Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias
Abstract 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 EC...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Annals of Noninvasive Electrocardiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/anec.13018 |
| _version_ | 1797955720989114368 |
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| author | Anthony H. Kashou Sarah LoCoco Preet A. Shaikh Bhavesh B. Katbamna Ojasav Sehrawat Daniel H. Cooper Sandeep S. Sodhi Phillip S. Cuculich Marye J. Gleva Elena Deych Ruiwen Zhou Lei Liu Abhishek J. Deshmukh Samuel J. Asirvatham Peter A. Noseworthy Christopher V. DeSimone Adam M. May |
| author_facet | Anthony H. Kashou Sarah LoCoco Preet A. Shaikh Bhavesh B. Katbamna Ojasav Sehrawat Daniel H. Cooper Sandeep S. Sodhi Phillip S. Cuculich Marye J. Gleva Elena Deych Ruiwen Zhou Lei Liu Abhishek J. Deshmukh Samuel J. Asirvatham Peter A. Noseworthy Christopher V. DeSimone Adam M. May |
| author_sort | Anthony H. Kashou |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T23:37:28Z |
| format | Article |
| id | doaj.art-ab9943e4461046129749b717264354c5 |
| institution | Directory Open Access Journal |
| issn | 1082-720X 1542-474X |
| language | English |
| last_indexed | 2024-04-10T23:37:28Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Annals of Noninvasive Electrocardiology |
| spelling | doaj.art-ab9943e4461046129749b717264354c52023-01-11T16:25:30ZengWileyAnnals of Noninvasive Electrocardiology1082-720X1542-474X2023-01-01281n/an/a10.1111/anec.13018Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardiasAnthony H. Kashou0Sarah LoCoco1Preet A. Shaikh2Bhavesh B. Katbamna3Ojasav Sehrawat4Daniel H. Cooper5Sandeep S. Sodhi6Phillip S. Cuculich7Marye J. Gleva8Elena Deych9Ruiwen Zhou10Lei Liu11Abhishek J. Deshmukh12Samuel J. Asirvatham13Peter A. Noseworthy14Christopher V. DeSimone15Adam M. May16Department of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Medicine Washington University School of Medicine Missouri St. Louis USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USADepartment of Medicine Washington University School of Medicine Missouri St. Louis USADepartment of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USADivision of Biostatistics Washington University School of Medicine Missouri St. Louis USADivision of Biostatistics Washington University School of Medicine Missouri St. Louis USADivision of Biostatistics Washington University School of Medicine Missouri St. Louis USADepartment of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Cardiovascular Medicine Mayo Clinic Minnesota Rochester USADepartment of Medicine, Division of Cardiovascular Diseases Washington University School of Medicine Missouri St. Louis USAAbstract 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.https://doi.org/10.1111/anec.13018ventricular tachycardia/fibrillation < basicnon‐invasive techniques—electrocardiography < clinical |
| spellingShingle | Anthony H. Kashou Sarah LoCoco Preet A. Shaikh Bhavesh B. Katbamna Ojasav Sehrawat Daniel H. Cooper Sandeep S. Sodhi Phillip S. Cuculich Marye J. Gleva Elena Deych Ruiwen Zhou Lei Liu Abhishek J. Deshmukh Samuel J. Asirvatham Peter A. Noseworthy Christopher V. DeSimone Adam M. May Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias Annals of Noninvasive Electrocardiology ventricular tachycardia/fibrillation < basic non‐invasive techniques—electrocardiography < clinical |
| title | Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias |
| title_full | Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias |
| title_fullStr | Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias |
| title_full_unstemmed | Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias |
| title_short | Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias |
| title_sort | computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide qrs complex tachycardias |
| topic | ventricular tachycardia/fibrillation < basic non‐invasive techniques—electrocardiography < clinical |
| url | https://doi.org/10.1111/anec.13018 |
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