Study of AI-Based ECG Analysis to Detect Acute Coronary Occlusion and Its Impact on Treatment Timing and Heart Damage in Adults with Non-ST Elevation Acute Coronary Syndrome

What this Trial Is About

Researchers are investigating the use of artificial intelligence AI to improve the diagnosis and timing of treatment for patients with suspected acute coronary syndrome ACS who do not show ST elevation in their ECG. The study focuses on detecting acute coronary occlusion myocardial infarction OMI using an AI model to see if it can better identify patients needing urgent care compared to traditional methods. The goal is also to understand how the timing from diagnosis to intervention affects heart damage size and outcomes. The study observes patients diagnosed with non-ST elevation ACS who undergo coronary angiography, a procedure to open blocked arteries using a balloon and stent. The AI model analyzes ECGs to classify patients as having OMI or not, and researchers compare these results with the timing of percutaneous coronary intervention PCI and troponin peak levels, which reflect heart damage. This approach uses natural variations in clinical practice to explore the potential benefits of AI-guided diagnosis and timing. Participants will be monitored for up to 12 months for outcomes including cardiovascular mortality and infarct size. Data collected include ECG analyses, timing from diagnosis to PCI, and troponin levels. The study is observational and does not alter standard care it follows patients through their usual clinical pathway while assessing the accuracy and impact of the AI model on diagnosing OMI and related outcomes.

Artificial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion Myocardial Infarction