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Use of aVR and Jastrzębski algorithms in the classification of wide complex tachycardia in dogs – a preliminary study

Open Access
|Oct 2018

Abstract

Introduction

Ventricular rhythm disturbances are a common pathology in human and veterinary medicine. In humans, the algorithmic approach is used to differentiate wide QRS complex tachycardia. The most commonly used are the aVR and Brugada algorithms as well as the ventricular tachycardia (VT) score developed by Jastrzębski and coworkers. In veterinary medicine, no such algorithms are available and the only parameter used to describe VT abnormalities is the duration of the QRS complexes. The aim of this analysis was determining whether human medicine algorithms for VT are applicable in veterinary medicine to differentiate wide QRS complex tachycardia in dogs.

Material and Methods

A retrospective analysis was performed on 11 dogs of both sexes and various breeds and age diagnosed with VT. The diagnosis was based on ambulatory ECG, further established based on the reaction to lidocaine or adenosine or an invasive electrophysiological study.

Results

Of the 11 tracings passed through the aVR algorithm, 10 met the VT criteria. The most common criterion was the Vi/Vt ratio (8 out of 11 tracings). Based on the VT score, seven out of eight dogs had a high probability of VT.

Conclusion

Retrospective analysis of ECGs by aVR and VT score indicates that the applied algorithms may be useful in differentiating wide QRS complex tachycardia as a quick, easy, and non-invasive alternative to cardiac electrophysiology.

Language: English
Page range: 365 - 369
Submitted on: Apr 29, 2018
Accepted on: Aug 24, 2018
Published on: Oct 23, 2018
Published by: National Veterinary Research Institute in Pulawy
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Marcin Michałek, Piotr Frydrychowski, Jakub Adamowicz, Agnieszka Sławuta, Urszula Pasławska, Agnieszka Noszczyk-Nowak, published by National Veterinary Research Institute in Pulawy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.