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

IF 3 3区医学 Q1 MEDICINE, GENERAL & INTERNAL

Journal of Korean Medical Science Pub Date : 2025-03-31 DOI:10.3346/jkms.2025.40.e105

Jiesuck Park, Joonghee Kim, Soyeon Ahn, Youngjin Cho, Yeonyee E Yoon

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引用次数: 0

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%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 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.

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急性胸痛患者冠脉造影的AI-ECG支持决策：QCG-AID研究。

本初步研究评估了人工智能（AI）辅助心电图（ECG）分析系统，QCG，以增强急诊科（ED）急性胸痛的紧急冠状动脉造影（CAG）决策。我们回顾性分析了300例ED病例，分为非冠状动脉胸痛（1组）、无闭塞性冠状动脉疾病（CAD）的急性冠状动脉综合征（ACS）（2组）和闭塞性冠心病（ACS）（3组）。6名临床医生使用常规方法（临床资料和ECG）和QCG辅助方法（包括QCG评分）做出紧急CAG决策。qcg辅助方法提高了第2组（36.0% vs. 45.3%, P = 0.003）和第3组（85.3% vs. 90.0%, P = 0.017） CAG决策的正确性，第1组的影响最小（92.7% vs. 95.0%, P = 0.125）。在QCG辅助下，ACS的诊断准确率从77%提高到81%，单独使用QCG时达到82%，这支持了人工智能在ED胸痛病例的紧急CAG决策方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Korean Medical Science 医学-医学：内科

CiteScore

7.80

自引率

8.90%

发文量

320

审稿时长

3-6 weeks

期刊介绍： The Journal of Korean Medical Science (JKMS) is an international, peer-reviewed Open Access journal of medicine published weekly in English. The Journal’s publisher is the Korean Academy of Medical Sciences (KAMS), Korean Medical Association (KMA). JKMS aims to publish evidence-based, scientific research articles from various disciplines of the medical sciences. The Journal welcomes articles of general interest to medical researchers especially when they contain original information. Articles on the clinical evaluation of drugs and other therapies, epidemiologic studies of the general population, studies on pathogenic organisms and toxic materials, and the toxicities and adverse effects of therapeutics are welcome.