Rethinking diagnostic pathways in acute coronary syndromes: insights from misclassified occlusion MI

Introduction Occlusion Myocardial Infarction (OMI) represents an evolving diagnostic framework in Acute Coronary Syndromes (ACS), challenging the conventional STEMI-based paradigm. It emphasizes identifying acute coronary occlusions based on subtle or atypical ECG findings, ensuring timely revascularization for patients at risk of being overlooked. This study aimed to characterize patients presenting with ACS who were initially labelled as NSTEMI but ultimately fulfilled OMI criteria, exploring the clinical implications of this reclassification. Methods We retrospectively analyzed 330 ACS patients admitted over one year. Patients were first categorized as STEMI or NSTEMI/unstable angina and then reclassified according to OMI criteria: TIMI flow ≤2 and/or markedly elevated troponin (Troponin T >1000 ng/L or Troponin I >5000 ng/L) associated with regional wall-motion abnormalities. Three subgroups were defined — STEMI, NSTEMI-OMI, and NSTEMI-non-OMI — and compared regarding demographics, comorbidities, and ECG features (bundle branch block [BBB], pacemaker rhythm, left ventricular hypertrophy). Results Among 330 ACS patients, 196 (59%) were classified as STEMI and 134 (41%) as NSTEMI/UA. Reclassification identified 52 patients (39%) with NSTEMI-OMI. Compared with STEMI, these patients more frequently presented with BBB or pacemaker rhythm (23.1% vs. 6.1%; p < 0.001) and had a higher prevalence of previous coronary artery disease (30.8% vs. 11.2%; p = 0.002). No significant differences were found in age, comorbidities, or other ECG variables. Similarly, no major differences were observed between NSTEMI-OMI and NSTEMI-non-OMI groups. Discussion The presence of conduction abnormalities (BBB or pacing) and prior coronary disease may conceal acute occlusion when using traditional STEMI criteria, resulting in underdiagnosis. This limitation highlights the potential of the OMI paradigm to improve early detection and management by focusing on the presence of occlusion rather than ECG morphology. Emerging tools such as AI-driven ECG interpretation could further enhance diagnostic precision and address current limitations. Conclusion Adopting the OMI paradigm may refine the detection and management of coronary occlusions, particularly in patients with baseline ECG alterations or prior coronary disease. Broader validation and standardization are essential to ensure consistent clinical application.