# Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $720,825

## Abstract

PROJECT SUMMARY / ABSTRACT
The pathophysiologic heterogeneity underlying heart failure with preserved ejection fraction (HFpEF) is poorly-
understood and is a major barrier to effective HFpEF treatments, necessitating bold new approaches to HFpEF
phenotyping. The long-term goal is to reduce the substantial morbidity and mortality of HFpEF by enabling better
detection, understanding and treatment of its phenotypic subtypes. The overall objectives of this application are
to (i) develop machine learning algorithms that can sequentially detect HFpEF then identify HFpEF phenotypes
using widely-available data; then (ii) validate this detection-phenotyping approach in a large cross-University of
California (UC) cohort. The central hypothesis is that machine learning can algorithmically extract physiologically-
valuable information from raw multi-modality data to phenotype HFpEF. The rationale is that algorithms to reliably
phenotype HFpEF will provide both the framework to investigate phenotype-specific mechanisms and therapies,
and the method by which to identify target patients. The first aim will develop algorithms that can reliably detect
HFpEF using widely-available electrocardiogram (ECG) data. Neural network algorithms will be trained using
ECG data to discriminate HFpEF from heart failure with reduced ejection fraction and patients without heart
failure. For the second aim, a novel machine learning architecture will be developed to extract maximal
information from multiple diagnostic modalities simultaneously. This architecture will then be used to train
algorithms to identify and phenotype HFpEF with widely-available data: ECGs, echocardiograms (echo) and
specific electronic health record (EHR) data elements. Once reproducible HFpEF phenotypes are identified using
our multi-modal neural network phenogrouping approach, we will characterize physiologic differences between
identified phenotypes. The third aim will construct a cross-UC heart failure/HFpEF cohort to externally validate
these multi-modal HFpEF algorithms and the identified HFpEF phenotypes. The cross-UC heart failure/HFpEF
cohort will be updated regularly and designed to support future prospective multi-center studies. The research
proposed in this application is innovative, in the applicant’s opinion, because it develops a novel algorithmic
approach to extract maximal information from widely-available data in multiple modalities simultaneously, to
more closely mimic how physicians triangulate information to make diagnoses. The proposed research is
significant because applying this algorithmic approach to HFpEF is expected to provide a critical phenotypic
framework, through which current and future HFpEF therapies can be tested and administered, and which will
also support future investigations into underlying disease mechanisms. Ultimately, establishment of reproducible
HFpEF phenotypes, and the ability to identify them with widely-available data, would dramatically shift the
manage...

## Key facts

- **NIH application ID:** 10683803
- **Project number:** 1R56HL161475-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Geoffrey H Tison
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $720,825
- **Award type:** 1
- **Project period:** 2022-09-13 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10683803

## Citation

> US National Institutes of Health, RePORTER application 10683803, Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms (1R56HL161475-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10683803. Licensed CC0.

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