Literature DB >> 28017305

Predicting Unsuccessful Electrical Cardioversion for Acute Atrial Fibrillation (from the AF-CVS Score).

Samuli Jaakkola1, Gregory Y H Lip2, Fausto Biancari3, Ilpo Nuotio4, Juha E K Hartikainen5, Antti Ylitalo6, K E Juhani Airaksinen7.   

Abstract

Electrical cardioversion (ECV) is the standard treatment for acute atrial fibrillation (AF), but identification of patients with increased risk of ECV failure or early AF recurrence is of importance for rational clinical decision-making. The objective of this study was to derive and validate a clinical risk stratification tool for identifying patients at high risk for unsuccessful outcome after ECV for acute AF. Data on 2,868 patients undergoing 5,713 ECVs of acute AF in 3 Finnish hospitals from 2003 through 2010 (the FinCV study data) were included in the analysis. Patients from western (n = 3,716 cardioversions) and eastern (n = 1,997 cardioversions) hospital regions were used as derivation and validation datasets. The composite of cardioversion failure and recurrence of AF within 30 days after ECV was recorded. A clinical scoring system was created using logistic regression analyses with a repeated-measures model in the derivation data set. A multivariate analysis for prediction of the composite end point resulted in identification of 5 clinical variables for increased risk: Age (odds ratio [OR] 1.31, confidence interval [CI] 1.13 to 1.52), not the First AF (OR 1.55, CI 1.19 to 2.02), Cardiac failure (OR 1.52, CI 1.08 to 2.13), Vascular disease (OR 1.38, CI 1.11 to 1.71), and Short interval from previous AF episode (within 1 month before ECV, OR 2.31, CI 1.83 to 2.91) [hence, the acronym, AF-CVS]. The c-index for the AF-CVS score was 0.67 (95% CI 0.65 to 0.69) with Hosmer-Lemeshow p value 0.84. With high (>5) scores (i.e., 12% to 16% of the patients), the rate of composite end point was ∼40% in both cohorts, and among low-risk patients (score <3), the composite end point rate was ∼10%. In conclusion, the risk of ECV failure and early recurrence of AF can be predicted with simple patient and disease characteristics.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 28017305     DOI: 10.1016/j.amjcard.2016.11.026

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  12 in total

Review 1.  Inflammasomes and Proteostasis Novel Molecular Mechanisms Associated With Atrial Fibrillation.

Authors:  Na Li; Bianca J J M Brundel
Journal:  Circ Res       Date:  2020-06-18       Impact factor: 17.367

2.  Optimal timing for cardioversion in patients with atrial fibrillation.

Authors:  Tapio Hellman; Tuomas Kiviniemi; Ilpo Nuotio; Fausto Biancari; Tuija Vasankari; Juha Hartikainen; Mika Lehto; K E Airaksinen
Journal:  Clin Cardiol       Date:  2018-07-23       Impact factor: 2.882

Review 3.  A review of factors associated with maintenance of sinus rhythm after elective electrical cardioversion for atrial fibrillation.

Authors:  Veronika Ecker; Charles Knoery; Gordon Rushworth; Ian Rudd; Astrid Ortner; David Begley; Stephen J Leslie
Journal:  Clin Cardiol       Date:  2018-06-07       Impact factor: 2.882

Review 4.  How to Optimize Cardioversion of Atrial Fibrillation.

Authors:  K E Juhani Airaksinen
Journal:  J Clin Med       Date:  2022-06-12       Impact factor: 4.964

5.  Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Authors:  Nicklas Vinter; Anne Sofie Frederiksen; Andi Eie Albertsen; Gregory Y H Lip; Morten Fenger-Grøn; Ludovic Trinquart; Lars Frost; Dorthe Svenstrup Møller
Journal:  Open Heart       Date:  2020-06

6.  Clinical factors related to successful or unsuccessful cardioversion in the EdoxabaN versus warfarin in subjectS UndeRgoing cardiovErsion of Atrial Fibrillation (ENSURE-AF) randomized trial.

Authors:  Gregory Y H Lip; Jose L Merino; Maciej Banach; Naab Al-Saady; James Jin; Michael Melino; Shannon M Winters; Monika Kozieł; Andreas Goette
Journal:  J Arrhythm       Date:  2020-04-15

7.  Mitochondrial Dysfunction Underlies Cardiomyocyte Remodeling in Experimental and Clinical Atrial Fibrillation.

Authors:  Marit Wiersma; Denise M S van Marion; Rob C I Wüst; Riekelt H Houtkooper; Deli Zhang; Natasja M S de Groot; Robert H Henning; Bianca J J M Brundel
Journal:  Cells       Date:  2019-10-05       Impact factor: 6.600

8.  Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion.

Authors:  Eva María Cirugeda Roldan; Sofía Calero; Víctor Manuel Hidalgo; José Enero; José Joaquín Rieta; Raúl Alcaraz
Journal:  Entropy (Basel)       Date:  2020-07-07       Impact factor: 2.524

Review 9.  Clinical scores for outcomes of rhythm control or arrhythmia progression in patients with atrial fibrillation: a systematic review.

Authors:  Hai Deng; Ying Bai; Alena Shantsila; Laurent Fauchier; Tatjana S Potpara; Gregory Y H Lip
Journal:  Clin Res Cardiol       Date:  2017-05-30       Impact factor: 5.460

10.  CHA2DS2-VASc score predicts atrial fibrillation recurrence after cardioversion: Systematic review and individual patient pooled meta-analysis.

Authors:  Francesco Vitali; Matteo Serenelli; Juhani Airaksinen; Rita Pavasini; Anna Tomaszuk-Kazberuk; Elzbieta Mlodawska; Samuli Jaakkola; Cristina Balla; Lorenzo Falsetti; Nicola Tarquinio; Roberto Ferrari; Angelo Squeri; Gianluca Campo; Matteo Bertini
Journal:  Clin Cardiol       Date:  2019-02-11       Impact factor: 2.882

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