Improving ECG methods to quickly identify heart issues in chest pain patients
Electrocardiographic Detection of Non-ST Elevation Myocardial Events for Accelerated Classification of Chest Pain Encounters (ECG-SMART 2)
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.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-10893324 on NIH RePORTER |
What this research studies
This research focuses on enhancing the ability to quickly and accurately assess patients who arrive at emergency departments with chest pain, a serious condition that can indicate heart problems. By utilizing advanced machine learning algorithms on a large database of ECG readings, the project aims to develop tools that can detect non-ST elevation myocardial events, which are often missed by traditional ECG methods. The goal is to create a real-time decision support system that can assist healthcare providers in making faster and more informed decisions at the bedside. This could lead to improved patient outcomes by ensuring timely treatment for those at risk of acute coronary events.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals who present to emergency departments with chest pain symptoms.
Not a fit: Patients who do not experience chest pain or have already been diagnosed with a stable cardiac condition may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnoses for patients experiencing chest pain, potentially saving lives.
How similar studies have performed: Previous research has shown promise in using machine learning for ECG interpretation, indicating that this approach could be effective in clinical settings.
Where this research is happening
Pittsburgh, United States
- University of Pittsburgh at Pittsburgh — Pittsburgh, United States (Active)
Researchers
- Principal investigator: Al-Zaiti, Salah S — University of Pittsburgh at Pittsburgh
- Study coordinator: Al-Zaiti, Salah S
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 below.