Algorithm for cavo-tricuspid isthmus flutter on surface ECGs: the ACTIONS study

• Authors: Daniel R Frisch, Thomas Jefferson University; Eitan Frankel, Thomas Jefferson University Hospital; Deanna Gill, Emory University Hospital; Jad Al Danaf, University of Utah Health
• Journal Title: Open Heart
• Volume: Volume 8, Number 1
• Publisher: BMJ Publishing Group | 2021-01-27
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1136/openhrt-2020-001431
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: electrocardiography; arrhythmias; cardiac; atrial flutter

Abstract

Objective Cavo-tricuspid isthmus atrial flutter (CTI-AFL) is an important arrhythmia to recognise because there is a highly effective and relatively low-risk ablation strategy. However, clinical experience has demonstrated that providers often have difficulty distinguishing AFL from atrial fibrillation. Methods We developed a novel ECG-based three-step algorithm to identify CTI-AFL based on established CTI flutter characteristics and verified on consecutive ablation cases of typical flutter, atypical flutter and atrial fibrillation. The algorithm assesses V1/inferior lead F-wave concordance, consistency of P-wave morphology and the presence of isoelectric intervals in the inferior leads. In this observation study, the algorithm was validated on a cohort of 50 second-year medical students. Students were paired in a control and experimental group, and each pair received 10 randomly selected ECGs (from a pool of 50 intracardiac electrogram-proven CTI-AFL and 50 AF or atypical AFL cases). The experimental group received a cover sheet with the CTI algorithm, and the control group received no additional guidance. Results There was a statistically significant difference in the mean number of correctly identified ECGs among the students in the experimental and control groups (8.12 vs 5.68, p<0.001). Students who used the algorithm correctly identified 2.44 more ECGs as being CTI-AFL or not CTI-AFL. Using the electrophysiology study as the gold standard, the algorithm had an accuracy of 81%, sensitivity of 81%, specificity of 82%, positive predictive value of 78% and negative predictive value of 84% in identifying CTI-AFL. Conclusion We developed a three-step ECG algorithm that provides a simple, sensitive, specific and accurate tool to identify CTI-AFL.