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Brief episodes of rapid irregular atrial activity (micro-AF) are a risk marker for atrial fibrillation: a prospective cohort study.

ABSTRACT: BACKGROUND:Short supraventricular tachycardias with atrial fibrillation (AF) characteristics are associated with an increased risk of developing AF over time. The aim of this study is to determine if presence of very short-lasting episodes of AF-like activity (micro-AF) can also be used as a marker of undiagnosed silent atrial fibrillation. METHODS:In the STROKESTOP II study, a Swedish mass screening study for AF among 75- and 76-year-olds, participants with NT-proBNP ?125?ng/L performed intermittent ECG recordings 30?s, four times daily for 2 weeks. Participants with micro-AF (sudden onset of irregular tachycardia with episodes of ?5 consecutive supraventricular beats and total absence of p-waves, lasting less than 30?s) were invited to undergo extended AF screening using continuous event recording for 2 weeks. A control group of individuals without micro-AF was examined using the same ECG modalities. RESULTS:Out of 3763 participants in STROKESTOP II who had elevated NT-proBNP levels and were free of AF, n?=?221 (6%) had micro-AF. The majority of participants with micro-AF (n?=?196) accepted further investigation with continuous ECG monitoring which showed presence of AF in 26 of them. In the control group (n?=?250), continuous monitoring detected 7 new AF cases. Thus, AF was significantly more common in the micro AF group (13%) compared to the control group (3%), p?

SUBMITTER: Fredriksson T

PROVIDER: S-EPMC7149874 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Brief episodes of rapid irregular atrial activity (micro-AF) are a risk marker for atrial fibrillation: a prospective cohort study.

Fredriksson Tove T Gudmundsdottir Katrin Kemp KK Frykman Viveka V Friberg Leif L Al-Khalili Faris F Engdahl Johan J Svennberg Emma E

BMC cardiovascular disorders 20200410 1

<h4>Background</h4>Short supraventricular tachycardias with atrial fibrillation (AF) characteristics are associated with an increased risk of developing AF over time. The aim of this study is to determine if presence of very short-lasting episodes of AF-like activity (micro-AF) can also be used as a marker of undiagnosed silent atrial fibrillation.<h4>Methods</h4>In the STROKESTOP II study, a Swedish mass screening study for AF among 75- and 76-year-olds, participants with NT-proBNP ≥125 ng/L pe ...[more]

PMID: 32276611

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Project description:BackgroundScreening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden.MethodsWe calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale-MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases.ResultsIrregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method.ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.

Dataset Information

Brief episodes of rapid irregular atrial activity (micro-AF) are a risk marker for atrial fibrillation: a prospective cohort study.

Publications

Brief episodes of rapid irregular atrial activity (micro-AF) are a risk marker for atrial fibrillation: a prospective cohort study.

Similar Datasets

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