Episode Details

Date: January 3, 2026  Reference: Shroyer et al. Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by PMcardio). Am J Emerg Med. 2025 Nov. Guest Skeptic: Dr. Amal Mattu has been on the faculty at the University of Maryland since 1996. He has developed an academic niche in emergency cardiology and electrocardiography, and he also enjoys teaching and writing on other topics, including emergency geriatrics, faculty development, and risk management. Amal is currently a tenured professor and Vice Chair of Emergency Medicine at the University of Maryland School of Medicine, and a Distinguished Professor of the University of Maryland-Baltimore. Case: A 58-year-old man with diabetes and hypertension arrives at the emergency department (ED) 30 minutes after the sudden onset of substernal chest pressure radiating to the left arm, now improved to 3/10. His vital signs are BP 146/88, HR 92, RR 18, O2 sat 98% on room air. The initial 12-lead ECG shows RBBB with left anterior fascicular block and subtle anterior ST‑depression with proportionally tall, broad T waves in V2 to V4. This is an appearance that can be seen with Hyper-Acute T Wave Occlusive Myocardial Infarction (HATW‑OMI) or an ST-Elevated Myocardial Infarction (STEMI)‑mimic in conduction disease. A debate ensues between emergency medicine and cardiology on whether to activate the cath lab now or get troponins plus serial ECGs? Background: Emergency physicians need to be experts at interpreting ECGs. For decades, we’ve been taught STEMI criteria, only to learn repeatedly that important exceptions exist (posterior OMI, de Winter, hyperacute T waves, modified Sgarbossa in LBBB, etc.). Those exceptions have evolved into two distinct categories. There are the STEMI‑equivalents (OMI without classic ST‑elevation) and STEMI‑mimics (ST‑elevation without OMI). That expanding exception list increases diagnostic complexity and uncertainty. This is the area where artificial intelligence (AI), utilizing computer vision and machine learning, could provide a benefit. ECG-specific AI models now aim squarely at this problem. The study we are reviewing today evaluated the Queen of Hearts (QoH) AI. It is a deep neural network trained to detect occlusive myocardial infarction (OMI) on 12-lead ECGs. The model is described as “91% accurate” in prior work and is undergoing FDA review as of March 24, 2025, but whether it outperforms practicing clinicians on the hardest cases (STEMI‑equivalents and mimics) remained unclear. ECG diagnostic accuracy is important in emergency medicine because misclassification cuts both ways. Missed OMI delays reperfusion, while overcalls send patients and teams to the cath lab unnecessarily, putting patients at risk and using up valuable resources. A diagnostic aid that catches true positive OMIs while reducing false activations could improve outcomes and team throughput. Clinical Question: Among EM physicians and cardiologists interpreting STEMI‑equivalent and STEMI‑mimic ECGs, how accurate are they compared with a machine‑learning ECG algorithm? Reference: Shroyer et al. Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by PMcardio). Am J Emerg Med. 2025 Nov. Population: 53 emergency physicians and 42 cardiologists from a community system. Intervention: Human interpretation and QoH AI algorithm classifying each ECG as OMI requiring immediate CLA vs not Comparison (Reference Standard): OMI Present: Angiographic culprit with ≤TIMI II flow and elevated troponin, or culprit with TIMI III flow and significantly elevated troponin. OMI Absent: No culprit ≥50% stenosis on angiography or, when no angiography, negative serial troponins, no new echo wall‑motion abnormality, and negative clinical follow-up Outcome: Diagnostic accuracy of ECG-based CLA decisions. CLA‑positive was defined a priori for STEMI/STEMI‑equivalents and for “rep