些患者会出现不良心血管结局。我们的核心假设 HTN患者和亚群aTRH和不良心血管结局的风险增加， 可以通过临床因素、生化因素、基因组因素和 患者报告的数据。为了验证我们的核心假设，我们将完成以下具体目标：1） 按医疗机构描述HTN患者的aTRH和不良心血管结局，城市 与农村地区和地理区域相比，使用基于纵向电子健康记录（EHR）的数据， 2)通过医疗保健确定HTN患者中aTRH的早期体征和不良心血管结局 机构，城市与农村地区和地理区域，使用基于EHR的数据，基因组数据和数据 从调查和可穿戴设备。为了实现这些目标，我们将利用All of Us研究员的现有数据 - 是的我们所有人的研究计划正在美国招收各种各样的人， 包括多种类型的真实世界数据（例如，EHR、人口统计、可穿戴设备、患者调查、基因组）。我们 将部署我们经过验证的HTN算法，以确定观察到的HTN，aTRH和不良反应的发生率。 心血管结局。我们将确定aTRH和不良心血管结局的特征， HTN患者。我们还将使用多变量回归分析和机器学习模型来识别 aTRH和不良心血管结局的预测因子（基于EHR、基因组、患者报告）。我们将 研究HTN患者aTRH和不良心血管结局的特征和预测因素， 卫生保健机构、城市与农村地区以及地理区域。

PROJECT SUMMARY This application aims to use existing data within the All of Us Researcher Workbench to improve our ability to identify hypertension (HTN) patients at increased risk for apparent treatment resistant hypertension (aTRH) and adverse cardiovascular outcomes. Resistant hypertension describes a subset of hypertensive individuals with uncontrolled blood pressure (BP) despite the use of three or more antihypertensive medications, or BP control that requires four or more antihypertensive medications. The term aTRH is used for resistant hypertension when pseudoresistance (e.g. medication nonadherence, white coat effect) cannot be excluded. The prevalence of aTRH was recently estimated at ~17-19% among adults taking antihypertensive medications. HTN is a major risk factor for adverse cardiovascular outcomes such as acute myocardial infarction, stroke, and heart failure. Additionally, when compared to controlled hypertension patients, patients with aTRH are at increased risk for adverse cardiovascular outcomes, target organ damage, and all-cause mortality. Although there are numerous first-line antihypertensive drugs to lower blood pressure and ultimately prevent adverse cardiovascular outcomes, there is great inter-patient variability in antihypertensive drug response. It is poorly understood why patients respond differently to the same drug, why some patients develop aTRH, and why some patients experience adverse cardiovascular outcomes. Our central hypothesis is that HTN patients and subpopulations at increased risk for aTRH and adverse cardiovascular outcomes associated with HTN can be identified through clinical factors, biochemical factors, genomic factors, and patient reported data. To test our central hypothesis we will complete the following Specific Aims: 1) Characterize aTRH and adverse cardiovascular outcomes in HTN patients by health care institutions, urban versus rural areas, and geographic regions, using longitudinal electronic health record (EHR)-based data, and 2) Identify early signs of aTRH and adverse cardiovascular outcomes in HTN patients by health care institutions, urban versus rural areas, and geographic regions, using EHR-based data, genomic data, and data from surveys and wearables. To achieve these aims, we will utilize existing data from the All of Us Researcher Workbench. The All of Us Research Program is enrolling a diverse group of persons in the United States, and including multiple types of real-world data (e.g. EHR, demographic, wearables, patient surveys, genomic). We will deploy our validated HTN algorithms to determine observed rates of HTN, aTRH, and adverse cardiovascular outcomes. We will identify characteristics of aTRH and adverse cardiovascular outcomes in HTN patients. We will also use multivariable regression analyses and machine-learning models to identify predictors (EHR-based, genomic, patient reported) of aTRH and adverse cardiovascular outcomes. We will examine characteristics and predictors of aTRH and adverse cardiovascular outcomes in HTN patients by health care institutions, urban versus rural areas, and geographic region.