Creating a platform to better understand heart failure with preserved ejection fraction using advanced data analysis.
Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms
This study is looking to make it easier to understand and treat heart failure with preserved ejection fraction (HFpEF) by using smart computer tools to analyze common health data, like ECGs, so that doctors can better identify different types of HFpEF and provide more personalized care for patients.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project ID | NIH-10683803 on NIH RePORTER |
What this research studies
This research aims to improve the understanding and treatment of heart failure with preserved ejection fraction (HFpEF) by developing machine learning algorithms that analyze widely-available health data, including electrocardiograms (ECGs). The project will focus on detecting HFpEF and identifying its various phenotypes, which are essential for tailoring effective treatments. By utilizing a large cohort from the University of California, the researchers will validate their approach to ensure it accurately captures the complexities of HFpEF. This innovative methodology seeks to enhance patient care by providing insights into the specific characteristics of heart failure patients.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals diagnosed with heart failure with preserved ejection fraction, particularly those with varying phenotypic characteristics.
Not a fit: Patients with heart failure with reduced ejection fraction or those without any form of heart failure may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and personalized treatment options for patients with heart failure.
How similar studies have performed: Previous research has shown promise in using machine learning for medical diagnostics, suggesting that this approach could yield significant advancements in understanding heart failure.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Tison, Geoffrey H — University of California, San Francisco
- Study coordinator: Tison, Geoffrey H
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 below.