Literature DB >> 21605132

Noninvasive time and frequency predictors of long-standing atrial fibrillation early recurrence after electrical cardioversion.

Raúl Alcaraz1, Fernando Hornero, José J Rieta.   

Abstract

BACKGROUND: Several clinical factors have been studied to predict atrial fibrillation (AF) recurrence after electrical cardioversion (ECV) with limited predictive value.
METHODS: A method able to predict robustly long-standing AF early recurrence by characterizing noninvasively the electrical atrial activity (AA) with parameters related to its time course and spectral features is presented. To this respect, 63 patients (20 men and 43 women; mean age 73.4 ± 9.0 years; under antiarrhythmic drug treatment with amiodarone) who were referred for ECV of persistent AF were studied. During a 4-week follow-up, AF recurrence was observed in 41 patients (65.1%).
RESULTS: RR variability and the studied AA spectral features, including dominant atrial frequency (DAF), its first harmonic and their amplitude, provided poor statistical differences between groups. On the contrary, f waves power (fWP) and Sample Entropy (SampEn) of the AA behaved as very good predictors. Patients who relapsed to AF presented lower fWP (0.036 ± 0.019 vs 0.081 ± 0.029 n.u.(2) , P < 0.001) and higher SampEn (0.107 ± 0.022 vs 0.086 ± 0.033, P < 0.01). Furthermore, fWP presented the highest predictive accuracy of 82.5%, whereas SampEn provided a 79.4%. The remaining features revealed accuracies lower than 70%. A stepwise discriminant analysis (SDA) provided a model based on fWP and SampEn with 90.5% of accuracy.
CONCLUSIONS: The fWP has proved to predict long-standing AF early recurrence after ECV and can be combined with SampEn to improve its diagnostic ability. Furthermore, a thorough analysis of the results allowed outlining possible associations between these two features and the concomitant status of atrial remodeling. ©2011, The Authors. Journal compilation ©2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 21605132     DOI: 10.1111/j.1540-8159.2011.03125.x

Source DB:  PubMed          Journal:  Pacing Clin Electrophysiol        ISSN: 0147-8389            Impact factor:   1.976


  10 in total

1.  Predictors of successful cardioversion with vernakalant in patients with recent-onset atrial fibrillation.

Authors:  Natalia Mochalina; Tord Juhlin; Bertil Öhlin; Jonas Carlson; Fredrik Holmqvist; Pyotr G Platonov
Journal:  Ann Noninvasive Electrocardiol       Date:  2014-07-09       Impact factor: 1.468

2.  Autonomic influence on atrial fibrillatory process: head-up and head-down tilting.

Authors:  Sten Östenson; Valentina D A Corino; Jonas Carlsson; Pyotr G Platonov
Journal:  Ann Noninvasive Electrocardiol       Date:  2016-09-09       Impact factor: 1.468

3.  Spectral Analysis of Electrocardiograms in Patients with Inducible Atrial Fibrillation after Catheter Ablation Predicts Sinus Rhythm Maintenance.

Authors:  Stavros Stavrakis; John W Dyer; Benjamin J Scherlag; Zeeshan Khan; Paul Yeung; Jawad Chohan; Sunny S Po
Journal:  Ann Noninvasive Electrocardiol       Date:  2016-05-26       Impact factor: 1.468

4.  Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings.

Authors:  Raúl Alcaraz; José Joaquín Rieta
Journal:  Biomed Eng Online       Date:  2012-08-09       Impact factor: 2.819

5.  Application of Wavelet Entropy to predict atrial fibrillation progression from the surface ECG.

Authors:  Raúl Alcaraz; José J Rieta
Journal:  Comput Math Methods Med       Date:  2012-09-26       Impact factor: 2.238

6.  Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome.

Authors:  Marianna Meo; Thomas Pambrun; Nicolas Derval; Carole Dumas-Pomier; Stéphane Puyo; Josselin Duchâteau; Pierre Jaïs; Mélèze Hocini; Michel Haïssaguerre; Rémi Dubois
Journal:  Front Physiol       Date:  2018-07-17       Impact factor: 4.566

7.  Nurse-directed Preventative Management of Atrial Fibrillation: Is it Feasible?

Authors:  Mary E Huntsinger; Rahul N Doshi
Journal:  J Innov Card Rhythm Manag       Date:  2019-09-15

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

9.  A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

Authors:  Mina K Chung; Anant Madabhushi; Thomas Atta-Fosu; Michael LaBarbera; Soumya Ghose; Paul Schoenhagen; Walid Saliba; Patrick J Tchou; Bruce D Lindsay; Milind Y Desai; Deborah Kwon
Journal:  BMC Med Imaging       Date:  2021-03-09       Impact factor: 1.930

10.  Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results.

Authors:  Isaac L Goldenthal; Robert R Sciacca; Teresa Riga; Suzanne Bakken; Maurita Baumeister; Angelo B Biviano; Jose M Dizon; Daniel Wang; Ketty C Wang; William Whang; Kathleen T Hickey; Hasan Garan
Journal:  J Cardiovasc Electrophysiol       Date:  2019-09-25
  10 in total

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