Electrocardiographic warning system to speed up chest-pain care (ECG-SMART 2)
Official title: Electrocardiographic Detection of Non-ST Elevation Myocardial Events for Accelerated Classification of Chest Pain Encounters (ECG-SMART 2)
Funded at University of Rochester · NIH-11099852 · Data updated 2026-11-12
A research project is testing new ECG-based computer tools to quickly and safely identify patients with potentially serious chest pain in emergency departments.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | April 15, 2018 |
| Project end | June 30, 2027 |
| Funding year | 2026 |
| Award (this year) | $423,203 |
| NIH project number | 5R01HL137761-09 |
| Project ID | NIH-11099852 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults presenting to emergency departments with chest pain who may be at risk for acute coronary syndromes.
✕Less likely to help
Patients with chest pain not related to acute coronary syndromes or those not eligible for the bedside deployment may not benefit.
What this research studies
This project builds on a large, real-world ECG database from many hospitals to create machine‑learned heart-attack risk rules that flag non‑ST elevation events. Researchers will validate these rules at the bedside and set up a real-time system to help clinicians decide patient priority. The work combines advanced algorithms with interpretable signals linked to heart ischemia to improve how chest pain is managed in the emergency setting. The ultimate goal is faster, more accurate triage so patients receive appropriate care sooner.
Why it matters
Potential benefit: If successful, patients with chest pain could get faster and more accurate assessment, reducing delays in treatment for those with serious heart problems.
How similar studies have gone: Previous work showed promise with ML-based ECG interpretation; this project extends it to real-time bedside use with external validation.
Where this research is happening
-
University of Rochester
Rochester, United States Active
Researchers
- Principal investigator: Al-Zaiti, Salah S — University of Rochester
- Study coordinator: Al-Zaiti, Salah S
Related research
Other NIH-funded projects on the same topics.
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ECG-BASED smart tools to quickly classify chest pain in emergency care
A patient-focused project to use advanced ECG analysis and machine learning to quickly identify heart problems in people with chest pain when they arrive at the ER.
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How emergency doctors’ decisions about chest pain affect testing and outcomes
A study using Medicare data will see if doctors who tend to admit chest-pain patients to the hospital lead to more tests and different outcomes for those patients.
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Using ECGs to find hidden heart attacks in people with chest pain
This project is creating smart ECG tools to quickly spot heart attacks that don't show the classic ST-elevation in people who come to the emergency room with chest pain.
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MACE-Alert: AI tools to help triage chest pain in the emergency department
An AI-powered system and a patient-facing chat tool aim to improve how emergency teams evaluate and triage people with chest pain.
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Evaluating a new approach for managing chest pain patients in outpatient settings
This study is testing a new way to help people with chest pain who aren't at high risk by letting them go home from the emergency room instead of staying in the hospital, and then checking in with them within three days to make sure they're doing okay and to manage any heart health concerns.
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Improving ECG methods to quickly identify heart issues in chest pain patients
This study is working on a new tool to help doctors quickly and accurately check patients with chest pain in the emergency room, using advanced technology to spot heart issues that might be missed by regular tests, so they can provide faster and better care for you.
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.