# Toward Equity in ATTR Cardiomyopathy Diagnosis

> **NIH NIH F32** · STANFORD UNIVERSITY · 2024 · $50,211

## Abstract

PROJECT SUMMARY
Transthyretin amyloid cardiomyopathy (ATTR-CM) is a life-threatening multi-system disease that is grossly
underdiagnosed, particularly in underserved groups. Early diagnosis of ATTR-CM is crucial to delay disease
progression, and now possible with newly available, disease-modifying medications that markedly decrease
heart failure hospitalizations and mortality. There is an urgent need to understand the factors associated with
delayed diagnosis and the clinical sequelae (Aim 1) and to create interventions to identify patients with ATTR-
CM and ensure timely treatment (Aim 2).
Aim 1 of this project will identify predictors of delayed diagnosis of ATTR-CM and estimate the effect of
delayed diagnosis on patient outcomes using National VA and Medicare data. This will determine the
sociodemographic, medical, and geographic factors associated with delayed diagnosis and the association
between delayed diagnosis rates at the patient and county level with cardiovascular hospitalization and
mortality.
Aim 2 will retrospectively assess the implementation of an echo-based machine learning algorithm to assist in
the diagnosis of ATTR-CM and evaluate its effect on diagnostic disparities. Characteristics of patients (race,
sex, age, social risk/vulnerability, homelessness, vulnerability) first identified through a clinical diagnosis, first
identified via the machine learning algorithm (without a pre-existing clinical diagnosis of ATTR-CM), and those
with a clinical diagnosis of ATTR-CM who are not captured by the machine learning algorithm will be compared
to identify the effect of implementation of the machine learning algorithm on diagnostic disparities.
This study will advance the field by identifying groups at risk for delayed diagnosis ATTR-CM that can be
targeted by health services interventions. Additionally, it will contribute to the nascent literature on evaluating
and ensuring equity in artificial intelligence-based healthcare interventions. It will also advance the applicant’s
goal of becoming a clinician-investigator in cardiology with a focus on health equity. Through an expert
mentorship team and with strong institutional support, they will: 1) gain critical skills to rigorously interrogate
large national databases to identify causes of diagnostic inequality, 2) understand how empirically derived
machine learning models improve or worsen existing inequalities, and 3) establish expertise in an emerging
important cause of heart failure with evidence of significant existing inequity.

## Key facts

- **NIH application ID:** 10997266
- **Project number:** 1F32HL176160-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Gabriela M. Spencer Bonilla
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $50,211
- **Award type:** 1
- **Project period:** 2024-12-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10997266

## Citation

> US National Institutes of Health, RePORTER application 10997266, Toward Equity in ATTR Cardiomyopathy Diagnosis (1F32HL176160-01). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10997266. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*