# Improving the Accuracy of ASCVD Risk Estimation Using Population-Level EHR and Genetic Data

> **NIH NIH F30** · BROWN UNIVERSITY · 2021 · $53,536

## Abstract

SUMMARY
Cardiovascular diseases (CVD) are the leading causes of morbidity and mortality in the United States.
Atherosclerotic cardiovascular disease (ASVD) is the primary mechanism for the development of CVD and is
largely considered preventable by the Center for Disease Control and Prevention. Lipid-lowering therapy is the
current mainstay of preventative treatment for ASCVD and guidelines for pharmacotherapy rely on the 2013
Pooled Cohort Equations (PCE) for estimating 10-year risk. While these equations have been validated at a
population level they have significant shortcomings that impact real-world patient-level effectiveness. These
include implementation (i.e. time and effort for clinicians to enter patient data into a phone or web-based
calculator), therapy changing sensitivity to highly variable inputs (e.g single time point blood pressure),
paradoxical risk estimation for some patient subgroups that are an artifact of linear modeling (e.g. women
smokers), blunt treatment of race (i.e. separately derived equations for black patients), and poor calibration for
modern cohorts (i.e. resulting in the overestimation of risk). This project will attempt to address these
shortcomings. First, portable tools for analyzing electronic health records found within the Rhode Island Health
Information Exchange (HIE) will be developed for the extraction of PCE risk factors to enable the automated
calculation of ASCVD risk. PCE risk factor extraction permutations (e.g. last vs mean blood pressure) will be
optimized and the equations will be calibrated for the population. Next, EHR-system agnostic tools for
extracting additional risk factors available within the medical record including symptom development, social
determinants of health, and family history will be developed. PCE and non-PCE risk factors will be used for
artificial neural network and dynamic Bayesian network modeling of ASCVD risk phenotype clusters to
augment PCE risk prediction. Finally, a single nucleotide polymorphism (SNP) genotype data derived ASCVD
genetic risk score will be integrated with the HIE derived risk factors to demonstrate the potential clinical
implications of implementing an omics-integrated learning healthcare system. The project will serve as
foundational training for the principal investigator towards pursuing a career as a physician-scientist in the field
of biomedical informatics.
Hypothesis: Atherosclerotic cardiovascular disease risk estimation is central to current lipid-lowering therapy
guidelines. This project will test the hypothesis that population-level data-driven methods will improve the
accuracy of risk calculators.
Aim 1: Determine the Predictive Performance of PCE Risk Factors Derived from Longitudinal HIE Data
Aim 2: Define Population-Based ASCVD Risk Phenotype Clusters
Aim 3: Demonstrate HIE-Omics-Integrated Learning Healthcare System with Direct-to-Consumer Sequencing

## Key facts

- **NIH application ID:** 10225340
- **Project number:** 5F30LM013320-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Aaron S Eisman
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $53,536
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-10-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225340

## Citation

> US National Institutes of Health, RePORTER application 10225340, Improving the Accuracy of ASCVD Risk Estimation Using Population-Level EHR and Genetic Data (5F30LM013320-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10225340. Licensed CC0.

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