AID-OMIE - Artificial Intelligence in Detection of Occlusive Myocardial Infarction in Emergency Medicine (AID-OMIE)

August 5, 2025 updated by: Simon Rauch, Institute of Mountain Emergency Medicine

Study Objective and Hypothesis The study hypothesizes that artificial intelligence (AI)-assisted interpretation of the 12-lead electrocardiogram (ECG) can improve the care of patients resuscitated after out-of-hospital cardiac arrest (OHCA) by enabling faster and more accurate detection of occlusion myocardial infarction (OMI). This enhanced diagnostic approach could reduce the time required for revascularization, improve patient outcomes, and decrease unnecessary activations of cardiac catheterization laboratories. The primary objective of the study is to assess the effectiveness of an AI-powered ECG model in identifying acute OMI in OHCA patients whose post-return of spontaneous circulation (ROSC) ECG does not show ST-elevation.

Methods

This is a retrospective observational study involving OHCA patients in Bolzano, Italy, who meet the following inclusion criteria:

Aged 18 years or older. Achieved ROSC after cardiac arrest. Underwent coronary angiography (CAG) within seven days post-OHCA. Prehospital post-ROSC ECG and CAG reports available.

Exclusion criteria include in-hospital cardiac arrest (IHCA), traumatic cardiac arrest, cardiac arrest from a non-cardiac cause, and poor-quality or corrupted ECG images. Post-ROSC ECGs will be analyzed using the PMcardio App, an AI tool for ECG interpretation. The data will be fully anonymized before storage. Coronary angiography charts will be reviewed for the presence of atherosclerotic lesions, the degree of arterial narrowing, and Thrombolysis in Myocardial Infarction (TIMI) flow, which assesses blood flow in coronary arteries.

Study Outcomes The primary outcome is the sensitivity and specificity of the AI-assisted ECG in detecting OMI in patients whose post-ROSC ECG does not show ST-elevation. Secondary outcomes include the frequency of OMI in OHCA patients without ST-elevation and the ability of the AI model to rule out OMI accurately in these cases.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

Study Objective and Hypothesis The study hypothesizes that artificial intelligence (AI)-assisted interpretation of the 12-lead electrocardiogram (ECG) can improve the care of patients resuscitated after out-of-hospital cardiac arrest (OHCA) by enabling faster and more accurate detection of occlusion myocardial infarction (OMI). This enhanced diagnostic approach could reduce the time required for revascularization, improve patient outcomes, and decrease unnecessary activations of cardiac catheterization laboratories. The primary objective of the study is to assess the effectiveness of an AI-powered ECG model in identifying acute OMI in OHCA patients whose post-return of spontaneous circulation (ROSC) ECG does not show ST-elevation.

Methods

This is a retrospective observational study involving OHCA patients in Bolzano, Italy, who meet the following inclusion criteria:

OHCA from 2018-2025 Aged 18 years or older. Achieved ROSC after cardiac arrest. Underwent coronary angiography (CAG) within seven days post-OHCA. Prehospital post-ROSC ECG and CAG reports available.

Exclusion criteria include in-hospital cardiac arrest (IHCA), traumatic cardiac arrest, cardiac arrest from a non-cardiac cause, and poor-quality or corrupted ECG images. Post-ROSC ECGs will be analyzed using the PMcardio App, an AI tool for ECG interpretation. The data will be fully anonymized before storage. Coronary angiography charts will be reviewed for the presence of atherosclerotic lesions, the degree of arterial narrowing, and Thrombolysis in Myocardial Infarction (TIMI) flow, which assesses blood flow in coronary arteries.

Study Outcomes The primary outcome is the sensitivity and specificity of the AI-assisted ECG in detecting OMI in patients whose post-ROSC ECG does not show ST-elevation. Secondary outcomes include the frequency of OMI in OHCA patients without ST-elevation and the ability of the AI model to rule out OMI accurately in these cases.

Study Type

Observational

Enrollment (Estimated)

200

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients who suffered OHCA from presumed cardiac cause and sustained ROSC.

Description

Inclusion Criteria:

  • OHCA from with ROSC in the Province of Bolzano, Italy
  • Coronary angiography (CAG) within 7 days post-OHCA
  • Age > 18 years
  • Available prehospital post-ROSC ECG
  • Available CAG report

Exclusion Criteria:

  • In-Hospital Cardiac Arrest (IHCA)
  • Age < 18 years
  • Traumatic cardiac arrest
  • Cardiac arrest from a clear non-cardiac cause
  • Corrupted ECG images
  • Poor ECG digitalization quality

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Patients after Out-of-Hospital Cardiac Arrest (OHCA) with ROSC in the Province of Bolzano, Italy
Patients after Out-of-Hospital Cardiac Arrest (OHCA) with Return of Spontaneous Circulation (ROSC) in the Province of Bolzano, Italy

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity and specificity of detecting OMI from the post-ROSC ECG with AI-assisted ECG interpretation in patients following OHCA with ROSC, where the post-ROSC ECG does not show ST-elevation.
Time Frame: Within 7 days after OHCA
Sensitivity and specificity of detecting occlusion myocardial infarction (OMI) from the electrocardiogram (ECG) taken after return of spontaneous circulation (ROSC) using artificial intelligence (AI)-assisted ECG interpretation in patients resuscitated from out-of-hospital cardiac arrest (OHCA) with ROSC, where the post-ROSC ECG does not display ST-segment elevation.
Within 7 days after OHCA

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Frequency of OMI post-OHCA without ST-elevation in the post-ROSC ECG
Time Frame: Within 7 days after OHCA
Frequency of occlusion myocardial infarction (OMI) in patients resuscitated from out-of-hospital cardiac arrest (OHCA) who achieved return of spontaneous circulation (ROSC), where the electrocardiogram (ECG) recorded post-ROSC does not show ST-segment elevation.
Within 7 days after OHCA
Sensitivity and specificity of excluding OMI with AI-assisted ECG interpretation in patients following OHCA with ROSC, where the post-ROSC ECG does not show ST-elevation.
Time Frame: Within 7 days from OHCA
Sensitivity and specificity of ruling out occlusion myocardial infarction (OMI) using artificial intelligence (AI)-assisted electrocardiogram (ECG) interpretation in patients resuscitated from out-of-hospital cardiac arrest (OHCA) who achieved return of spontaneous circulation (ROSC), where the post-ROSC ECG does not display ST-segment elevation.
Within 7 days from OHCA

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Institute of Mountain Emergency Medicine

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 1, 2025

Primary Completion (Estimated)

May 30, 2026

Study Completion (Estimated)

May 31, 2026

Study Registration Dates

First Submitted

January 5, 2025

First Submitted That Met QC Criteria

January 5, 2025

First Posted (Actual)

January 10, 2025

Study Record Updates

Last Update Posted (Actual)

August 8, 2025

Last Update Submitted That Met QC Criteria

August 5, 2025

Last Verified

August 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 01-2025

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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