Acute Coronary Syndrome, Coronary Occlusion, Coronary Syndrome, NSTEMI, NSTEMI - Non-ST Segment Elevation MI, STEMI
Conditions
Keywords
Acute Coronary Syndrome, ECG, Artificial Intelligence, Occlusion Myocardial Infarction, Myocardial Infarction (MI)
Brief summary
AI ECG TIMI is an investigator-initiated, international, and multicenter registry of acute coronary syndrome patients aimed to identify electrocardiographic findings detected by an AI model predicting coronary blood flow alteration. The aim of the study is to identify electrocardiographic findings detected by an automated artificial intelligence (AI) model that can predict coronary blood flow alteration as assessed by the TIMI grade flow at the very moment of the invasive coronary angiography in patients with acute coronary syndromes.
Interventions
None listed
Sponsors
Powerful Medical
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
* Patients with acute coronary syndromes undergoing invasive coronary angiography as clinically indicated. * Availability of a standard 12-lead ECG performed at invasive coronary angiogram before percutaneous coronary intervention (maximum within 15 min before vascular access). * Age ≥18 years.
Exclusion criteria
* Individuals presenting for a non-emergent (elective) cardiac catheterization. * Individuals presenting with chronic coronary syndrome (CCS) or stable angina symptoms. * Individuals without symptoms suspicious for acute coronary syndromes. * Individuals with contraindications for cardiac catheterization.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of the AI-ECG TIMI AI Model | Index hospitalization (assessed up to 5 days) | The primary endpoint is the AI-ECG TIMI model's ability to identify patients with actively occluded (TIMI 0-1) at the time of invasive coronary angiography using only single-standard 12-lead ECGs assessed by accuracy, sensitivity, specificity, positive, negative predictive values and F1 scores. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Subgroup Performance | Index hospitalization (assessed up to 5 days) | AI-ECG TIMI AI Model's performance across subgroups 1. Demographic 2. Risk factors \& co-morbidities 3. Electrocardiographic 4. Culprit artery territory |
| Correlate established definitions of occlusion myocardial infarction (OMI) to myocardial perfusion status | Index hospitalization (assessed up to 5 days) | 2\. OMI definitions using routine angiographic and laboratory parameters will be correlated to abnormal myocardial perfusion using correlation coefficients. |
| ECG prediction of mechanical reperfusion | Index hospitalization (assessed up to 5 days) | a. Correlation of AI-ECG TIMI model's raw numeric predictions (between 0 and 1) with myocardial perfusion grading post PCI. |
| Assessment of expert ECG interpretation blinded to all clinical information | Index hospitalization (assessed up to 5 days) | 1. Accuracy, sensitivity, specificity, positive and negative predictive value of expert interpretation of active OMI, reperfused OMI and entirety of OMI 2. Inter-rater variability of expert interpretation of active OMI, reperfused OMI and entirety of OMI. |
| STEMI-equivalent ECG patterns | Index hospitalization (assessed up to 5 days) | a. Prevalence of STEMI equivalent ECG patterns and their correlation to TIMI flow grade at angiography. |
| Subgroup analysis of NSTEMI-OMI patients | Index hospitalization (assessed up to 5 days) | 9\. NSTEMI-OMI stratified according to immediate (\<2h) vs. SoC (\>2h) invasive management, median: i. Peak troponin (maximum rise before fall) ii. Median LVEF at discharge iii. Length of hospital stay iv. Length of cardiac care unit (CCU) or intensive care unit (ICU) stay v. Presence of life-threatening arrhythmias |
Countries
Austria, Belgium, Italy
Contacts
PRINCIPAL_INVESTIGATORRobert Herman, MD, PhD
Powerful Medical
STUDY_CHAIREmanuele Barbato, MD, PhD
University of Roma La Sapienza
STUDY_CHAIRJozef Bartunek, MD, PhD
Azorg Hospital, Aalst
Outcome results
None listed