College of Medicine and Public Health, Flinders University, Adelaide, Australia.
Division of Medicine, Cardiac and Critical Care, Flinders Medical Centre, Adelaide, Australia.
Ann Noninvasive Electrocardiol. 2024 Nov;29(6):e70025. doi: 10.1111/anec.70025.
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.
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.
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.
由于全球范围内急诊科就诊的房颤 (AF) 患者增多，因此对房颤终止的前瞻性预测成为一个日益重要的技术难题。能够无创性预测哪些房颤发作将终止，这对于围绕治疗和入院的临床决策具有重要意义，继而会对医院容量和房颤住院的经济成本产生影响。
于 2023 年 7 月 29 日在 MEDLINE、EMCare、CINAHL、CENTRAL 和 SCOPUS 上检索了使用标准体表心电图记录尝试预测房颤终止的文章。最终的综述纳入了 35 篇文章。信号处理技术可分为三大类，包括机器学习 (n=14)、熵分析 (n=12) 和时频/频率分析 (n=9)。所有研究均使用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) 使用了相同的心电图数据库，其中包括在 1 分钟内终止或持续超过 1 小时的记录。组间的准确性没有显著差异 (H(2)=0.058，p 值=0.971)。只有一项研究评估了早于终止前几分钟的记录，使用持续时间长达 174 秒的阵发性发作的中央 10 秒记录，准确率达到 92%。
没有研究试图实时前瞻性预测房颤终止，这为新的前瞻性验证研究提供了机会。利用源自数据库的心电图记录，多种信号处理技术已被证明可准确预测房颤终止。
