Dynamic risk prediction and subtyping of hypertensive disorders in pregnancy
Official title: Dynamic Risk Prediction and Phenotype Discovery in Preeclampsia and Gestational Hypertension
Funded at Emory University · NIH-11442165 · Data updated 2026-11-12
A digital health approach uses AI to spot early warning signs of preeclampsia and gestational hypertension from pregnancy data, so clinicians can tailor care for pregnant people.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Emory University NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | September 1, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $426,948 |
| NIH project number | 1R21HD123150-01 |
| Project ID | NIH-11442165 on NIH RePORTER |
Who could benefit from this research
✓Could help
Pregnant individuals in the first or second trimester who have or are at risk for hypertensive disorders and whose care is documented in electronic health records at participating centers.
✕Less likely to help
Pregnant people whose data are not included in the study or whose HDP risk is not altered by the AI-driven approach may not experience additional benefit.
What this research studies
This project will develop AI models that combine structured and unstructured electronic health record data to find meaningful subtypes of hypertensive disorders in pregnancy and predict when HDP may develop before diagnosis. By focusing on dynamic changes across gestation, the study aims to move beyond static risk scores toward personalized surveillance and management. The work will be conducted using data from patients at academic medical centers, with validation to ensure the models translate into real-world clinical workflows. The ultimate goal is to provide clinicians with timely, mechanism-specific risk information to improve maternal and fetal outcomes.
Why it matters
Potential benefit: If successful, this could enable earlier detection and more personalized monitoring and treatment of HDP for pregnant people.
How similar studies have gone: Previous research suggests AI-driven analyses of EHR data can uncover clinically relevant subtypes and improve prediction, but the integration into obstetric care remains to be demonstrated.
Where this research is happening
-
Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Katebijahromi, Nasim — Emory University
- Study coordinator: Katebijahromi, Nasim
Related research
Other NIH-funded projects on the same topics.
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Gestational hypertension and preeclampsia: better blood pressure targets before delivery
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A new, affordable ultrasound AI system helps frontline workers identify pregnancy complications like high blood pressure, preeclampsia, and restricted fetal growth.
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This grant studies how giving antihypertensive medicine during pregnancy for women with chronic high blood pressure affects their children’s health as they grow up.
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Reducing chronic high blood pressure after preeclampsia to protect heart and vessels
A randomized trial tests whether more intensive blood pressure control in the weeks after giving birth can lower long-term high blood pressure and improve blood vessel health in women who had a hypertensive pregnancy.
-
Why blood pressure can stay high after preeclampsia
This work looks at how immune cells and tiny blood vessel changes may cause long-lasting high blood pressure in women after preeclampsia.
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.