Assessing sudden cardiac death risk in patients with mitral valve prolapse using advanced imaging and AI.
Official title: Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
Funded at University of California, San Francisco · NIH-10814950 · Data updated 2026-11-12
This study is looking at how we can better understand the risk of sudden heart problems in people with mitral valve prolapse by using special heart imaging tests, so we can find out who might need extra care or treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | May 26, 2020 |
| Project end | April 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $759,534 |
| NIH project number | 5R01HL153447-05 |
| Project ID | NIH-10814950 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with mitral valve prolapse, particularly those with a history of arrhythmias or other cardiac symptoms.
✕Less likely to help
Patients without mitral valve prolapse or those who do not exhibit any cardiac symptoms may not benefit from this research.
What this research studies
This research investigates the risk of sudden cardiac arrest and death in individuals with mitral valve prolapse (MVP) by utilizing advanced imaging techniques such as cardiac magnetic resonance (CMR) and echocardiography, combined with machine learning algorithms. The study aims to identify specific imaging markers that can predict these severe outcomes, which currently lack reliable indicators. By analyzing data from patients with MVP, the research seeks to improve risk stratification and guide decisions regarding preventive measures like implantable cardioverter defibrillators (ICDs). Patients may undergo imaging tests to help determine their risk levels and potential treatment options.
Why it matters
Potential benefit: If successful, this research could lead to better identification of patients at risk for sudden cardiac events, allowing for timely interventions that could save lives.
How similar studies have gone: Previous research has shown promise in using advanced imaging techniques and machine learning for risk assessment in cardiac conditions, indicating potential for success in this novel approach.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Delling, Francesca N — University of California, San Francisco
- Study coordinator: Delling, Francesca N
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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.