Validation of a Simple Electrocardiographic Algorithm for Detection of Ventricular Tachycardia

Background: The differential diagnosis of wide QRS complex tachycardia (WCT) between ventricular tachycardia (VT) or supraventricular tachycardia with aberrant conduction (SVT-A) is sometimes difficult in the emergency room. Objective: The aim of this study was to evaluate the accuracy of a new simple electrocardiographic algorithm to recognize VT in patients with wide complex tachycardia. Methods: The 12-lead electrocardiograms (ECG) for WCT were prospectively obtained from 120 patients during electrophysiological study. Six physicians with different expertise analyzed the electrocardiographic recordings, and made the diagnosis based on the D12V16 algorithm, that involves the analysis of the predominant polarity of QRS in leads I, II, V1 and V6. The diagnosis was compared with that made using the traditional Brugada algorithm and the gold-standard electrophysiological study. Statistical analyses were performed with a significance level of 5% (p<0.05). Results: According to the EPS study, 82 ECG recordings were VT and 38 SVT-A. Structural heart diseases were present in 71 (86.6%) patients with VT and in 8 (21.1%) with SVT-A. The Brugada algorithm had higher global sensitivity (87.2%), and the D12V16 algorithm had higher global specificity (85.1%) for VT. Both D12V16 and Brugada's algorithms presented a high positive predictive value (90.9% vs 85.8%, respectively) and similar accuracy (73.8% vs 81.4%, respectively) for the diagnosis of VT. Experienced evaluators were more accurate using Brugada algorithm than the D12V16 algorithm, but the accuracy of both algorithms was similar according to less experienced examiners. Conclusion: The simplified algorithm may be a useful method to recognize VT in the ECG, especially for less experienced doctors.

Automated summary

This study compared the accuracy of a new D12V16 algorithm with the traditional Brugada algorithm in diagnosing VT in patients with WCT. The D12V16 algorithm showed higher specificity, while the Brugada algorithm demonstrated higher sensitivity. Both algorithms had high positive predictive value and similar accuracy.