Improving diagnosis of a heart condition in older adults, especially in underserved groups.
Official title: Toward Equity in ATTR Cardiomyopathy Diagnosis
Funded at Stanford University · NIH-10997266 · Data updated 2026-11-12
This study is looking into why some older adults, especially those in underserved communities, take longer to get diagnosed with a serious heart condition called ATTR-CM, and it aims to use technology to help doctors spot it earlier so patients can get better care sooner.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | December 1, 2024 |
| Project end | November 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $83,932 |
| NIH project number | 1F32HL176160-01 |
| Project ID | NIH-10997266 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include older adults, particularly those aged 65 and above, especially from underserved racial and ethnic backgrounds.
✕Less likely to help
Patients under the age of 65 or those without risk factors for ATTR-CM may not benefit from this research.
What this research studies
This research focuses on transthyretin amyloid cardiomyopathy (ATTR-CM), a serious heart condition that is often missed in older adults, particularly those from underserved communities. The project aims to identify why some patients experience delays in diagnosis and how these delays impact their health outcomes. It will also explore the use of a machine learning algorithm to enhance the accuracy of diagnosing ATTR-CM, potentially leading to earlier treatment and better patient care. By analyzing data from various sources, the research seeks to create targeted interventions to improve diagnosis rates.
Why it matters
Potential benefit: If successful, this research could lead to earlier diagnosis and treatment of ATTR-CM, significantly improving health outcomes for patients.
How similar studies have gone: Previous research has shown that machine learning algorithms can effectively improve diagnostic accuracy in various medical conditions, suggesting potential success for this approach.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Spencer Bonilla, Gabriela M. — Stanford University
- Study coordinator: Spencer Bonilla, Gabriela M.
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.