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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

Author-Logo_PM
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

Can an AI ECG algorithm improve diagnostic accuracy for acute coronary occlusion in the difficult subset of canceled catheterization lab activations?

Discordance in ECG interpretation between Emergency Medicine and Cardiology teams is common, and within canceled STEMI activations, a true acute coronary occlusion myocardial infarction (OMI) can go unrecognized. This retrospective study examined whether an AI ECG algorithm (Queen of Hearts™) could improve OMI detection in this difficult subset. Across three referral centers, the investigators analyzed 185 activations canceled for not meeting STEMI criteria, of which 17 met the definition of a missed OMI. The AI algorithm identified 16 of 17 cases, far exceeding STEMI criteria in sensitivity (94.1% vs. 47.1%), supporting its use as an adjunct to clinical judgment in ambiguous cases.

Artificial Intelligence-Assisted, ECG-Based Triage of Patients With Chest Pain to Immediate Invasive Treatment

Rapid identification of acute coronary occlusion (ACO) is critical in chest pain patients, yet conventional STEMI criteria miss occlusion in many NSTEMI cases, delaying life-saving invasive treatment. This retrospective study tested whether a deep learning ECG AI model could improve ACO detection in an unselected cohort of more than 4,000 consecutive patients from a German chest pain unit. Each 12-lead ECG was assessed using both standard STEMI criteria and the AI model, with ACO independently adjudicated by a blinded physician. The AI model clearly outperformed STEMI criteria, identifying 73 of 104 ACO cases versus 30 (area under the curve 0.958 vs. 0.589), with fewer false positives. The findings suggest AI-assisted ECG interpretation can detect subtle ischemic changes beyond established criteria and support faster triage of NSTE-ACO patients to immediate invasive care.

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