AI detection of hidden coronary occlusions and timing of treatment for heart attacks
Artificial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion Myocardial Infarction Without ST Elevation
This project tests whether an AI reading of 12‑lead ECGs can find hidden coronary occlusions in adults with suspected non‑ST‑elevation ACS and whether the time to PCI after that detection relates to troponin peak levels.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1500 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Azienda Ospedaliera di Bolzano Academic / other |
| Locations | 1 site (Bolzano, BZ) |
| Trial ID | NCT06910436 on ClinicalTrials.gov |
What this trial studies
This is a prospective observational study that applies an artificial intelligence ECG model to adults presenting with suspected non‑ST‑elevation acute coronary syndrome who undergo routine invasive coronary angiography. The AI model will be used to identify occlusion myocardial infarction (OMI) from digitized 12‑lead ECGs while usual clinical care pathways are maintained. For patients the study identifies as having OMI, investigators will record the elapsed time from diagnosis to PCI and compare peak troponin values across different timing intervals. Coronary angiogram findings and routine clinical data will be collected to link AI ECG findings with angiographic occlusion and biochemical evidence of infarction.
Who should consider this trial
Good fit: Adults over 18 with a working diagnosis of non‑ST‑elevation ACS who have good‑quality digital 12‑lead ECGs and are scheduled for invasive coronary angiography are the intended participants.
Not a fit: Patients with ST‑elevation MI, poor or corrupted ECG digitalization, major sustained ventricular arrhythmias, or those under 18 are unlikely to benefit from this AI evaluation.
Why it matters
Potential benefit: If successful, the approach could help clinicians find occult coronary occlusions earlier so that timely PCI reduces infarct size and potential complications.
How similar studies have performed: Previous research has shown promising results for AI algorithms in detecting occult coronary occlusion patterns on ECGs, but few prospective observational studies have directly linked AI detection to timing of reperfusion and infarct size.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age \> 18 yrs * Working diagnosis of Non- ST Elevation Acute Coronary Syndrome after the assessment by specialist Exclusion Criteria: * ST-Elevation Myocardial infarction * Age \< 18 yrs * Major sustained ventricular arrhythmias * Corrupted ECG images * Poor digitalisation quality of the ECG
Where this trial is running
Bolzano, BZ
- Azienda Sanitaria di Bolzano — Bolzano, Bz, Italy (Recruiting)
Study contacts
- Study coordinator: Matthias Unterhuber, MD, Associate Prof.
- Email: matthias.unterhuber@gmail.com
- Phone: +39 471 43 9950
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.