# Effectiveness of Therapies for Heart Failure with Mid-Range Ejection Fraction

> **NIH NIH R56** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $777,674

## Abstract

Project Summary / Abstract
 Evidence from trials has established that 4 separate classes of guideline-directed medical therapies
(GDMT) for heart failure with reduced ejection fraction (HFrEF) together reduce mortality by nearly two-thirds.
Conversely, for heart failure with mid-range ejection fraction (HFmrEF), trials have produced only weaker
evidence, resulting in ambiguous guidelines. Our long-term goal is to build the largest echocardiography
registry in the world to generate comparative effectiveness evidence for questions for specific populations in
heart failure where trials have not been able to provide strong evidence. The overall objectives in this
application are to build the registry, test mathematical assumptions required for the application of strong causal
inference methods developed in economics, and then apply the strongest methods possible to measure
treatment effects for GDMT in HFmrEF. The central hypothesis is that “real-world” treatment decisions will
differ at guideline-suggested thresholds of left ventricular ejection fraction (LVEF), and patients on either side
of those immediate LVEF thresholds will be otherwise similar (“as good as random”), allowing the application of
regression discontinuity methods to measure treatment effects. We believe these methods will demonstrate
reduced mortality with GDMT for patients with HFmrEF. The rationale for this proposal is that (1) LVEF cutoffs
in guidelines are semi-arbitrary in the sense that LVEF exists on a physiological continuum but guideline
cutoffs are based on strict thresholds, and (2) our preliminary data demonstrates expected discontinuities in
treatment frequency at influential LVEF thresholds for other HF therapies (defibrillators) in real-world practice.
As such, the conditions for “as good as random” likely exist in small ranges around relevant LVEF thresholds,
allowing the application of regression discontinuity methods. Our central hypothesis will be tested with 2
specific aims. In Aim #1, we will use natural language processing to aggregate data from echocardiography
reports from 11 hospitals creating the largest echocardiography registry in the world, allowing clinical
adjudication of both clinical information and echocardiographic images. We will then systematically test
mathematical assumptions required for regression discontinuity. In Aim #2, we will create robust estimates of
each of the effects of each GDMT medication class on mortality in HFmrEF with fuzzy RDD and alternative
methods, including propensity score methods. The feasibility of propensity score methods does not depend on
the Aim 1 analyses, so at least 1 of the 2 proposed methods will be feasible. We therefore expect that these
results will upgrade evidence from the current class 2b (“usefulness is unknown”) in heart failure guidelines for
3 of the 4 GDMT medication classes. We think the proposed research is innovative because it combines
strong causal inference methods to observational data with...

## Key facts

- **NIH application ID:** 11192968
- **Project number:** 1R56HL171144-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Jason Harmon Wasfy
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $777,674
- **Award type:** 1
- **Project period:** 2024-09-24 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11192968

## Citation

> US National Institutes of Health, RePORTER application 11192968, Effectiveness of Therapies for Heart Failure with Mid-Range Ejection Fraction (1R56HL171144-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11192968. Licensed CC0.

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