The WCT Formula: A novel algorithm designed to automatically differentiate wide-complex tachycardias

Adam M. May, Christopher V. DeSimone, Anthony H. Kashou, David O. Hodge, Grace D Lin, Suraj Kapa, Samuel J Asirvatham, Abhishek J. Deshmukh, Peter Noseworthy, Peter A. Brady

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)61-68
Number of pages8
JournalJournal of Electrocardiology
Volume54
DOIs
StatePublished - May 1 2019

Fingerprint

Tachycardia
Electrocardiography
Ventricular Tachycardia
Logistic Models
Software

Keywords

  • Computerized electrocardiogram interpretation
  • Supraventricular tachycardia
  • Ventricular tachycardia
  • Wide complex tachycardia

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

The WCT Formula : A novel algorithm designed to automatically differentiate wide-complex tachycardias. / May, Adam M.; DeSimone, Christopher V.; Kashou, Anthony H.; Hodge, David O.; Lin, Grace D; Kapa, Suraj; Asirvatham, Samuel J; Deshmukh, Abhishek J.; Noseworthy, Peter; Brady, Peter A.

In: Journal of Electrocardiology, Vol. 54, 01.05.2019, p. 61-68.

Research output: Contribution to journalArticle

May, Adam M. ; DeSimone, Christopher V. ; Kashou, Anthony H. ; Hodge, David O. ; Lin, Grace D ; Kapa, Suraj ; Asirvatham, Samuel J ; Deshmukh, Abhishek J. ; Noseworthy, Peter ; Brady, Peter A. / The WCT Formula : A novel algorithm designed to automatically differentiate wide-complex tachycardias. In: Journal of Electrocardiology. 2019 ; Vol. 54. pp. 61-68.
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abstract = "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.",
keywords = "Computerized electrocardiogram interpretation, Supraventricular tachycardia, Ventricular tachycardia, Wide complex tachycardia",
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T2 - A novel algorithm designed to automatically differentiate wide-complex tachycardias

AU - May, Adam M.

AU - DeSimone, Christopher V.

AU - Kashou, Anthony H.

AU - Hodge, David O.

AU - Lin, Grace D

AU - Kapa, Suraj

AU - Asirvatham, Samuel J

AU - Deshmukh, Abhishek J.

AU - Noseworthy, Peter

AU - Brady, Peter A.

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N2 - 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.

AB - 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.

KW - Computerized electrocardiogram interpretation

KW - Supraventricular tachycardia

KW - Ventricular tachycardia

KW - Wide complex tachycardia

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