Hypertension Prediction and Identification in All of Us

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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.