Finding women at risk for heart disease after pregnancy complications
Leveraging machine learning for cardiovascular disease risk prediction and prevention in women with a history of adverse pregnancy outcomes
This project uses machine learning to find which women who had pregnancy complications are most likely to develop heart disease so they can get earlier follow-up and prevention.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham and Women's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-11234257 on NIH RePORTER |
What this research studies
If you had a pregnancy complication such as preeclampsia, gestational diabetes, high blood pressure in pregnancy, preterm birth, or a small baby, researchers will use your delivery and early postpartum information to look for patterns tied to later heart problems. They will combine details about the pregnancy, delivery, baby outcomes, health and lifestyle, and other medical records and run machine learning to identify risk "types" among women with these histories. The team aims to build a clinical decision tool that providers could use at or soon after delivery to guide blood pressure, cholesterol, or lifestyle follow-up and prevention. The work will use large linked datasets and may include follow-up of health outcomes years after delivery.
Who could benefit from this research
Good fit: Women with a history of adverse pregnancy outcomes—preeclampsia, gestational hypertension, gestational diabetes, preterm birth, or small-for-gestational-age infant—especially those recently delivered or with linked medical records, are the ideal candidates.
Not a fit: Women without a history of pregnancy complications or those without available medical records may not directly benefit from this project.
Why it matters
Potential benefit: If successful, the work could help doctors identify high-risk women early and offer targeted prevention to lower future heart disease risk.
How similar studies have performed: Prior studies showed that pregnancy complications are linked to higher heart disease risk, but using machine learning to define risk phenotypes and build a clinical decision tool is a new approach.
Where this research is happening
Boston, United States
- Brigham and Women's Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Rich-Edwards, Janet W — Brigham and Women's Hospital
- Study coordinator: Rich-Edwards, Janet W
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.