TY - JOUR
T1 - Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardias
AU - LoCoco, Sarah
AU - Kashou, Anthony H.
AU - Deshmukh, Abhishek J.
AU - Asirvatham, Samuel J.
AU - DeSimone, Christopher V.
AU - Mikhova, Krasimira M.
AU - Sodhi, Sandeep S.
AU - Cuculich, Phillip S.
AU - Ghadban, Rugheed
AU - Cooper, Daniel
AU - Maddox, Thomas M.
AU - Noseworthy, Peter A.
AU - May, Adam
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - The differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia (SWCT) via 12-lead ECG (electrocardiogram) interpretation is a crucial yet demanding clinical task. Decades of research have been dedicated to simplifying and improving this differentiation via manual algorithms. Despite such research, the effectiveness of such algorithms still remains limited, primarily due to reliance on user expertise. To combat this limitation, automated algorithms have been created that show promise as alternatives to manual ECG interpretation. However, direct comparison of the methods’ diagnostic performances has not been undertaken. A recent publication (LoCoco et al., 2024) compared the diagnostic performance between traditional manual ECG interpretation approaches (i.e. Brugada, Vereckei aVR, and VT Score) to novel automated wide QRS complex tachycardia differentiation algorithms (i.e. WCT Formula I, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). Two electrophysiologists independently applied the 3 manual WCT differentiation approaches to 213 ECGs. Simultaneously, computerized data from the same paired WCT with baseline ECGs were processed by the 5 automated WCT differentiation algorithms. Following these analyses, the diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. In this article, a summary of data components relating to diagnostic performance of the methods tested is presented.
AB - The differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia (SWCT) via 12-lead ECG (electrocardiogram) interpretation is a crucial yet demanding clinical task. Decades of research have been dedicated to simplifying and improving this differentiation via manual algorithms. Despite such research, the effectiveness of such algorithms still remains limited, primarily due to reliance on user expertise. To combat this limitation, automated algorithms have been created that show promise as alternatives to manual ECG interpretation. However, direct comparison of the methods’ diagnostic performances has not been undertaken. A recent publication (LoCoco et al., 2024) compared the diagnostic performance between traditional manual ECG interpretation approaches (i.e. Brugada, Vereckei aVR, and VT Score) to novel automated wide QRS complex tachycardia differentiation algorithms (i.e. WCT Formula I, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). Two electrophysiologists independently applied the 3 manual WCT differentiation approaches to 213 ECGs. Simultaneously, computerized data from the same paired WCT with baseline ECGs were processed by the 5 automated WCT differentiation algorithms. Following these analyses, the diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. In this article, a summary of data components relating to diagnostic performance of the methods tested is presented.
KW - Automated algorithms
KW - Supraventricular wide complex tachycardia
KW - Ventricular tachycardia
KW - Wide complex tachycardia
KW - Wide QRS complex tachycardia
UR - http://www.scopus.com/inward/record.url?scp=85211475064&partnerID=8YFLogxK
U2 - 10.1016/j.dib.2024.111198
DO - 10.1016/j.dib.2024.111198
M3 - Article
AN - SCOPUS:85211475064
SN - 2352-3409
VL - 58
JO - Data in Brief
JF - Data in Brief
M1 - 111198
ER -