Using ECGs to find hidden heart attacks in people with chest pain
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-11292312 · Data updated 2026-11-12
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.
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 | 2024 |
| Award (this year) | $586,726 |
| NIH project number | 7R01HL137761-08 |
| Project ID | NIH-11292312 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults who present with chest pain or suspected acute coronary syndrome in participating emergency departments or prehospital (ambulance) systems would be the ideal candidates.
✕Less likely to help
People whose conditions are already clearly identified by standard ECGs (for example classic ST-elevation heart attacks) or whose chest pain is due to clearly non-cardiac causes may not gain additional benefit from this tool.
What this research studies
The team collected thousands of 12-lead ECGs linked to outcomes from multiple hospitals and ambulances to train computer models. They used machine learning to find ECG patterns tied to non-ST-elevation coronary events and checked that these patterns make clinical sense. Now they plan to build a bedside, real-time decision support system and test it prospectively in clinical settings. The work includes external validation at other hospitals and a technical setup to deliver ECG alerts to clinicians during patient care.
Why it matters
Potential benefit: If successful, this could help doctors find and treat heart attacks sooner in people whose ECGs do not show the classic ST-elevation, reducing missed diagnoses and delays in care.
How similar studies have gone: Prior retrospective work using the project's large ECG database showed promising algorithm performance, but real-time bedside deployment and prospective validation remain new steps.
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.
-
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.
-
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.
-
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.
-
Electrocardiographic warning system to speed up chest-pain care (ECG-SMART 2)
A research project is testing new ECG-based computer tools to quickly and safely identify patients with potentially serious chest pain in emergency departments.
-
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.
-
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.