Feasibility of Artificial Intelligence-Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study.

Q2 Medicine
JMIR Cardio Pub Date : 2023-05-02 DOI:10.2196/44791
Jiesuck Park, Yeonyee Yoon, Youngjin Cho, Joonghee Kim
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引用次数: 0

Abstract

Background: Despite accumulating research on artificial intelligence-based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated.

Objective: We evaluated the utility of an existing artificial intelligence-based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina.

Methods: This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)-related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score.

Results: Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001).

Conclusions: QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible.

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基于人工智能的心电图分析预测稳定型心绞痛患者阻塞性冠状动脉疾病的可行性:验证研究。
背景:尽管基于人工智能的心电图(ECG)算法用于预测急性冠脉综合征(ACS)的研究越来越多,但其在稳定型心绞痛中的应用尚未得到很好的评估。目的:我们评估现有的基于人工智能的定量心电图(QCG)分析仪在稳定型心绞痛中的应用,并开发一种更适合稳定型心绞痛的新的ECG生物标志物。方法:该单中心研究纳入了连续的稳定型心绞痛患者。探讨有创血管造影证实的阻塞性冠心病(CAD)相关疾病(ACS、心肌损伤、危重状态、st段抬高型心肌梗死、左心室功能障碍)QCG评分预测的独立值和增量值。此外,将QCG分析仪提取的心电信号作为输入,形成新的QCG评分。结果:723例稳定型心绞痛患者(中位年龄68岁;男性:470/723(65%),497(69%)患有阻塞性CAD。ACS和心肌损伤的QCG评分与阻塞性CAD独立相关(比值比[OR]为1.09,95% CI为1.03-1.17,OR为1.08,95% CI为1.02-1.16,每增加10分),但与临床特征相比,预测效果没有显著提高。然而,我们的新QCG评分对阻塞性CAD的预测效果(受试者工作特征曲线下面积0.802)优于原始QCG评分,并且结合临床特征(受试者工作特征曲线下面积0.827 vs 0.730;结论:用于急性病情的QCG评分在识别稳定型心绞痛的阻塞性CAD方面表现有限。然而,通过对稳定型心绞痛患者的综合心电图信号进行培训,对QCG分析仪进行改进是可行的。
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
JMIR Cardio
JMIR Cardio Computer Science-Computer Science Applications
CiteScore
3.50
自引率
0.00%
发文量
25
审稿时长
12 weeks
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