AI Reading of Triage ECGs to Spot Blocked Heart Arteries in Chest Pain

Trial details

Who runs it

University Medical Centre Maribor

Trial code

NCT07882849

Record updated

20 October 2026

Original description

Chest pain is among the most common reasons people attend an emergency department, and the electrocardiogram (ECG) recorded at triage is the first test used to decide who needs urgent treatment for a blocked coronary artery. Recognising the ECG patterns that indicate an acutely blocked artery is difficult. Many such patterns do not produce the classic ST-segment elevation, and identifying them reliably requires a level of ECG expertise that is not always available at the point of triage - particularly at night and among clinicians who are not cardiologists.Read full original descriptionShow less

Prospective External Validation of an Artificial Intelligence Electrocardiogram Model for Detection of Occlusion Myocardial Infarction in Patients Presenting to Emergency Department Triage With Suspected Acute Coronary Syndrome: The TRIACS Study

Chest pain is among the most common reasons people attend an emergency department, and the electrocardiogram (ECG) recorded at triage is the first test used to decide who needs urgent treatment for a blocked coronary artery. Recognising the ECG patterns that indicate an acutely blocked artery is difficult. Many such patterns do not produce the classic ST-segment elevation, and identifying them reliably requires a level of ECG expertise that is not always available at the point of triage - particularly at night and among clinicians who are not cardiologists.

Artificial intelligence (AI) models that read the 12-lead ECG have been shown in several retrospective studies, mainly from Western Europe and the United States, to identify acute coronary occlusion more sensitively than conventional interpretation. These models are now in clinical use in a number of health systems. Their performance has not been assessed in a prospectively enrolled, unselected Slovenian emergency department population, and diagnostic accuracy commonly declines when a model is applied outside the setting in which it was developed.

TRIACS is a prospective, single-centre, observational external validation study in the Emergency Department of University Medical Centre Maribor. All consecutive adults presenting with symptoms suggestive of acute coronary syndrome who have a 12-lead ECG recorded at triage as part of routine care will be enrolled. Each triage ECG is analysed by the AI model. The model's output is withheld from all treating clinicians and plays no part in patient care; every patient receives standard guideline-directed management.

The main aim is to measure how accurately the AI model identifies angiographically confirmed occlusion myocardial infarction in this population, and to compare its accuracy with the triage decision actually made by the clinical team. The study is therefore designed to inform an adoption decision in either direction, rather than to demonstrate a predetermined conclusion.

Inclusion: Age ≥18 years; Presentation to ED triage with symptoms suspicious for acute coronary syndrome, including chest discomfort, dyspnoea, epigastric pain, or other anginal equivalents; Standard 12-lead ECG recorded at triage as part of routine care, available in machine-readable or interpretable image format.

Exclusion: Ventricular arrhythmia or ongoing cardiopulmonary resuscitation at the time of the triage ECG; ECG of insufficient technical quality for analysis (recorded and reported as an exclusion count); Patients with prehospital STEMI diagnosis transported directly to the catheterisation laboratory and bypassing ED triage; Patients for whom laboratory results are not retrievable by UKC MB; Traumatic cause of chest pain; Declined participation / withdrawal of consent.

ClinicalTrials.gov NCT07882849