Specificity of the Occlusion Myocardial Infarction-Trained PMcardio "Queen of Hearts" AI-ECG in Inflammatory Myopericardial Syndromes.

BACKGROUND

Inflammatory myopericardial syndromes (IMPS)-including pericarditis, myocarditis, and overlap phenotypes-frequently present with chest pain and electrocardiographic abnormalities that can mimic occlusion myocardial infarction (OMI). High electrocardiographic specificity is essential to avoid unnecessary catheterization laboratory activation.

OBJECTIVES

To evaluate the ECG-level specificity of the OMI-trained PMcardio Queen of Hearts (QoH) AI-ECG model for OMI classification in patients with IMPS.

METHODS

We conducted a single-centre retrospective study including consecutive emergency department patients with adjudicated IMPS (2010-2025) at the University & Hospital of Fribourg. Obstructive coronary artery disease was excluded by coronary angiography or cardiac magnetic resonance imaging according to contemporary guidelines. Index ECGs were analysed using QoH. Specificity was compared with European Society of Cardiology (ESC) STEMI ECG criteria, the 4-variable formula (when applicable), and 3 large language models (LLMs; ChatGPT 5.2, Claude Opus 4.5, and Gemini 3). The primary endpoint was ECG-level specificity. Because only adjudicated IMPS cases were included, the analysis was designed to estimate ECG-level specificity rather than overall diagnostic accuracy.

RESULTS

Among 242 IMPS patients, QoH correctly classified 230 as non-OMI (specificity: 95.0%; 95% CI: 91.5-97.1). ESC STEMI ECG criteria showed 78.1% specificity, and the 4-variable formula demonstrated 73.3% specificity in anterior presentations (n = 30). All LLMs exhibited substantially lower specificity (26.9% to 70.2%; all P < 0.001 vs QoH). Discordant ESC-positive/QoH-negative cases (n = 43) predominantly showed diffuse, nonterritorial inflammatory ECG patterns. Specificity was higher in (myo)pericarditis than in (peri)myocarditis (97.7% vs 87.7%, P = 0.004).

CONCLUSIONS

In IMPS, the OMI-trained QoH model demonstrated high specificity and fewer false-positive OMI classifications than guideline ECG-STEMI criteria, ECG-based scores, and general-purpose LLMs.