Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals

• Published in: Annals of noninvasive electrocardiology Vol. 27; no. 1; pp. e12890 - n/a
• Main Authors: Kashou, Anthony H., LoCoco, Sarah, McGill, Trevon D., Evenson, Christopher M., Deshmukh, Abhishek J., Hodge, David O., Cooper, Daniel H., Sodhi, Sandeep S., Cuculich, Phillip S., Asirvatham, Samuel J., Noseworthy, Peter A., DeSimone, Christopher V., May, Adam M.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2022; John Wiley and Sons Inc; Wiley
• Subjects: Amplitudes; Automation; Cardiac arrhythmia; Cardiology; Derivation; Diagnosis, Differential; Differentiation; EKG; electrocardiogram; Electrocardiography; Heart; Humans; Laboratories; Logistic Models; Mathematical models; New Technologies; Patients; Prediction models; Regression analysis; Regression models; Software packages; supraventricular tachycardia; Synthesis; Tachycardia; Tachycardia, Supraventricular - diagnosis; Tachycardia, Ventricular - diagnosis; Ventricle; ventricular tachycardia; wide complex tachycardia; United States > US; Minnesota; electrocardiogram; wide complex tachycardia; supraventricular tachycardia; ventricular tachycardia
• ISSN: 1082-720X; 1542-474X
• DOI: 10.1111/anec.12890

Background Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. Methods A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one “all‐inclusive” model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. Results The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X‐lead QRS amplitude change, Y‐lead QRS amplitude change, and Z‐lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). Conclusion Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.