Finding which people with sleep apnea are at higher risk of heart disease

Application of Machine Learning to Identify Obstructive Sleep Apnea Subgroups at Risk for Atherosclerosis Progression and Cardiovascular Disease Events (OSA-GRANDE)

NIH-funded research Icahn School of Medicine at Mount Sinai · NIH-11262916

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 typeR01 grant
Study typeNIH-funded research
Funding institutionIcahn School of Medicine at Mount Sinai NIH-funded
Lab location1 site (New York, United States)
Project IDNIH-11262916 on NIH RePORTER

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.

Who could benefit from this research

Good fit: 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.

Not a fit: 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.

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 performed: 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

New York, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-10 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.