Evidence map›Paper›PMID 36680625›Full record

ArticleSleep & breathing = Schlaf & Atmung2023

Detecting atrial fibrillation in the polysomnography-derived electrocardiogram: a software validation study.

Julia van Kempen, Christian Glatz, Julian Wolfes, Gerrit Frommeyer, Matthias Boentert

Open access · hybridAbstract read
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 99% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Julia van KempenDepartment of Neurology with Institute of Translational Neurology, Münster University Hospital (UKM), Münster, Germany.
Christian GlatzDepartment of Neurology with Institute of Translational Neurology, Münster University Hospital (UKM), Münster, Germany.
Julian WolfesDepartment of Cardiology II - Electrophysiology, Münster University Hospital (UKM), Münster, Germany.
Gerrit Frommeyer *Department of Cardiology II - Electrophysiology, Münster University Hospital (UKM), Münster, Germany.
Matthias Boentert *Department of Neurology with Institute of Translational Neurology, Münster University Hospital (UKM), Münster, Germany. matthias.boentert@ukmuenster.de.ORCID 0000-0001-6133-1397
University Hospital Münster · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe present study validated a software-based electrocardiogram (ECG) analysis tool for detection of atrial fibrillation (AF) and risk for AF using polysomnography (PSG)-derived ECG recordings.

methodsThe Stroke Risk Analysis® (SRA®) software was applied to 3-channel ECG tracings from diagnostic PSG performed in enrolled subjects including a subgroup of subjects with previously documented AF. No subjects used positive airway pressure therapy. All ECG recordings were visually analyzed by a blinded cardiologist.

resultsOf subjects enrolled in the study, 93 had previously documented AF and 178 of 186 had an ECG that could be analyzed by either method. In subjects with known history of AF, automated analysis using SRA® classified 47 out of 87 ECG as either manifest AF or showing increased risk for paroxysmal AF (PAF) by SRA® (sensitivity 0.54, specificity 0.86). On visual analysis, 36/87 ECG showed manifest AF and 51/87 showed sinus rhythm. Among the latter subgroup, an increased risk for PAF was ascribed by SRA® in 11 cases (sensitivity 0.22, specificity 0.78) and by expert visual analysis in 5 cases (sensitivity 0.1, specificity 0.90). Among 36/178 ECG with manifest AF on visual analysis, 33 were correctly identified by the SRA® software (sensitivity and specificity 0.92).

conclusionSleep studies provide a valuable source of ECG recordings that can be easily subjected to software-based analysis in order to identify manifest AF and automatically assess the risk of PAF. For optimal evaluability of data, multiple channel ECG tracings are desirable. For assessment of PAF risk, the SRA® analysis probably excels visual analysis, but sensitivity of both methods is low, reflecting that repeated ECG recording remains essential.

Indexed as

Atrial FibrillationElectrocardiographyHumansPolysomnographySoftwareSoftware ValidationAtrial fibrillationAutomated ECG analysisElectrocardiogramPolysomnography

Identifiers

PMID36680625
PMCPMC10539451
OpenAlexW4317605896

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.