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Powerful Medical
16. May 2024
3 min to read

Single Center Retrospective Validation of an Artificial Intelligence ECG Model Detecting Acute Coronary Occlusion

Overview:

In this single-center US-based cohort study of emergency department patients with suspected heart attacks, the PMcardio STEMI AI ECG Model outperformed standard STEMI criteria, improving detection sensitivity by 30%. Patients correctly identified by AI but missed by cardiologists faced notable treatment delays (~22 hours), underscoring the AI’s potential to enable faster diagnosis and earlier intervention.

Published In: Heart Rhythm
Presented Date: May 16, 2024

Background

STEMI criteria have low sensitivity to detect acute coronary occlusion myocardial infarction (OMI). The Queen of Hearts (QOH) is an artificial intelligence (AI) model developed by Powerful Medical (Bratislava, Slovakia) which has been shown to outperform STEMI criteria for detection of OMI. Performance in all-comer chest pain populations is not known.

We evaluated QOH in our center in consecutive patients presenting to the emergency department (ED) with suspected acute coronary syndrome (ACS).

Methods

We reviewed patients who presented to our ED in October 2023 with suspected ACS. We included patients who ruled out by serial troponin or underwent angiography. Primary outcome of OMI was defined as culprit artery with TIMI flow 0-2 or culprit artery with TIMI flow 3 and high sensitivity troponin I > 5000 ng/L. Secondary outcome was door-to-device time (D2D). QOH outputs an AI interpretation (OMI or not OMI) and confidence level (low, mid, or high).

We considered OMI with mid or high confidence to be positive. For cardiologist over-read, we considered “acute MI” to be positive. We compared sensitivity using Fisher’s exact test, specificity and accuracy using Chi square, and D2D using Student’s t-test.

Results

Among 528 patients, 498 (94.3%) ruled out and 31 (17.0%) had angiography. OMI outcome was met in 10 (1.9%) and was not met in 518 (98.1%). Comparing QOH to cardiologist over-read, sensitivity was 70% vs 40%, (p=0.18), specificity was 99.2% vs 99.4% (p=NS), and accuracy was 98.7% vs 98.3% (p=0.6), respectively. For patients with QOH and cardiologist positive ECG, mean D2D was 86 ± 21 minutes. For patients with QOH positive and cardiologist negative ECG, mean D2D was 1358 ± 452 minutes (p=0.0021).

Conclusion

In our center, QOH ECG interpretation improved sensitivity for OMI by 30% without loss of specificity. QOH could expedite identification of OMI for early intervention.


Authors: William Frick, Issam Atallah, Rabya Saraf, Kuan-Yu Lin, Chien-Jung Lin, Phillip Mar

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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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