American Heart Journal
Volume 158, Issue 4 , Pages 622-628, October 2009

Electrocardiographic predictors of atrial fibrillation

  • Marco V. Perez, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
    • Corresponding Author InformationReprint requests: Marco V. Perez, MD, Falk CVRC, 300 Pasteur Drive, Stanford, CA 94305-5406.
  • ,
  • Frederick E. Dewey, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
  • ,
  • Rachel Marcus, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
  • ,
  • Euan A. Ashley, MRCP, DPhil, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
  • ,
  • Amin A. Al-Ahmad, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
  • ,
  • Paul J. Wang, MD

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
  • ,
  • Victor F. Froelicher, MD, FACC

      Affiliations

    • Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
    • Cardiovascular Medicine, Palo Alto Veterans Affairs Health Care System, Stanford, CA

Received 26 May 2009; accepted 6 August 2009.

Background

Atrial fibrillation (AF) is the most prevalent arrhythmia in the United States and accounts for more than 750,000 strokes per year. Noninvasive predictors of AF may help identify patients at risk of developing AF. Our objective was to identify the electrocardiographic characteristics associated with onset of AF.

Methods

This was a retrospective cohort analysis of 42,751 patients with electrocardiograms (ECGs) ordered by physician's discretion and analyzed using a computerized system. The population was followed for detection of AF on subsequent ECGs. Cox proportional hazard regression analysis was performed to test the association between these ECG characteristics and development of AF.

Results

For a mean follow-up of 5.3 years, 1,050 (2.4%) patients were found to have AF on subsequent ECG recordings. Several ECG characteristics, such as P-wave dispersion (the difference between the widest and narrowest P waves), premature atrial contractions, and an abnormal P axis, were predictive of AF with hazard ratio of approximately 2 after correcting for age and sex. P-wave index, the SD of P-wave duration across all leads, was one of the strongest predictors of AF with a concordance index of 0.62 and a hazard ratio of 2.7 (95% CI 2.1-3.3) for a P-wave index >35. These were among the several independently predictive markers identified on multivariate analysis.

Conclusions

Several ECG markers are independently predictive of future onset of AF. The P index, a measurement of disorganized atrial depolarization, is one of the strongest predictors of AF. The ECG contains valuable prognostic information that can identify patients at risk of AF.

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PII: S0002-8703(09)00612-7

doi:10.1016/j.ahj.2009.08.002

American Heart Journal
Volume 158, Issue 4 , Pages 622-628, October 2009