用于基层医疗中基于人工智能解读心电图的PMcardio智能手机应用程序的诊断准确性（AMSTELHEART-1）。

Diagnostic accuracy of the PMcardio smartphone application for artificial intelligence-based interpretation of electrocardiograms in primary care (AMSTELHEART-1).

作者信息

Himmelreich Jelle C L, Harskamp Ralf E

机构信息

Department of General Practice, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands.

Personalized Medicine, Amsterdam Public Health, Amsterdam, Netherlands.

出版信息

Cardiovasc Digit Health J. 2023 Apr 5;4(3):80-90. doi: 10.1016/j.cvdhj.2023.03.002. eCollection 2023 Jun.

DOI:10.1016/j.cvdhj.2023.03.002

PMID:37351331

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10282008/

Abstract

BACKGROUND

The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG.

OBJECTIVE

To validate a smartphone application (PMcardio) as a stand-alone interpretation tool for 12-lead ECG in primary care.

METHODS

We recruited consecutive patients who underwent 12-lead ECG as part of routinely indicated primary care in the Netherlands. All ECGs were assessed by the PMcardio app, which analyzes a photographed image of 12-lead ECG for automated interpretation, installed on an Android platform (Samsung Galaxy M31) and an iOS platform (iPhone SE2020). We validated the PMcardio app for detecting any major ECG abnormality (MEA, primary outcome), defined as atrial fibrillation/flutter (AF), markers of (past) myocardial ischemia, or clinically relevant impulse and/or conduction abnormalities; or AF (key secondary outcome) with a blinded expert panel as reference standard.

RESULTS

We included 290 patients from 11 Dutch general practices with median age 67 (interquartile range 55-74) years; 48% were female. On reference ECG, 71 patients (25%) had MEA and 35 (12%) had AF. Sensitivity and specificity of PMcardio for MEA were 86% (95% CI: 76%-93%) and 92% (95% CI: 87%-95%), respectively. For AF, sensitivity and specificity were 97% (95% CI: 85%-100%) and 99% (95% CI: 97%-100%), respectively. Performance was comparable between Android and iOS platform (kappa = 0.95, 95% CI: 0.91-0.99 and kappa = 1.00, 95% CI: 1.00-1.00 for MEA and AF, respectively).

CONCLUSION

A smartphone app developed to interpret 12-lead ECGs was found to have good diagnostic accuracy in a primary care setting for major ECG abnormalities, and near-perfect properties for diagnosing AF.

摘要

背景

12导联心电图（ECG）在常规基层医疗中使用普遍，但经验不足的心电图解读人员可能难以充分解读心电图。

目的

验证一款智能手机应用程序（PMcardio）作为基层医疗中12导联心电图的独立解读工具。

方法

我们招募了在荷兰作为常规基层医疗一部分接受12导联心电图检查的连续患者。所有心电图均由安装在安卓平台（三星Galaxy M31）和iOS平台（iPhone SE2020）上的PMcardio应用程序进行评估，该应用程序通过分析12导联心电图的拍摄图像进行自动解读。我们以一个盲法专家小组作为参考标准，验证了PMcardio应用程序检测任何主要心电图异常（MEA，主要结局）的能力，主要心电图异常定义为心房颤动/扑动（AF）、（既往）心肌缺血标志物或临床相关的冲动和/或传导异常；以及以AF（关键次要结局）为指标的检测能力。

结果

我们纳入了来自荷兰11家全科诊所的290名患者，中位年龄67岁（四分位间距55 - 74岁）；48%为女性。在参考心电图上，71名患者（25%）有主要心电图异常，35名患者（12%）有AF。PMcardio检测主要心电图异常的敏感性和特异性分别为86%（95%CI：76% - 93%）和92%（95%CI：87% - 95%）。对于AF，敏感性和特异性分别为97%（95%CI：85% - 100%）和99%（95%CI：97% - 100%）。安卓和iOS平台之间的性能相当（主要心电图异常的kappa值 = 0.95，95%CI：0.91 - 0.99；AF的kappa值 = 1.00，95%CI：1.00 - 1.00）。