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移动单导联心电图技术在半农村非洲人群中进行房颤筛查的性能：来自“埃塞俄比亚之心：关注房颤”（TEFF-AF）研究的见解。

Performance of a Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Screening in a Semirural African Population: Insights From "The Heart of Ethiopia: Focus on Atrial Fibrillation" (TEFF-AF) Study.

机构信息

Centre for Heart Rhythm Disorders, The University of Adelaide, Adelaide, Australia.

Flinders University, Adelaide, Australia.

出版信息

JMIR Mhealth Uhealth. 2021 May 19;9(5):e24470. doi: 10.2196/24470.

DOI:10.2196/24470
PMID:34009129
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8173399/
Abstract

BACKGROUND

Atrial fibrillation (AF) screening using mobile single-lead electrocardiogram (ECG) devices has demonstrated variable sensitivity and specificity. However, limited data exists on the use of such devices in low-resource countries.

OBJECTIVE

The goal of the research was to evaluate the utility of the KardiaMobile device's (AliveCor Inc) automated algorithm for AF screening in a semirural Ethiopian population.

METHODS

Analysis was performed on 30-second single-lead ECG tracings obtained using the KardiaMobile device from 1500 TEFF-AF (The Heart of Ethiopia: Focus on Atrial Fibrillation) study participants. We evaluated the performance of the KardiaMobile automated algorithm against cardiologists' interpretations of 30-second single-lead ECG for AF screening.

RESULTS

A total of 1709 single-lead ECG tracings (including repeat tracing on 209 occasions) were analyzed from 1500 Ethiopians (63.53% [953/1500] male, mean age 35 [SD 13] years) who presented for AF screening. Initial successful rhythm decision (normal or possible AF) with one single-lead ECG tracing was lower with the KardiaMobile automated algorithm versus manual verification by cardiologists (1176/1500, 78.40%, vs 1455/1500, 97.00%; P<.001). Repeat single-lead ECG tracings in 209 individuals improved overall rhythm decision, but the KardiaMobile automated algorithm remained inferior (1301/1500, 86.73%, vs 1479/1500, 98.60%; P<.001). The key reasons underlying unsuccessful KardiaMobile automated rhythm determination include poor quality/noisy tracings (214/408, 52.45%), frequent ectopy (22/408, 5.39%), and tachycardia (>100 bpm; 167/408, 40.93%). The sensitivity and specificity of rhythm decision using KardiaMobile automated algorithm were 80.27% (1168/1455) and 82.22% (37/45), respectively.

CONCLUSIONS

The performance of the KardiaMobile automated algorithm was suboptimal when used for AF screening. However, the KardiaMobile single-lead ECG device remains an excellent AF screening tool with appropriate clinician input and repeat tracing.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12619001107112; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378057&isReview=true.

摘要

背景

使用移动单导联心电图（ECG）设备进行心房颤动（AF）筛查的敏感性和特异性存在差异。然而，在资源有限的国家中，此类设备的使用数据有限。

目的

本研究旨在评估 KardiaMobile 设备（AliveCor Inc）的自动算法在埃塞俄比亚半农村地区人群中用于 AF 筛查的效用。

方法

对 TEFF-AF（埃塞俄比亚之心：关注心房颤动）研究的 1500 名参与者使用 KardiaMobile 设备获得的 30 秒单导联 ECG 描记进行分析。我们评估了 KardiaMobile 自动算法对 KardiaMobile 自动算法对 30 秒单导联 ECG 进行 AF 筛查的cardiologists 解释的性能。

结果

从 1500 名接受 AF 筛查的埃塞俄比亚人（63.53%[953/1500]男性，平均年龄 35[SD 13]岁）中分析了总共 1709 个单导联 ECG 描记（包括 209 次重复描记）。使用 KardiaMobile 自动算法与 cardiologists 手动验证相比，单次单导联 ECG 描记的初始成功节律判断（正常或可能的 AF）较低（1176/1500，78.40%，vs 1455/1500，97.00%；P<.001）。在 209 名个体中重复单导联 ECG 描记改善了整体节律判断，但 KardiaMobile 自动算法仍较差（1301/1500，86.73%，vs 1479/1500，98.60%；P<.001）。导致 KardiaMobile 自动节律确定不成功的主要原因包括质量差/嘈杂的描记（214/408，52.45%）、频繁的异位（22/408，5.39%）和心动过速（>100 bpm；167/408，40.93%）。使用 KardiaMobile 自动算法进行节律判断的灵敏度和特异性分别为 80.27%（1168/1455）和 82.22%（37/45）。

结论

当用于 AF 筛查时，KardiaMobile 自动算法的性能不理想。然而，KardiaMobile 单导联 ECG 设备仍然是一种出色的 AF 筛查工具，可通过适当的临床医生输入和重复描记进行使用。

试验注册

澳大利亚和新西兰临床试验注册 ACTRN12619001107112；https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378057&isReview=true。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/71f3045a3703/mhealth_v9i5e24470_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/32e97c47682e/mhealth_v9i5e24470_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/fef644d085d1/mhealth_v9i5e24470_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/b4657d7c35f7/mhealth_v9i5e24470_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/dbf12defc3c7/mhealth_v9i5e24470_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/71f3045a3703/mhealth_v9i5e24470_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/32e97c47682e/mhealth_v9i5e24470_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/fef644d085d1/mhealth_v9i5e24470_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/b4657d7c35f7/mhealth_v9i5e24470_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/dbf12defc3c7/mhealth_v9i5e24470_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cce/8173399/71f3045a3703/mhealth_v9i5e24470_fig5.jpg

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