Validation of a classification system to grade fractionation in atrial fibrillation and correlation with automated detection systems

Europace. 2009 Dec;11(12):1587-96. doi: 10.1093/europace/eup351. Epub 2009 Nov 6.

Abstract

Aims: We tested application of a grading system describing complex fractionated electrograms (CFE) in atrial fibrillation (AF) and used it to validate automated CFE detection (AUTO).

Methods and results: Ten seconds bipolar electrograms were classified by visual inspection (VI) during ablation of persistent AF and the result compared with offline manual measurement (MM) by a second blinded operator: Grade 1 uninterrupted fractionated activity (defined as segments > or =70 ms) for > or =70% of recording and uninterrupted > or =1 s; Grade 2 interrupted fractionated activity > or =70% of recording; Grade 3 intermittent fractionated activity 30-70%; Grade 4 discrete (<70 ms) complex electrogram (> or =5 direction changes); Grade 5 discrete simple electrograms (< or =4 direction changes); Grade 6 scar. Grade by VI and MM for 100 electrograms agreed in 89%. Five hundred electrograms were graded on Carto and NavX by VI to validate AUTO in (i) detection of CFE (grades 1-4 considered CFE), and (ii) assessing degree of fractionation by correlating grade and score by AUTO (data shown as sensitivity, specificity, r): NavX 'CFE mean' 92%, 91%, 0.56; Carto 'interval confidence level' using factory settings 89%, 62%, -0.72, and other published settings 80%, 74%, -0.65; Carto 'shortest confidence interval' 74%, 70%, 0.43; Carto 'average confidence interval' 86%, 66%, 0.53.

Conclusion: Grading CFE by VI is accurate and correlates with AUTO.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Atrial Fibrillation / classification*
  • Atrial Fibrillation / diagnosis*
  • Body Surface Potential Mapping / methods*
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography / methods*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Observer Variation
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Statistics as Topic