Anthony H Kashou1, Christopher V DeSimone2, Abhishek J Deshmukh2, Trevon D McGill1, David O Hodge3, Rickey Carter3, Daniel H Cooper4, Phillip S Cuculich4, Amit Noheria4, Samuel J Asirvatham2, Peter A Noseworthy2, Adam M May5. 1. Department of Medicine, Mayo Clinic, United States of America. 2. Department of Cardiovascular Diseases, Mayo Clinic, United States of America. 3. Department of Health Sciences Research, Mayo Clinic, United States of America. 4. Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, United States of America. 5. Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, United States of America. Electronic address: may.adam@wustl.edu.
Abstract
BACKGROUND: Differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional manually-operated electrocardiogram (ECG) interpretation methods is difficult. Recent research has shown that accurate WCT differentiation may be accomplished by automated approaches (e.g., WCT Formula) implemented by computerized ECG interpretation software. OBJECTIVE: We sought to develop a new automated means to differentiate WCTs. METHODS: First, a derivation cohort of paired WCT and baseline ECGs was examined to secure independent VT predictors to be incorporated into a logistic regression model (i.e., WCT Formula II). Second, the WCT Formula II was trialed against a separate validation cohort of paired WCT and baseline ECGs. RESULTS: The derivation cohort comprised 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. The WCT Formula II was composed of baseline QRS duration (p = 0.02), WCT QRS duration (p < 0.001), frontal percent time-voltage area change (p < 0.001), and horizontal percent time-voltage area change (p < 0.001). The area under the curve (AUC) for VT and SWCT differentiation was 0.96 (95% CI 0.94-0.98) for the derivation cohort. The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. WCT Formula II implementation on the validation cohort yielded effective WCT differentiation (AUC 0.96; 95% CI 0.94-0.98). CONCLUSION: The WCT Formula II is an example of how contemporary ECG interpretation software could be used to differentiate WCTs successfully.
BACKGROUND: Differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional manually-operated electrocardiogram (ECG) interpretation methods is difficult. Recent research has shown that accurate WCT differentiation may be accomplished by automated approaches (e.g., WCT Formula) implemented by computerized ECG interpretation software. OBJECTIVE: We sought to develop a new automated means to differentiate WCTs. METHODS: First, a derivation cohort of paired WCT and baseline ECGs was examined to secure independent VT predictors to be incorporated into a logistic regression model (i.e., WCT Formula II). Second, the WCT Formula II was trialed against a separate validation cohort of paired WCT and baseline ECGs. RESULTS: The derivation cohort comprised 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. The WCT Formula II was composed of baseline QRS duration (p = 0.02), WCT QRS duration (p < 0.001), frontal percent time-voltage area change (p < 0.001), and horizontal percent time-voltage area change (p < 0.001). The area under the curve (AUC) for VT and SWCT differentiation was 0.96 (95% CI 0.94-0.98) for the derivation cohort. The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. WCT Formula II implementation on the validation cohort yielded effective WCT differentiation (AUC 0.96; 95% CI 0.94-0.98). CONCLUSION: The WCT Formula II is an example of how contemporary ECG interpretation software could be used to differentiate WCTs successfully.
Authors: Anthony H Kashou; Christopher M Evenson; Peter A Noseworthy; Thoddi R Muralidharan; Christopher V DeSimone; Abhishek J Deshmukh; Samuel J Asirvatham; Adam M May Journal: Indian Heart J Date: 2020-09-23
Authors: Anthony H Kashou; Sarah LoCoco; Trevon D McGill; Christopher M Evenson; Abhishek J Deshmukh; David O Hodge; Daniel H Cooper; Sandeep S Sodhi; Phillip S Cuculich; Samuel J Asirvatham; Peter A Noseworthy; Christopher V DeSimone; Adam M May Journal: Ann Noninvasive Electrocardiol Date: 2021-09-25 Impact factor: 1.468