Literature DB >> 20720410

Detection of paroxysmal atrial fibrillation in acute stroke patients.

T Rizos1, C Rasch, E Jenetzky, C Hametner, S Kathoefer, R Reinhardt, T Hepp, W Hacke, R Veltkamp.   

Abstract

BACKGROUND: Atrial fibrillation (AF) is a frequent cause of stroke, but detecting paroxysmal AF (pAF) poses a challenge. We investigated whether continuous bedside ECG monitoring in a stroke unit detects pAF more sensitively than 24-hour Holter ECG, and tested whether examining RR interval dynamics on short-term ECG recordings using an automated screening algorithm (ASA) for pAF detection is a useful tool to predict the risk of pAF outside periods of manifest AF.
METHODS: Patients >60 years with acute ischemic stroke or transient ischemic attacks (TIA) were prospectively enrolled unless initial ECG revealed AF or they had a history of paroxysmal or persistent AF. ASA was performed on 1- to 2-hour ECG recordings in the emergency room and patients were classified into 5 risk categories for pAF. All patients underwent continuous bedside ECG monitoring for >48 h. Additionally, 24-hour Holter ECG was performed.
RESULTS: 136 patients were enrolled (median age: 72 years, male: 58.8%). In 29 (21.3%), pAF was newly diagnosed by continuous bedside ECG monitoring. pAF increased with age (p = 0.031). Median time to first pAF detection on continuous bedside ECG monitoring was 36 h. In 16 patients, pAF was detected by continuous bedside ECG monitoring prior to the performance of 24-hour Holter ECG. Thirteen of the remaining patients were pAF positive on continuous bedside ECG monitoring, but 24-hour Holter detected only 3 patients. Accordingly, the sensitivity of 24-hour Holter was 0.23. Sensitivity of higher-risk categories of ASA compared to continuous bedside ECG monitoring was 0.72, and specificity 0.63.
CONCLUSION: Continuous bedside ECG monitoring is more sensitive than 24-hour Holter ECG for pAF detection in acute stroke/TIA patients. Screening patients for pAF outside AF episodes using ASA requires further development.
Copyright © 2010 S. Karger AG, Basel.

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Year:  2010        PMID: 20720410     DOI: 10.1159/000316885

Source DB:  PubMed          Journal:  Cerebrovasc Dis        ISSN: 1015-9770            Impact factor:   2.762


  18 in total

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Authors:  Scott B Silverman
Journal:  Curr Treat Options Cardiovasc Med       Date:  2016-08

2.  Atrial fibrillation and prestroke cognitive impairment in stroke.

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3.  Multiple subcortical acute ischemic lesions reflect small vessel disease rather than cardiogenic embolism.

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Journal:  J Neurol       Date:  2012-02-17       Impact factor: 4.849

4.  [Detection of atrial fibrillation after stroke].

Authors:  U C Hoppe
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5.  The Associations of Atrial Fibrillation With the Risks of Incident Invasive Breast and Colorectal Cancer.

Authors:  Sylvia Wassertheil-Smoller; Aileen P McGinn; Lisa Martin; Beatriz L Rodriguez; Marcia L Stefanick; Marco Perez
Journal:  Am J Epidemiol       Date:  2017-03-01       Impact factor: 4.897

6.  Incidence of atrial fibrillation detected by implantable loop recorders in unexplained stroke.

Authors:  Paul E Cotter; Peter J Martin; Liam Ring; Elizabeth A Warburton; Mark Belham; Peter J Pugh
Journal:  Neurology       Date:  2013-03-27       Impact factor: 9.910

7.  Detection of atrial fibrillation after cryptogenic stroke.

Authors:  Richard A Bernstein
Journal:  Curr Treat Options Cardiovasc Med       Date:  2012-06

8.  Selective cognitive patterns resulting from bilateral hippocampal ischemia.

Authors:  David Cachia; Joan Swearer; Warren Ferguson; Majaz Moonis
Journal:  Arch Med Sci       Date:  2011-03-08       Impact factor: 3.318

9.  P-wave dispersion for predicting paroxysmal atrial fibrillation in acute ischemic stroke.

Authors:  Umuttan Dogan; Ebru Apaydin Dogan; Mehmet Tekinalp; Osman Serhat Tokgoz; Alpay Aribas; Hakan Akilli; Kurtulus Ozdemir; Hasan Gok; Betigul Yuruten
Journal:  Int J Med Sci       Date:  2011-12-17       Impact factor: 3.738

10.  Improved detection of paroxysmal atrial fibrillation utilizing a software-assisted electrocardiogram approach.

Authors:  Jürgen R Schaefer; Dieter Leussler; Ludger Rosin; David Pittrow; Thomas Hepp
Journal:  PLoS One       Date:  2014-02-28       Impact factor: 3.240

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