Samuli Jaakkola, Gregory Y.H. Lip, Fausto Biancari, Ilpo Nuotio, Juha E.K. Hartikainen, Antti Ylitalo, K.E. Juhani Airaksinen, Predicting Unsuccessful Electrical Cardioversion for Acute Atrial Fibrillation (from the AF-CVS Score), The American Journal of Cardiology, Volume 119, Issue 5, 2017, Pages 749-752, ISSN 0002-9149, https://doi.org/10.1016/j.amjcard.2016.11.026
Predicting unsuccessful electrical cardioversion for acute atrial fibrillation (from the AF-CVS score)
|Author:||Jaakkola, Samuli1; Lip, Gregory Y. H.2,3; Biancari, Fausto4;|
1Heart Center, Turku University Hospital and University of Turku, Turku, Finland
2University of Birmingham Institute of Cardiovascular Sciences, City Hospital, Birmingham, United Kingdom
3Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
4Department of Surgery, Oulu University Hospital, Oulu, Finland
5Department of Acute Internal Medicine, Turku University Hospital and University of Turku, Turku, Finland
6Heart Center, Kuopio University Hospital, Kuopio, Finland
7Heart Center, Satakunta Central Hospital, Pori, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019101532714
|Publish Date:|| 2019-10-15
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.
The American journal of cardiology
|Pages:||749 - 752|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
Dr. Lip is a consultant for Bayer/Janssen, Astellas, Merck, Sanofi, BMS/Pfizer, Biotronik, Medtronic, Portola, Boehringer Ingelheim, Microlife, and Daiichi-Sankyo; speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Microlife, Roche, and Daiichi-Sankyo. Dr. Hartikainen received research grants from the Finnish Foundation for Cardiovascular Research and the European Union Seventh Framework Program; provides lectures for Cardiome, St Jude Medical, and Biotronic; and is a member in the advisory boards for Astra Zeneca, Amgen, and Bayer. Dr. Airaksinen received research grants from the Finnish Foundation for Cardiovascular Research; provides lectures for Bayer, Cardiome, and Boehringer Ingelheim; and is a member in the advisory boards for Bayer, Astra Zeneca, and Boston Scientific.
© 2016 Elsevier Inc. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.