[Switch from STEMI to OMI]

Area of Science:

• Cardiology
• Medical Diagnostics
• Artificial Intelligence in Medicine

Background:

• Current ST-elevation myocardial infarction (STEMI) diagnostic criteria, established historically, are criticized for their limitations and frequent use as sole indicators for acute angiography.
• False positive STEMI diagnoses can lead to unnecessary invasive procedures, highlighting a need for more accurate diagnostic approaches.

Purpose of the Study:

• To discuss the transition from STEMI to Occlusion Myocardial Infarction (OMI) as a diagnostic standard.
• To explore the diagnostic capabilities of OMI, with and without electrocardiogram (ECG) data.
• To evaluate the potential of OMI in improving patient selection for immediate cardiac interventions.

Main Methods:

• Critical analysis of existing STEMI diagnostic criteria and their application in clinical practice.
• Review of OMI diagnostic methodologies, including non-ECG based approaches.
• Examination of retrospective studies utilizing Artificial Intelligence (AI) models based on OMI for predicting intervention needs.

Main Results:

• Occlusion Myocardial Infarction (OMI) demonstrates superior sensitivity compared to STEMI criteria.
• AI models leveraging OMI have shown significant promise in retrospectively identifying patients requiring immediate intervention.
• OMI diagnosis can be achieved with or without ECG findings, offering broader applicability.

Conclusions:

• Implementing OMI could lead to more accurate and timely interventions for myocardial infarction.
• Further prospective studies are warranted to confirm OMI's role in guiding urgent interventions.
• OMI represents a promising diagnostic paradigm that could be integrated into clinical practice in the near future.