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Powerful Medical
3. November 2024
5 min to read

State-of-the-Art Review – From ST-Segment Elevation MI to Occlusion MI: The New Paradigm Shift in Acute Myocardial Infarction

From ST-Segment Elevation MI to Occlusion MI

Overview:

This state-of-the-art review explores the evolution of heart attack classification, challenging the limitations of the standard-of-care STEMI/NSTEMI framework. It advocates for a shift toward diagnosing heart attacks based on the presence of acute vessel occlusion rather than relying solely on standard ECG criteria. By redefining how myocardial infarctions are identified and managed, this approach has the potential to reduce misdiagnoses, optimize triage, and refine treatment prioritization in emergency cardiology.

Published In: Journal of the American College of Cardiology (JACC) – JACC Advances
Presented Date: November 03, 2024

Introduction

A generation ago thrombolytic therapy led to a paradigm shift in myocardial infarction (MI), from Q-wave/non-Q-wave to ST-segment elevation MI (STEMI) vs non-STEMI. Using STE on the electrocardiogram (ECG) as a surrogate marker for acute coronary occlusion (ACO) allowed for rapid diagnosis and treatment. But the vast research catalyzed by the STEMI paradigm has revealed increasing anomalies: 25% of “non-STEMI” have ACO with delayed reperfusion and higher mortality.

Studying these limitations has given rise to the occlusion MI (OMI) paradigm, based on the presence or absence of ACO in the patient rather than STE on ECG. The OMI paradigm shift harnesses advanced ECG interpretation aided by artificial intelligence, complementary bedside echocardiography and advanced imaging, and clinical signs of refractory ischemia, and offers the next opportunity to transform emergency cardiology and improve patient care. This State-of-the-Art Review examines the paradigm shifts from Q wave to STEMI to OMI.

Highlights

  • The STEMI paradigm transformed emergency cardiology, but there is increasing recognition of its limitations.
  • STEMI criteria is a poor surrogate marker for acute coronary occlusion, leading to delayed reperfusion.
  • Evidence-based advances can identify OMI not meeting STEMI criteria, and false positive STEMI.
  • The OMI paradigm harnesses advanced ECG interpretation aided by artificial intelligence, echocardiography, and advanced imaging.

Authors: Jesse McLaren, José Nunes de Alencar, Emre K. Aslanger, H Pendell Meyers, and Stephen W. Smith

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Powerful Medical leads one of the most important shifts in modern medicine by augmenting human-made clinical decisions with artificial intelligence. Our primary focus is on cardiovascular diseases, the world’s leading cause of death.

About PMcardio

PMcardio is a CE-certified AI that reads ECGs and offers a complex assessment of 49 cardiac conditions. Clinically validated in 15+ studies and trusted by over 100,000 clinicians, it delivers rapid, expert‑level interpretations, empowering emergency physicians, GPs, nurses, paramedics, and cardiologists to act with confidence at the point of care. Available for Individuals and Organizations.

About Powerful Medical

Established in 2017, Powerful Medical has embarked on a mission to revolutionize the diagnosis and treatment of cardiovascular diseases. We are a medical company backed by 28 world-class cardiologists and led by our expert Scientific Board with decades of experience in daily patient care, clinical research, and medical devices. The results of our research are implemented, developed, certified, and brought to market by our 50+ strong interdisciplinary team of physicians, data scientists, AI experts, software engineers, regulatory specialists, and commercial teams.

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Relevant Publications

An AI Model for Electrocardiogram Detection of Occlusion Myocardial Infarction: A Retrospective Study to Reduce False Positive Cath Lab Activations

This single-centre retrospective study examined whether an AI-powered ECG model (PMcardio’s Queen of Hearts OMI model) can better detect acute occlusion myocardial infarction (OMI) and reduce false-positive cardiac catheterization laboratory (CCL) activations compared with traditional STEMI millimetre criteria. The authors analysed 304 consecutive STEMI pathway activations over a 2-year period at a tertiary academic centre and applied the AI model to pre-angiography 12-lead ECGs, comparing its performance against standard STEMI criteria. The AI model showed higher sensitivity (89.2% vs 68.3%), higher specificity (72.9% vs 51.7%), greater overall accuracy (82.9% vs 61.8%), and a high AUROC of 0.884 for identifying true OMI, while correctly flagging nearly three-quarters of false-positive activations as non-OMI. These findings suggest that integrating a specialized AI-ECG model into existing STEMI alert pathways could meaningfully reduce unnecessary CCL activations without compromising the detection of true occlusions.

Bifascicular Block Associated With Myocardial Infarction: A Marker of Proximal Left Anterior Descending Artery Occlusion Confirmed by the Artificial Intelligence-Based Smartphone App Queen of Hearts

This single-patient case report describes an elderly man presenting with chest pain, hypotension, and bifascicular block (BFB)—a combination of right bundle branch block (RBBB) and left anterior fascicular block (LAFB)—whose ECG showed QRS‑concordant anterior and lateral ST‑segment elevation consistent with a STEMI‑equivalent / occlusive myocardial infarction (OMI) pattern. Urgent coronary angiography revealed a long, severely calcified, near‑occlusive proximal left anterior descending (LAD) artery lesion, successfully treated with primary PCI and drug‑eluting stent implantation, achieving TIMI 3 flow. The Queen of Hearts (PMcardio) AI‑based smartphone app correctly classified the ECG as STEMI‑equivalent, identified atrial flutter and BFB, and predicted reduced left ventricular ejection fraction, later confirmed by echocardiography (EF 38%). This case underscores BFB with concordant anterior ST elevation as a high‑risk marker of proximal LAD‑culprit OMI and provides anecdotal evidence that specialized AI‑enabled ECG interpretation can support rapid, accurate decision‑making in ACS. 

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