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Tytuł pozycji:

Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke.

Tytuł:
Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke.
Autorzy:
Gröschel S; Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Lange B; Department of Cardiology II, University Medical Center of the Johannes Gutenberg University, Mainz, Germany.
Wasser K; Department of Neurology, University Medicine Göttingen, Göttingen, Germany.
Hahn M; Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Wachter R; Clinic and Policlinic for Cardiology, University Hospital Leipzig, Leipzig, Germany.; Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany.; German Cardiovascular Research Center (DZHK), partner site Göttingen, Göttingen, Germany.
Gröschel K; Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Uphaus T; Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Źródło:
Annals of clinical and translational neurology [Ann Clin Transl Neurol] 2020 Oct; Vol. 7 (10), pp. 1779-1787. Date of Electronic Publication: 2020 Aug 30.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: [Hoboken, NJ] : Wiley Periodicals, Inc on behalf of American Neurological Association, [2014]-
MeSH Terms:
Electrocardiography, Ambulatory*/adverse effects
Atrial Fibrillation/*physiopathology
Brain Ischemia/*physiopathology
Stroke/*physiopathology
Aged ; Aged, 80 and over ; Atrial Fibrillation/diagnosis ; Brain Ischemia/complications ; Electrocardiography/methods ; Female ; Humans ; Male ; Medical History Taking ; Middle Aged ; Prospective Studies ; Risk Factors ; Stroke/diagnosis ; Time Factors
References:
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Entry Date(s):
Date Created: 20200831 Date Completed: 20210817 Latest Revision: 20210817
Update Code:
20240105
PubMed Central ID:
PMC7545589
DOI:
10.1002/acn3.51157
PMID:
32862499
Czasopismo naukowe
Objective: Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice.
Methods: In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into "no risk of AF" or "risk of AF" and compared to clinical variables to predict AF during 72 hours Holter-ECG.
Results: pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as "risk of AF" (n = 21, 17.8%) compared to "no risk of AF" (n = 33, 3.6%). AA-based risk stratification as "risk of AF" remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024-7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021-1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023-1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313-5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724-0.778; AUC for the combination: 0.789, 95% CI 0.763-0.814; difference between the AUC P = 0.022).
Interpretation: Automated software-based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
(© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)
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