4.4 Article

Systematic analysis of ECG predictors of sinus rhythm maintenance after electrical cardioversion for persistent atrial fibrillation

期刊

HEART RHYTHM
卷 13, 期 5, 页码 1020-1027

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.hrthm.2016.01.004

关键词

Atrial fibrillation; Electrocardiography; Electrical cardioversion

资金

  1. European Network for Translational Research in Atrial Fibrillation [261057]
  2. Center for Translational Molecular Medicine (COHFAR)
  3. Netherlands Heart Foundation [CVON2014-09]
  4. European Union [633196]
  5. Netherlands Genomics Initiative [93612004]
  6. European Network for Translational Research in Atrial Fibrillation
  7. Centre for Translational Molecular Medicine (COHFAR)
  8. Netherlands Genomics Initiative (Preseed grant)
  9. Leducq Foundation (ENAFRA)
  10. Roche Diagnostics (Switzerland)
  11. Bayer Healthcare (Germany)
  12. Medtronic (United States)

向作者/读者索取更多资源

BACKGROUND Electrical cardioversion (ECV) is one of the rhythm control strategies in patients with persistent atrial fibrillation (AF). Unfortunately, recurrences of AF are common after ECV, which significantly limits the practical benefit of this treatment in patients with AF. OBJECTIVES The objectives of this study were to identify non-invasive complexity or frequency parameters obtained from the surface electrocardiogram (ECG) to predict sinus rhythm (SR) maintenance after ECV and to compare these ECG parameters with clinical predictors. METHODS We studied a wide variety of ECG-derived time- and frequency-domain AF complexity parameters in a prospective cohort of 502 patients with persistent AF referred for ECV. RESULTS During 1-year follow-up, 161 patients (32%) maintained SR. The best clinical predictor of SR maintenance was antiarrhythmic drug (AAD) treatment. A model including clinical parameters predicted SR maintenance with a mean cross-validated area under the receiver operating characteristic curve (AUC) of 0.62 +/- 0.05. The best single ECG parameter was the dominant frequency (DF) on lead V-6. Combining several ECG parameters predicted SR maintenance with a mean AUC of 0.64 +/- 0.06. Combining clinical and ECG parameters improved prediction to a mean AUC of 0.67 +/- 0.05. Although the DF was affected by AAD treatment, excluding patients taking AADs did not significantly lower the predictive performance captured by the ECG. CONCLUSION ECG-derived parameters predict SR maintenance during 1-year follow-up after ECV at least as good as known clinical predictors of rhythm outcome. The DF proved to be the most powerful ECG-derived predictor.

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