Hypertension Prediction and Identification in All of Us

我们所有人的高血压预测和识别

基本信息

批准号：
10797850
负责人：
Caitrin W McDonough
金额：
$ 15.25万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-10 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10797850
关键词：
Acute myocardial infarction Adult Adverse event Algorithms All of Us Research Program Antihypertensive Agents Biochemical Biological Blood Pressure Cardiovascular system Characteristics Clinical Complex Data Data Reporting Data Set Databases Diagnosis Early Diagnosis Early identification Electronic Health Record Enrollment Exclusion Genomics Geographic Locations Goals Healthcare Heart failure Hypertension Individual Institution Knowledge Mentored Research Scientist Development Award National Heart, Lung, and Blood Institute Organ Outcome Patients Persons Pharmaceutical Preparations Phenotype Population Prevalence Prognosis Regression Analysis Reporting Research Research Personnel Resistant Hypertension Risk Risk Factors Selection for Treatments Signal Transduction Stroke Surveys Testing United States Work analytical method blood pressure control blood pressure elevation blood pressure reduction cardiovascular disorder risk cardiovascular risk factor computable phenotypes design genomic data high risk hypertensive improved interpatient variability machine learning model medication nonadherence mortality prevent response rural area sex wearable device

项目摘要

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.

项目概要该应用程序旨在使用“All of Us Researcher Workbench”中的现有数据来提高我们的能力识别明显难治性高血压 (aTRH) 风险增加的高血压 (HTN) 患者和不良心血管结局。顽固性高血压是指高血压患者的一个子集尽管使用了三种或更多抗高血压药物，但血压仍不受控制（BP）需要四种或更多抗高血压药物的控制。术语 aTRH 用于耐药性不能排除假性耐药（例如药物不依从性、白大衣效应）时的高血压。最近估计，在服用抗高血压药物的成年人中，aTRH 的患病率约为 17-19% 药物。高血压是急性心肌梗塞等不良心血管结局的主要危险因素梗塞、中风和心力衰竭。此外，与控制高血压的患者相比，患者患有 aTRH 的患者出现不良心血管结局、靶器官损伤和全因疾病的风险增加死亡。尽管有许多一线抗高血压药物可以降低血压，并最终预防不良心血管结局，抗高血压药物的患者间差异很大回复。人们知之甚少，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经历不良的心血管结局。我们的中心假设高血压患者和亚群 aTRH 和不良心血管结局的风险增加与高血压相关的因素可以通过临床因素、生化因素、基因组因素和患者报告的数据。为了检验我们的中心假设，我们将完成以下具体目标：1) 按医疗保健机构、城市描述 HTN 患者的 aTRH 和不良心血管结局特征使用基于纵向电子健康记录 (EHR) 的数据与农村地区和地理区域进行比较，以及 2) 通过医疗保健识别 HTN 患者的 aTRH 早期症状和不良心血管结局机构、城市与农村地区以及地理区域，使用基于 EHR 的数据、基因组数据和数据来自调查和可穿戴设备。为了实现这些目标，我们将利用 All of Us 研究人员的现有数据工作台。 “我们所有人研究计划”正在美国招募不同的人群，并且包括多种类型的现实世界数据（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 算法来确定观察到的 HTN、aTRH 和不良反应的发生率心血管结局。我们将确定 aTRH 的特征和不良心血管结局高血压患者。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 的、基因组的、患者报告的）。我们将通过以下方法检查 HTN 患者 aTRH 和不良心血管结局的特征和预测因素：医疗保健机构、城市与农村地区以及地理区域。