Time for a Diagnostic Paradigm Shift From STEMI/​NSTEMI to OMI/​NOMI (DIFOCCULT-3)

In a single‑centre retrospective study of 145 consecutive emergency‑department patients whose first ECG showed ≥1 mm ST‑elevation in lead aVR, investigators compared conventional electrocardiographic rules with a deep‑learning classifier (PMcardio “Queen of Hearts”) for recognising an acute coronary occlusion (ACO). Angiography and biomarker adjudication proved ACO in 19 patients (13 %). At an optimised probability threshold, the AI system achieved an area‑under‑the‑ROC curve of 0.918, detected 63 % of occlusions, and—crucially—generated no false‑positive calls in the 54‑patient rule‑out subgroup. By contrast, classic STEMI millimetre criteria identified only one in four occlusions and would have prompted between four and fourteen unnecessary emergency catheterisations.

In a one‑year, single‑centre cohort of 217 cath‑lab patients (72 STEMI, 145 NSTEMI), the AERO‑ACS study tested PMcardio’s AI ECG against traditional ST‑elevation rules for detecting angiographic occlusion‑MI. The algorithm matched STEMI sensitivity (86.5 % vs 83.3 %) while raising specificity to 82.2 % (vs 66.0 %), achieved 100 % sensitivity in STEMI cases, and flagged occlusions linked to a 12‑fold higher in‑hospital mortality risk—suggesting more accurate triage with fewer unnecessary activations.