A novel diagnostic algorithm for the identification of different supraventircular tachycardias

Abstract Background Supraventricular arrhythmias represent a diagnostic challenge. Its prevalence and causes are not well established, given the impossibility to differentiate between all different types of supraventricular tachycardias (SVT). There are several supraventricular arrhythmias, but we focus on: 1) Atrial tachycardia (AT) 2) Junctional tachycardia (JT) 3) Atrial fibrillation (AF) 4) Atrial flutter (AA) 5) Atrioventricular nodal reentrant tachycardia (AVNRT), and 6) Atrioventricular reentrant tachycardia (AVRT). The electrocardiographic diagnosis is based on the presence of P-waves, its morphology and relationship with the QRS complex, and the relationship between the atrial and ventricular frequency. Purpose The purpose of this study was to create a helpful clinical tool that could serve the physician as a guide to determine a diagnosis and initial treatment. Additionally, we wanted to establish the sensitivity and specificity of the algorithm. Methods It is a diagnostic test study. We include 190 electrocardiograms of different SVT of patients undergoing electrophysiological studies. The data consists of 760 observations from two different readings of the electrocardiograms. Results 104 of 112 AF, were correctly identified using the algorithm, with a sensitivity and specificity of 92.9% and 99.1%, respectively (95% CI: 0.86–0.96). 76 of 760 were AA, and 62 were correctly diagnosed, with a sensitivity and specificity of 81.6% and 95.5%, respectively (95% CI 0.71–0.88). 50 of the 72 AT were correctly classified, with a sensitivity of 69.4% and specificity of 97.4% (95% CI 0.58–0.78). 99 of 152 AVNRT were identified with a sensitivity and specificity of 64.5% and 87%, respectively (95% CI 0.84–0.89). 254 of 344 AVRT were diagnosed correctly with a sensitivity of 73.8% and specificity of 88.2% (95% CI 0.68–0.78). Finally, 1 of 4 JT were identified, with a sensitivity and specificity of 25% and 99.1% respectively (95% CI 0.04–0.69). Conclusion The algorithm is an excellent diagnostic tool to identify atrial flutter, atrial fibrillation and atrioventricular reentrant tachycardia. SVT algorithm Funding Acknowledgement Type of funding source: None