Finding which people with sleep apnea are at higher risk of heart disease
Official title: Application of Machine Learning to Identify Obstructive Sleep Apnea Subgroups at Risk for Atherosclerosis Progression and Cardiovascular Disease Events (OSA-GRANDE)
Funded at Icahn School of Medicine at Mount Sinai · NIH-11262916 · Data updated 2026-11-12
Using machine learning to identify which adults with obstructive sleep apnea are most likely to develop heart disease and who may benefit most from CPAP treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | March 1, 2024 |
| Project end | December 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $718,857 |
| NIH project number | 5R01HL168897-03 |
| Project ID | NIH-11262916 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults (21+) with a diagnosis of obstructive sleep apnea, especially those using or considering CPAP or who have cardiovascular risk factors, would be the most relevant group.
✕Less likely to help
People without obstructive sleep apnea, children, or those not represented in the clinical datasets and electronic records used are unlikely to see direct benefit from this project.
What this research studies
This project applies advanced computer learning to large, well-characterized datasets to look beyond standard sleep tests and find different subtypes of sleep apnea. The team will combine sleep study measures, clinical information, and electronic health records to build tools that predict progression of artery disease and future heart events. They will also search for patterns that show who gets the most benefit from CPAP treatment. Models will be validated using real-world health record data to check they work across different patient groups.
Why it matters
Potential benefit: If successful, the work could help doctors target CPAP and other care to patients most likely to avoid heart disease and personalize treatment plans.
How similar studies have gone: Previous randomized trials have not shown clear heart-protection from CPAP, and applying machine learning to multimodal clinical and EHR data to find responsive subgroups is a newer approach with limited prior demonstration of success.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Shah, Neomi a — Icahn School of Medicine at Mount Sinai
- Study coordinator: Shah, Neomi a
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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.