BACKGROUND: The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software. METHODS: In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance. RESULTS: The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p < 0.001), frontal percent amplitude change (%) (p < 0.001), and horizontal percent amplitude change (%) (p < 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT. CONCLUSION: The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.
BACKGROUND: The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software. METHODS: In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance. RESULTS: The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p < 0.001), frontal percent amplitude change (%) (p < 0.001), and horizontal percent amplitude change (%) (p < 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT. CONCLUSION: The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.
Authors: Anthony H Kashou; Christopher V DeSimone; David O Hodge; Rickey Carter; Grace Lin; Samuel J Asirvatham; Peter A Noseworthy; Abhishek J Deshmukh; Adam M May Journal: Data Brief Date: 2020-04-21
Authors: Adam M May; Christopher V DeSimone; Anthony H Kashou; David O Hodge; Grace Lin; Suraj Kapa; Samuel J Asirvatham; Abhishek J Deshmukh; Peter A Noseworthy; Peter A Brady Journal: Data Brief Date: 2019-04-17
Authors: Anthony H Kashou; Peter A Noseworthy; Christopher V DeSimone; Abhishek J Deshmukh; Samuel J Asirvatham; Adam M May Journal: J Am Heart Assoc Date: 2020-05-19 Impact factor: 5.501
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