Using machine learning to predict heart disease risk in patients with a specific blood condition
Official title: Personalized Prediction of Cardiovascular Outcomes through Machine Learning Analysis of Cardiac MRI and Genomics
Funded at Yale University · NIH-11175780 · Data updated 2026-11-12
This study is looking at how computer technology can help doctors predict heart problems in people with a condition called Clonal Hematopoiesis of Indeterminate Potential (CHIP) by analyzing heart scans and genetic information, so they can provide better, personalized care even before any symptoms appear.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | September 17, 2024 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $753,813 |
| NIH project number | 1R56HL175627-01 |
| Project ID | NIH-11175780 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Clonal Hematopoiesis of Indeterminate Potential (CHIP), including both cancer and non-cancer patients.
✕Less likely to help
Patients without CHIP or those who do not have cardiovascular risk factors may not benefit from this research.
What this research studies
This research investigates how machine learning can analyze cardiac MRI and genomic data to predict cardiovascular outcomes in patients with Clonal Hematopoiesis of Indeterminate Potential (CHIP). By utilizing advanced algorithms, the study aims to identify patients at risk of heart failure or myocardial infarction, even if they do not currently show symptoms. The approach involves evaluating heart images and genetic information to create risk scores that help in understanding individual patient risks. This could lead to more personalized treatment plans and better management of heart health.
Why it matters
Potential benefit: If successful, this research could enable earlier identification and intervention for patients at risk of heart disease, potentially improving their health outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning techniques for predicting cardiovascular outcomes, suggesting that this approach could be effective.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Kwan, Jennifer M — Yale University
- Study coordinator: Kwan, Jennifer M
Related research
Other NIH-funded projects on the same topics.
-
Aging Well with HIV: Alcohol, Risk Reduction, and Education Center (AWAR3E)
A center at Yale is exploring how aging with HIV interacts with alcohol use to create inflammation and frailty, with the goal of finding biomarkers and potential treatments.
-
Community Health Worker–Led Hypertension Prevention and Control in Nepal
A community health worker–driven approach in Nepal aims to prevent and control high blood pressure among adults by bringing education and care directly to communities and homes.
-
Community health worker-led hypertension prevention and control in Nepal
A community health worker–led program in Nepal aims to help adults prevent and better control high blood pressure through home visits, education, and ongoing support.
-
Adapting a mobile health tool to boost HIV outcomes for older adults with HIV in Ukraine
A mobile health platform called PositiveLinks would be adapted for older adults with HIV in Ukraine to help them stay engaged in care and improve treatment success.
-
Behavioral and neural links between spoken language sounds and written words in young children
This grant studies how young children learn from listening and reading to improve literacy, by looking at how sound patterns transfer to written language.
-
Understanding how autoimmunity causes myasthenia gravis
This project looks at why the immune system attacks the nerve-muscle connection in myasthenia gravis and how different patient groups are affected.
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.