Predicting Spontaneous Termination of Atrial Fibrillation Based on Analysis of Standard Electrocardiograms: A Systematic Review

IF 1.1 4区医学 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS

Annals of Noninvasive Electrocardiology Pub Date : 2024-10-25 DOI:10.1111/anec.70025

Brandon Wadforth, Jing Soong Goh, Kathryn Tiver, Sobhan Salari Shahrbabaki, Ivaylo Tonchev, Dhani Dharmaprani, Anand N. Ganesan

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引用次数: 0

Abstract

Background

Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non-invasively predict which AF episodes will terminate has important implications in terms of clinical decision-making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations.

Methods and Results

MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p-value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174.

Conclusions

No studies attempted to forward predict AF termination in real-time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively.

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基于标准心电图分析预测心房颤动的自发终止：系统回顾

背景：由于全球急诊室心房颤动就诊人数不断增加，房颤终止的前瞻性预测是一个具有挑战性的技术问题，其重要性与日俱增。无创预测心房颤动发作将终止的能力对围绕治疗和入院的临床决策具有重要意义，进而影响医院的收治能力和心房颤动住院的经济成本：于 2023 年 7 月 29 日在 MEDLINE、EMCare、CINAHL、CENTRAL 和 SCOPUS 上检索了试图使用标准表面心电图记录预测房颤终止的文章。最终共检索到 35 篇文章。信号处理技术分为三大类，包括机器学习（14 篇）、熵分析（12 篇）和时频/频率分析（9 篇）。所有研究都使用了回顾性处理的心电图数据，没有前瞻性验证研究。大多数研究（n = 33）使用相同的心电图数据库，其中包括在 1 分钟内终止或持续 1 小时以上的记录。各组之间的准确性无显著差异（H(2) = 0.058，p 值 = 0.971）。只有一项研究对终止前几分钟之前的记录进行了评估，使用持续时间长达 174.结论的阵发性发作的 10 秒中心时间，准确率达到 92%：没有研究尝试实时预测房颤终止，这为新的前瞻性验证研究提供了机会。事实证明，多种信号处理技术可利用数据库中的心电图记录准确预测房颤终止。

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

Annals of Noninvasive Electrocardiology 医学-心血管系统

CiteScore

3.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The ANNALS OF NONINVASIVE ELECTROCARDIOLOGY (A.N.E) is an online only journal that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients. ANE is the first journal in an evolving subspecialty that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients. The publication includes topics related to 12-lead, exercise and high-resolution electrocardiography, arrhythmias, ischemia, repolarization phenomena, heart rate variability, circadian rhythms, bioengineering technology, signal-averaged ECGs, T-wave alternans and automatic external defibrillation. ANE publishes peer-reviewed articles of interest to clinicians and researchers in the field of noninvasive electrocardiology. Original research, clinical studies, state-of-the-art reviews, case reports, technical notes, and letters to the editors will be published to meet future demands in this field.