TY - JOUR
T1 - Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias
AU - Kashou, Anthony H.
AU - LoCoco, Sarah
AU - Shaikh, Preet A.
AU - Katbamna, Bhavesh B.
AU - Sehrawat, Ojasav
AU - Cooper, Daniel H.
AU - Sodhi, Sandeep S.
AU - Cuculich, Phillip S.
AU - Gleva, Marye J.
AU - Deych, Elena
AU - Zhou, Ruiwen
AU - Liu, Lei
AU - Deshmukh, Abhishek J.
AU - Asirvatham, Samuel J.
AU - Noseworthy, Peter A.
AU - DeSimone, Christopher V.
AU - May, Adam M.
N1 - Publisher Copyright:
© 2022 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.
PY - 2023/1
Y1 - 2023/1
N2 - Background: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. Objective: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. Methods: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). Results: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. Conclusion: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.
AB - Background: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. Objective: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. Methods: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). Results: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. Conclusion: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.
KW - non-invasive techniques—electrocardiography < clinical
KW - ventricular tachycardia/fibrillation < basic
UR - http://www.scopus.com/inward/record.url?scp=85142359312&partnerID=8YFLogxK
U2 - 10.1111/anec.13018
DO - 10.1111/anec.13018
M3 - Article
C2 - 36409204
AN - SCOPUS:85142359312
SN - 1082-720X
VL - 28
JO - Annals of Noninvasive Electrocardiology
JF - Annals of Noninvasive Electrocardiology
IS - 1
M1 - e13018
ER -