# HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targets

> **NIH NIH U54** · NORTHWESTERN UNIVERSITY · 2024 · $303,614

## Abstract

Title:
Extraction of SDOH elements from multisite EHR for endorsed CDE mapping and analysis:
HeartShare
Project Summary:
Heart failure with preserved ejection (HFpEF) is a highly prevalent and complex disorder that
confers a substantial burden of morbidity and mortality. In contrast with the many evidence-based
therapeutic options available for heart failure with reduced ejection fraction (HFrEF), progress for
disease-modifying therapies for HFpEF has been limited and five-year survival rates following
hospitalization have remained stagnant at approximately 50%. A major barrier in identifying
effective treatments for HFpEF (identified by the NHLBI HFpEF Working Group convened in 2020) is
the “one size fits all” approach to what is a heterogeneous syndrome that comprises many
different subtypes.1 Therefore, the primary goals of HeartShare are to classify heart failure with
preserved ejection fraction (HFpEF) into distinct phenotypes, characterize disease mechanisms,
and identify therapeutic targets for each HFpEF subtype. The study includes three overlapping
components; a prospective, observational study of patients with HFpEF and controls that begins
with an intensive in-person assessment, a low-touch longitudinal registry and a HeartShare EHR
Study. In the EHR study, a multi-center retrospective cohort of patients with heart failure is being
created to better understand the epidemiology and health care patterns of a large, diverse
population of patients with HFpEF across seven health systems.
A supplement focusing on extraction of SDOH information with mapping to established CDE in
HeartShare accomplishes numerous objectives; 1) raising the awareness of CDE in the Heart
Failure advocacy and research communities (who are highly integrated in the program) promote
collection of high quality interoperable data in future trials and studies 2) generation of high-quality
individual-level SDOH data from a large EHR dataset—which would be expected to contain more
diversity in SDOH than many trials and studies which often disproportionately enroll individuals of
higher SES and 3) integration of individual-level SDOH into numerous HeartShare datasets
facilitating numerous analyses investigating the critical role of SDOH as exposures, covariates,
and mediators for key Heart Failure outcomes.
Over the past several years, many health systems have begun to systematically collect individual-
level, health-related social risk data; the collection of this data was motivated both by internal
interest to implement programs to advance health equity as well as evolving external incentives
and requirements focused on this type of data collection. Although several groups, such as the HL7
Gravity Project and PhenX, have sought to develop consensus-driven standards for health-related
social risk data from research participants and patients, these standards have not been widely
adopted by individual health systems or EHR vendors and the result is a wide variety of data
co...

## Key facts

- **NIH application ID:** 11167161
- **Project number:** 3U54HL160273-04S1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Abel N. Kho
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $303,614
- **Award type:** 3
- **Project period:** 2024-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11167161

## Citation

> US National Institutes of Health, RePORTER application 11167161, HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targets (3U54HL160273-04S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11167161. Licensed CC0.

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