CardioPredict ML: A smarter way to gauge heart risk before pregnancy
Official title: CardioPredict ML: Machine Learning-enhanced Cardiovascular Risk Assessment for Preconception Care
Funded at Pennsylvania State Univ Hershey Med Ctr · NIH-11445270 · Data updated 2026-11-12
A machine learning tool helps identify women at risk for pregnancy-related heart problems before conception, using health records and prior pregnancy data.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Pennsylvania State Univ Hershey Med Ctr NIH-funded |
| Lab location | 1 site (Hershey, United States) |
| Project start | September 15, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $232,719 |
| NIH project number | 1R21HL184290-01A1 |
| Project ID | NIH-11445270 on NIH RePORTER |
Who could benefit from this research
✓Could help
Women of reproductive age who are evaluating pregnancy plans and have access to electronic health records suitable for preconception risk modeling.
✕Less likely to help
Women without available prior health data or who are planning pregnancy but not candidates for machine-learning risk tools may see limited benefit.
What this research studies
This project aims to create and test a machine learning model that predicts cardiovascular complications during pregnancy using preconception data from electronic health records. The approach relies on data from the TriNetX global health network to train and validate the algorithm with women who were evaluated before conception and followed through delivery and the postpartum year. If successful, the model could be implemented in clinics to provide earlier, more accurate risk assessments for expecting mothers. The study will balance model performance with ease of integration into real-world clinical workflows to support preconception counseling.
Why it matters
Potential benefit: If successful, patients could receive earlier and more accurate risk predictions for pregnancy-related heart issues, enabling proactive care and safer pregnancies.
How similar studies have gone: Prior studies suggest machine learning can outperform traditional tools in predicting cardiovascular risk, supporting the potential of this approach.
Where this research is happening
-
Pennsylvania State Univ Hershey Med Ctr
Hershey, United States Active
Researchers
- Principal investigator: Nawab, Khalid — Pennsylvania State Univ Hershey Med Ctr
- Study coordinator: Nawab, Khalid
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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.