Using ECGs to improve health outcomes for patients on dialysis
Official title: Deep learning on ECGs to improve outcomes in patients on dialysis
Funded at Icahn School of Medicine at Mount Sinai · NIH-11091572 · Data updated 2026-11-12
This study is looking to help people on dialysis by using heart scans and smart computer techniques to predict and prevent serious health issues during their treatment, and it will involve patients from dialysis centers in New York City.
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 | August 1, 2023 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $657,073 |
| NIH project number | 5R01HL167050-03 |
| Project ID | NIH-11091572 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients who are currently undergoing maintenance hemodialysis and are at risk for cardiovascular complications.
✕Less likely to help
Patients who are not on dialysis or those with stable cardiovascular health may not receive any benefit from this research.
What this research studies
This research focuses on improving the health outcomes of patients undergoing maintenance hemodialysis by utilizing electrocardiograms (ECGs) and advanced deep learning techniques. The study aims to develop accurate predictive models to identify risks of intradialytic hypotension and major adverse cardiovascular events, which are common complications in this patient population. By analyzing ECG waveform data, the research seeks to provide actionable insights that can lead to better-targeted interventions and ultimately enhance patient care. The study will involve recruiting a diverse group of patients from dialysis units in New York City to validate these predictive models.
Why it matters
Potential benefit: If successful, this research could lead to improved monitoring and management of cardiovascular risks in patients on dialysis, potentially reducing morbidity and mortality.
How similar studies have gone: Previous research has shown promise in using deep learning with ECG data for predicting cardiac outcomes, indicating that this approach has potential for success.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Nadkarni, Girish Nitin — Icahn School of Medicine at Mount Sinai
- Study coordinator: Nadkarni, Girish Nitin
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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.