# Preventing Cardiovascular Disease through Improved Risk Prediction and Communication

> **NIH NIH K01** · DUKE UNIVERSITY · 2020 · $139,020

## Abstract

Cardiovascular disease (CVD) remains a leading cause of death in the United States. Statin therapy has
proven remarkably effective in reducing CVD events when used in those at risk, yet statins remain under-
utilized. These gaps in care represent a twofold problem: a failure to appropriately identify candidates for
primary prevention and a failure of patients to take these medications when offered. Currently available models
to predict CVD risk have significant limitations, particularly for younger adults. Further, their probabilistic
outputs are neither easily understood nor impactful to patients. Dr. Navar's K01 proposal uses pooled data
from seven NHLBI cohort studies to create an improved CVD risk prediction model for young adults. This
model will estimate “continuous“ lifetime CVD risk, incorporating a range of risk factors and evaluating risk at
multiple patient ages. Second, Dr. Navar will evaluate how this novel continuous lifetime risk model improves
identification of candidates for statin therapy relative to current risk tools. Third, to improve the ability to
communicate these data to patients, she will transform these risk estimates into population relative risk. This
will allow a patient to understand how his CVD risk profile compares with similar age- and gender-matched
peers. She will then use a unique national registry to better understand how best to communicate numerical
and probabilistic data to which patients. Finally, she will develop a web-based risk communication tool that
incorporates the novel lifetime risk, population relative risk, and current 10-year risk estimates and pilot this tool
in patient focus groups. This web-based tool will be linked to the electronic health record and tested in an
outpatient clinical setting to assess how real time estimation and patient-tailored shared decision-making affect
clinical care. The current proposal will also assist Dr. Navar in fully strengthening her background in public
health. The didactic and applied statistical experiences, including training in predictive modeling and causal
analysis, will allow her to become more prepared to be a national leader in the understanding of real world
safety and effectiveness of preventive therapies. Similarly, the formal training in qualitative research and
behavioral science proposed in this application, combined with the practical development of a CVD risk
communication tool, will allow her to establish a program of independent research focused on improved uptake
of CVD preventive therapies through effective patient risk communication. This training program builds on
already successful research at the Duke Clinical Research Institute (DCRI). The mentorship team, led by Dr.
Eric Peterson, Director of the DCRI and expert in CVD outcomes research, includes experts in risk prediction
(Pencina, co-mentor, and D'Agostino, external advisor) and risk communication (Boulware, co-mentor), all of
whom will ensure scientific success and oversee the can...

## Key facts

- **NIH application ID:** 9982377
- **Project number:** 5K01HL133416-05
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Ann Marie Navar
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $139,020
- **Award type:** 5
- **Project period:** 2016-08-15 → 2020-09-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982377

## Citation

> US National Institutes of Health, RePORTER application 9982377, Preventing Cardiovascular Disease through Improved Risk Prediction and Communication (5K01HL133416-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9982377. Licensed CC0.

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