Cardiac Arrest, Out-Of-HospitalAID-OMIENCT06767709
AID-OMIE - Artificial Intelligence in Detection of Occlusive Myocardial Infarction in Emergency Medicine
An observational study of Cardiac Arrest, Out-Of-Hospital and Cardiac Arrest Due to Underlying Cardiac Condition, run by Institute of Mountain Emergency Medicine.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiac Arrest, Out-Of-Hospital, Cardiac Arrest Due to Underlying Cardiac Condition
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2025to 31 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who suffered OHCA from presumed cardiac cause and sustained ROSC.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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
Read more
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.
Read the 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.
- Keywords
- Cardiac arrest, OHCA, Occlusion myocardial infarction, OMI, Artificial intelligence
- Medical subject headings
- Out-of-Hospital Cardiac Arrest, Heart Arrest
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
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.
Measured: Within 7 days after OHCA
Secondary outcomes (2)
Frequency of OMI post-OHCA without ST-elevation in the post-ROSC ECG
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.
Measured: 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.
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.
Measured: Within 7 days from OHCA
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2025
- Study start1 Sep 2025 (estimated)
- Primary completion30 May 2026 (estimated)
- Study completion31 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac Arrest, Out-Of-HospitalStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Institute of Mountain Emergency Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Simon Rauch, Institute of Mountain Emergency Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06767709
- Sponsor's protocol ID
- 01-2025
- Acronym
- AID-OMIE
- Status last verified
- Aug 2025
- First submitted
- 5 Jan 2025
- Last updated
- 8 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AID-OMIE - Artificial Intelligence in Detection of Occlusive Myocardial Infarction in Emergency Medicine
- ClinicalTrials.gov ID
- NCT06767709
- Acronym / sponsor ID
- AID-OMIE / 01-2025
- Keywords
- Cardiac arrest, OHCA, Occlusion myocardial infarction, OMI, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Institute of Mountain Emergency MedicineUniversity, hospital or other
- Study start
- 1 Sep 2025 (estimated)
- Primary completion
- 30 May 2026 (estimated)
- Study completion
- 31 May 2026 (estimated)
- First posted
- 10 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who suffered OHCA from presumed cardiac cause and sustained ROSC.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (6 listed)
- In-Hospital Cardiac Arrest (IHCA)
- Age < 18 years
- Traumatic cardiac arrest
- Cardiac arrest from a clear non-cardiac cause
- Corrupted ECG images
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
2 secondary outcomes
Frequency of OMI post-OHCA without ST-elevation in the post-ROSC ECG
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiac Arrest, Out-Of-Hospital and Cardiac Arrest Due to Underlying Cardiac Condition, run by Institute of Mountain Emergency Medicine.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 8 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Simon Rauch, MD, PhD (+393404967398, simon.rauch@eurac.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Aug 2025.