Literature DB >> 20881756

Accuracy of diagnosing atrial flutter and atrial fibrillation from a surface electrocardiogram by hospital physicians: analysis of data from internal medicine departments.

Arthur Shiyovich1, Arik Wolak, Lital Yacobovich, Aviva Grosbard, Amos Katz.   

Abstract

INTRODUCTION: Atrial fibrillation (AF) and atrial flutter (AFL) are clinically and electrocardiographically similar. However, considering significant therapeutic differences, differentiation of these 2 arrhythmias is essential. Our aims were to evaluate the misdiagnosis rate among electrocardiograms (ECGs) interpreted as AF or AFL by internists and to describe the factors that could be responsible for the misinterpretation.
METHODS: We evaluated patients discharged with a diagnosis of AF or AFL from internal medicine wards of a tertiary referral center. The reanalysis of the ECGs was performed by 2 senior cardiologists (1 electrophysiologist), blinded to the primary analysis and patient's clinical data.
RESULTS: The ECGs of 44 of 268 (16%) patients were misinterpreted and consisted of: 25 (57%) AFL, 5 (11%) SVT, 7 (16%) sinus rhythm with premature atrial beats and 7 (16%) AF. The baseline diagnosis was correct in 212 of 246 (86%) for AF and 12 of 22 (55%) for AFL, P < 0.001. A significantly higher rate of AFL was misdiagnosed compared with AF [25 of 37 (68%) versus 7 of 219 (3%), respectively; P < 0.001], higher in atypical than typical AFL [16 of 20 (80%) versus 9 of 17 (53%), respectively; P = 0.07]. Reduced quality ECGs was found more often among the incorrectly than the correctly diagnosed ECGs (P < 0.001].
CONCLUSIONS: ECGs, interpreted as AF or AFL by internists, are often misdiagnosed. AFL was misdiagnosed more often than AF, with atypical more often than typical AFL. Consulting with a cardiologist and applying diagnostic criteria may reduce misdiagnosis.

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Year:  2010        PMID: 20881756     DOI: 10.1097/MAJ.0b013e3181e73fcf

Source DB:  PubMed          Journal:  Am J Med Sci        ISSN: 0002-9629            Impact factor:   2.378


  5 in total

1.  Impact of a 4q25 genetic variant in atrial flutter and on the risk of atrial fibrillation after cavotricuspid isthmus ablation.

Authors:  Jason D Roberts; Jonathan C Hsu; Bradley E Aouizerat; Clive R Pullinger; Mary J Malloy; John P Kane; Jeffrey E Olgin; Gregory M Marcus
Journal:  J Cardiovasc Electrophysiol       Date:  2013-12-13

2.  An Efficient and Automatic ECG Arrhythmia Diagnosis System using DWT and HOS Features and Entropy- Based Feature Selection Procedure.

Authors:  Abdullah Jafari Chashmi; Mehdi Chehel Amirani
Journal:  J Electr Bioimpedance       Date:  2019-08-20

3.  Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.

Authors:  Alvaro Alonso; Bouwe P Krijthe; Thor Aspelund; Katherine A Stepas; Michael J Pencina; Carlee B Moser; Moritz F Sinner; Nona Sotoodehnia; João D Fontes; A Cecile J W Janssens; Richard A Kronmal; Jared W Magnani; Jacqueline C Witteman; Alanna M Chamberlain; Steven A Lubitz; Renate B Schnabel; Sunil K Agarwal; David D McManus; Patrick T Ellinor; Martin G Larson; Gregory L Burke; Lenore J Launer; Albert Hofman; Daniel Levy; John S Gottdiener; Stefan Kääb; David Couper; Tamara B Harris; Elsayed Z Soliman; Bruno H C Stricker; Vilmundur Gudnason; Susan R Heckbert; Emelia J Benjamin
Journal:  J Am Heart Assoc       Date:  2013-03-18       Impact factor: 5.501

4.  Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.

Authors:  Tsai-Min Chen; Chih-Han Huang; Edward S C Shih; Yu-Feng Hu; Ming-Jing Hwang
Journal:  iScience       Date:  2020-02-04

5.  Expert-enhanced machine learning for cardiac arrhythmia classification.

Authors:  Sebastian Sager; Felix Bernhardt; Florian Kehrle; Maximilian Merkert; Andreas Potschka; Benjamin Meder; Hugo Katus; Eberhard Scholz
Journal:  PLoS One       Date:  2021-12-23       Impact factor: 3.240

  5 in total

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