Using machine learning to find sleep apnea patients at risk for 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-11041046 · Data updated 2026-11-12
This study is looking at how advanced computer technology can help find people with obstructive sleep apnea who might be more likely to develop heart problems, so that doctors can tailor treatments to each person's needs and improve their care.
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 | 2025 |
| Award (this year) | $669,381 |
| NIH project number | 5R01HL168897-02 |
| Project ID | NIH-11041046 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include adults diagnosed with obstructive sleep apnea who are at risk for cardiovascular disease.
✕Less likely to help
Patients without obstructive sleep apnea or those who do not have cardiovascular disease risk factors may not benefit from this research.
What this research studies
This research investigates how machine learning can analyze various data sources to identify patients with obstructive sleep apnea (OSA) who are at higher risk for developing cardiovascular disease (CVD). By examining multimodal data beyond traditional sleep studies, the project aims to create predictive tools that can forecast CVD events and understand how different patients respond to CPAP treatment. The goal is to enhance patient care by personalizing treatment strategies based on individual risk profiles. Participants will be monitored and their health records will be used to validate the effectiveness of these predictive models.
Why it matters
Potential benefit: If successful, this research could lead to better-targeted treatments for sleep apnea patients, potentially reducing their risk of heart disease.
How similar studies have gone: Previous research has shown promise in using machine learning for health predictions, suggesting that this approach could yield significant insights in the context of sleep apnea and cardiovascular risk.
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.