Synergy of Elevation of the Head and Thorax and REBOA During Out-of-Hospital Cardiac Arrest
NCT06537492
Cardiac Arrest, Out-Of-Hospital, Cardiopulmonary Arrest
Grenoble, France
View Trial DetailsNCT Number: NCT06767709
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.
Trial opening soon.
Get Notified18 year and older
All sexes
Observational
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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.
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.
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.
Contact information is provided by the study sponsor or research team.
Institute of Mountain Emergency Medicine
Other
Acronym: AID-OMIE
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