Vereckei criteria used as a diagnostic tool by emergency medicine residents to distinguish between ventricular tachycardia and supra-ventricular tachycardia with aberrancy.

BACKGROUND: Accurate electrocardiographic (ECG) differentiation of ventricular tachycardia (VT) from supraventricular tachycardia with aberrancy (SVT-A) on ECG is key to therapeutic decision-making in the emergency department (ED) setting. OBJECTIVE: The goal of this study was to test the accuracy and agreement of emergency medicine residents to differentiate VT from SVT-A using the Vereckei criteria. METHODS: Six emergency medicine residents volunteered to participate in the review of 114 ECGs from 86 patients with a diagnosis of either VT or SVT-A based on an electrophysiology study. The resident reviewers initially read 12-lead ECGs blinded to clinical information, and then one week later reviewed a subset of the same 12-lead ECGs unblinded to clinical information. RESULTS: One reviewer was excluded for failing to follow study protocol and one reviewer was excluded for reviewing less than 50 blinded ECGs. The remaining four reviewers each read 114 common ECGs blinded to clinical data and their diagnostic accuracy for VT was 74% (sensitivity 70%, specificity 80%), 75% (sensitivity 76%, specificity 73%), 61% (sensitivity 81%, specificity 25%), and 68% (sensitivity 84%, specificity 40%). The intraclass correlation coefficient (ICC) was 0.31 (95% CI 0.22-0.42). Eliminating two of the four reviewers who left a disproportionately high number of ECGs unclassified resulted in an increase in overall mean diagnostic accuracy (70-74%) and agreement (0.31-0.50) in the two remaining reviewers. Three reviewers read 45 common ECGs unblinded to clinical information and had accuracies for VT 93%, 93% and 78%. CONCLUSION: The new single lead Vereckei criteria, when applied by emergency medicine residents achieved only fair-to-good individual accuracy and moderate agreement. The addition of clinical information resulted in substantial improvement in test characteristics. Further improvements (accuracy and simplification) of algorithms for differentiating VT from SVT-A would be helpful prior to clinical implementation.

Duke Scholars

Author Christopher Lindsell Biostatistics & Bioinformatics, Division of Biostatistics