Improving ECG methods to quickly identify heart issues in chest pain patients

Who could benefit from this research

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.

Why it matters

Where this research is happening

• 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

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.

  NIH research University of Rochester · Rochester, United States · NIH-11550067

• 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.

  NIH research University of California, San Francisco · San Francisco, United States · NIH-11371961

• 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.

  NIH research University of Rochester · Rochester, United States · NIH-11292312

• 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.

  NIH research University of California, San Diego · La Jolla, United States · NIH-11288452

• 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.

  NIH research University of Rochester · Rochester, United States · NIH-11099852

• 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.

  NIH research Wake Forest University Health Sciences · Winston-Salem, United States · NIH-11003318

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.