# Modeling and Forecasting Atherosclerotic Risk: A Complex Systems Approach

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2020 · $585,430

## Abstract

Project Summary/Abstract
Inequality in health outcomes in relation to Americans' socioeconomic status (SES) is rising: a recent study by the
Brookings Institution found that life expectancy for men and women in the top 10% of career earnings was over 10 years
greater than those in the bottom 10%. Cardiovascular disease – still leading cause of death for Americans – merits study
with respect to these findings.
More research on how SES affects atherosclerotic risk is needed. The goal of our project is to develop advanced
forecasting algorithms for atherosclerotic cardiovascular disease (ASCVD)-related events – both at baseline and
longitudinally – using systems-based modeling methodologies which incorporate probabilistic representations of patients'
socioeconomic and environmental characteristics. This represents a paradigm shift beyond existing models used in
guiding primary and secondary prevention of atherosclerotic disease; in particular, risk models developed by the
American College of Cardiology Foundation and the American Heart Association (ACCF/AHA) are based solely on
physiological risk factors. We believe that the prediction performance of ASCVD risk models can be significantly
improved by incorporating socioeconomic and environmental risks, especially in an era where improved primary and
secondary prevention and increased socioeconomic inequality have resulted in complex phenomena among elderly
Americans with respect to ASCVD risk.
Our preliminary work indicates a significant degree of neighborhood-level variability in major ASCVD events
(myocardial infarction, stroke or cardiovascular death), with low-SES neighborhoods associated with event rates over
three times that of high-SES neighborhoods. Moreover, neighborhood SES explained four times the amount of
neighborhood-level variation in ASCVD event rates than that explained by the ACCF/AHA Pooled Cohort Equations
Risk Model. Our proposed project will therefore provide an essential risk modeling platform to health care systems
focused on optimizing the health of populations that are highly heterogeneous with respect to socioeconomic and
environmental characteristics.
These models will be developed in a team-based environment, including translational scientists from general internal
medicine, cardiology, social work, spatial epidemiology, urban poverty, community development, immunology, and data
and population health sciences. Informing the models will be a newly-established, cutting-edge regional research registry,
based on electronic health data from Northeast Ohio's two largest health systems, Cleveland Clinic and MetroHealth.
Ultimately, this research is anticipated to yield new mechanistic insights and hypotheses, more accurate prediction models
for cardiovascular outcomes, and a basis for informing decisions at multiple strategic and programmatic levels.

## Key facts

- **NIH application ID:** 9903107
- **Project number:** 5R01AG055480-04
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** JARROD DALTON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $585,430
- **Award type:** 5
- **Project period:** 2017-09-15 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9903107

## Citation

> US National Institutes of Health, RePORTER application 9903107, Modeling and Forecasting Atherosclerotic Risk: A Complex Systems Approach (5R01AG055480-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9903107. Licensed CC0.

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