Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse
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 thi...
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011600 |
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| author | Sarah LoCoco Anthony H. Kashou Abhishek J. Deshmukh Samuel J. Asirvatham Christopher V. DeSimone Krasimira M. Mikhova Sandeep S. Sodhi Phillip S. Cuculich Rugheed Ghadban Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Adam M. May |
| author_facet | Sarah LoCoco Anthony H. Kashou Abhishek J. Deshmukh Samuel J. Asirvatham Christopher V. DeSimone Krasimira M. Mikhova Sandeep S. Sodhi Phillip S. Cuculich Rugheed Ghadban Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Adam M. May |
| author_sort | Sarah LoCoco |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-058d2127f4c2433a88ca50c5cb24b96f |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-058d2127f4c2433a88ca50c5cb24b96f2025-01-31T05:11:28ZengElsevierData in Brief2352-34092025-02-0158111198Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverseSarah LoCoco0Anthony H. Kashou1Abhishek J. Deshmukh2Samuel J. Asirvatham3Christopher V. DeSimone4Krasimira M. Mikhova5Sandeep S. Sodhi6Phillip S. Cuculich7Rugheed Ghadban8Daniel H. Cooper9Thomas M. Maddox10Peter A. Noseworthy11Adam M. May12Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United States; Corresponding author.Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United StatesThe 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.http://www.sciencedirect.com/science/article/pii/S2352340924011600Wide QRS complex tachycardiaWide complex tachycardiaVentricular tachycardiaSupraventricular wide complex tachycardiaAutomated algorithms |
| spellingShingle | Sarah LoCoco Anthony H. Kashou Abhishek J. Deshmukh Samuel J. Asirvatham Christopher V. DeSimone Krasimira M. Mikhova Sandeep S. Sodhi Phillip S. Cuculich Rugheed Ghadban Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Adam M. May Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse Data in Brief Wide QRS complex tachycardia Wide complex tachycardia Ventricular tachycardia Supraventricular wide complex tachycardia Automated algorithms |
| title | Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse |
| title_full | Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse |
| title_fullStr | Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse |
| title_full_unstemmed | Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse |
| title_short | Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse |
| title_sort | dataset and analysis of automated and manual methods to differentiate wide qrs complex tachycardiasdataverse |
| topic | Wide QRS complex tachycardia Wide complex tachycardia Ventricular tachycardia Supraventricular wide complex tachycardia Automated algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011600 |
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