# Testing a CVD screening tool in pregnant and postpartum women

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA-IRVINE · 2021 · $257,148

## Abstract

Cardiovascular disease (CVD) has emerged as the leading cause of maternal mortality in the United States.
Approximately one third of the maternal deaths could have been prevented if CVD was diagnosed earlier by
the health care providers. Women's CVD symptoms are often misdiagnosed or dismissed, causing delays in
the recognition and treatment of CVD with a high risk for serious short- and long-term morbidity and mortality.
In particular, African-American women exhibit three-to-four-fold higher mortality rate, presence of pre-existing
CVD, hypertensive disorders of pregnancy, and peripartum cardiomyopathy compared to other racial/ethnic
groups. A standardized screening tool could guide clinicians in identifying pregnant women at risk of CVD who
require additional testing and follow up. The California Maternal Quality Care Collaborative developed a
screening algorithm that guides stratification and initial evaluation of symptomatic or high-risk pregnant or
postpartum women. This algorithm was implemented at two regional Level 3 birthing centers with high volume
Medicaid patients and diverse racial ethnic mix: University of California, Irvine (UCI), and Albert Einstein
College/Montefiore Medical Center (MMC), New York. In this new R21 we hypothesize that this algorithm has
a potential in predicting CVD in pregnant women. We will test this idea by three aims. Aim 1 will validate a
CVD screening algorithm in a sample of 3,000 pregnant and postpartum women at two regional
hospital networks. We will conduct a retrospective medical chart review to describe the CVD screening
algorithm's implementation at 23 clinic sites at UCI and MMC and obtain feedback on the experiences of the
clinicians and patients. Aim 2 will determine the prevalence of CVD in pregnancy and postpartum women
by race / ethnicity. Estimating a 1.5% true positive rate at UCI and a 3.0% true positive rate at MMC, we will
identify approximately 75 women with CVD among 3,000 women screened. We will compare the prevalence
among the African-American population with that of White, Hispanic, Asian, and Other/mixed race women. Aim
3 will calculate the positive predictive value of the CVD screening tool, each of its parameters, and
combination of parameters. We will calculate the overall predictive value of the CVD algorithm and false
positive and true positive values. We will calculate the positive predictive value of each of the 18 parameters of
the algorithm or any informative combinations of the algorithm. Even though CVD is the leading cause of
maternal mortality, a reliable clinical screening approach is lacking. This proposal will determine the predictive
value of this innovative CVD screening tool, which has the potential to become the standard of care for all
pregnant and postpartum women and, ultimately, decreasing VD related morbidity and mortality.

## Key facts

- **NIH application ID:** 10128875
- **Project number:** 1R21HD101783-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Afshan Hameed
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $257,148
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10128875

## Citation

> US National Institutes of Health, RePORTER application 10128875, Testing a CVD screening tool in pregnant and postpartum women (1R21HD101783-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10128875. Licensed CC0.

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