Research Projects Directory

Research Projects Directory

1,934 active projects

This information was updated 7/1/2022

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. Views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program. Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

2 projects have 'Cardiovascular Risk Prediction' in the project title
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Cardiovascular Risk Prediction in Diverse Populations

Our goal is to understand how clinical variables and genetics interact to confer the risk of cardiovascular diseases in a broad sense (coronary artery disease, arrhythmias, stroke, heart failure and bicuspid aortic valve/aortopathy, to name a few). We hope to…

Scientific Questions Being Studied

Our goal is to understand how clinical variables and genetics interact to confer the risk of cardiovascular diseases in a broad sense (coronary artery disease, arrhythmias, stroke, heart failure and bicuspid aortic valve/aortopathy, to name a few). We hope to discover and quantify synergistic (non-additive) relationships between clinical or genetic factors that work together to further increase the risk of cardiovascular disease morbidity and mortality.

Project Purpose(s)

  • Disease Focused Research (Cardiovascular Disease )
  • Methods Development
  • Ancestry

Scientific Approaches

We will leverage the immense resources and diverse nature of All of Us electronic health record and genetic data to accomplish our goals. We will deploy explainable artificial intelligence (AI) tools to explore and quantify the relationships between clinical and genetic variables for more precise outcomes analyses.

Anticipated Findings

We anticipate that some clinical and genetic variables interact in a synergistic (non-additive) manner to further increase the risk of poor outcomes in cardiovascular disease. Our hope is to create novel risk prediction tools that can be validated across other health care settings.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Cardiovascular Risk Prediction

Cardiovascular disease risk is an important determinant of how we treat patients. Preventive medications can reduce future risk, but they are associated with both financial and biological side-effects. Previous methods of predicting risk, notably the Pooled Cohort Equation (PCE), tend…

Scientific Questions Being Studied

Cardiovascular disease risk is an important determinant of how we treat patients. Preventive medications can reduce future risk, but they are associated with both financial and biological side-effects. Previous methods of predicting risk, notably the Pooled Cohort Equation (PCE), tend to perform worse in racial and ethnic minorities than in populations from European ancestry. As such, we plan to evaluate the accuracy of cardiovascular risk prediction in the diverse cohort of the All of Us study, and determine which if any variables might aid in refining risk in racial and ethnic minorities.

Project Purpose(s)

  • Disease Focused Research (arteriosclerotic cardiovascular disease)
  • Population Health

Scientific Approaches

Atherosclerotic cardiovascular disease (ASCVD) is defined by the American College of Cardiology and American Heart Association (ACC/AHA) includes stroke, transient ischemic attack (TIA), documented coronary artery disease (CAD) with stable angina, acute coronary syndromes (ACS), coronary or other arterial revascularization, peripheral vascular disease with or without claudication, and aortic aneurysm. In clinical practice, physicians will also include asymptomatic CAD (meaning, without angina) with demonstrable ischemia on a stress test.

We plan to utilize data from adults greater than 18 years of age in the All of Us Research study who are free of ASCVD, and have complete data for inputs in the Pooled Cohort Equation (age, gender, race, cholesterol levels, blood pressure (BP), BP medications, diabetes status, and smoking status).

We propose to then use the PCE to assess accuracy of risk prediction in the multi-ethnic All of Us cohort.

Anticipated Findings

The anticipated findings are that the PCE will not perform equally across all racial and ethnic categories. This will provide an opportunity for further refinement of risk-prediction algorithms using commonly collected clinical information.

An improved risk prediction algorithm would improve our ability to target the most needed preventive therapies to those at greatest risk of future events, and minimize harms from unnecessary therapies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Sarah Urbut - Research Fellow, The Broad Institute
  • Mark Trinder - Graduate Trainee, The Broad Institute
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