AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

Journal: Journal of Korean medical science
PMID: 40165577

Abstract

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, = 0.003) and Group 3 (85.3% vs. 90.0%, = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decision-making for ED chest pain cases.

Authors

  • Jiesuck Park
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea.
  • Joonghee Kim
    Department of Emergency Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea.
  • Soyeon Ahn
    Division of Statistics, Medical Research Collaborating Center, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do, The Republic of Korea.
  • Youngjin Cho
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam.
  • Yeonyee E Yoon
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).

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