Automated differentiation of wide QRS complex tachycardia using QRS complex polarity
Abstract Background Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00725-2 |
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| author | Adam M. May Bhavesh B. Katbamna Preet A. Shaikh Sarah LoCoco Elena Deych Ruiwen Zhou Lei Liu Krasimira M. Mikhova Rugheed Ghadban Phillip S. Cuculich Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Anthony Kashou |
| author_facet | Adam M. May Bhavesh B. Katbamna Preet A. Shaikh Sarah LoCoco Elena Deych Ruiwen Zhou Lei Liu Krasimira M. Mikhova Rugheed Ghadban Phillip S. Cuculich Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Anthony Kashou |
| author_sort | Adam M. May |
| collection | DOAJ |
| description | Abstract Background Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]). Methods In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. Part 1 uses WCT ECG measurements alone, Part 2 pairs WCT and baseline ECG features, and Part 3 combines all features used in Parts 1 and 2 Results Among 235 WCT patients (158 SWCT, 77 VT), 103 had gold standard diagnoses. Part 1 models achieved AUCs of 0.86–0.88 using WCT ECG features alone. Part 2 improved accuracy with paired ECGs (AUCs 0.90–0.93). Part 3 showed variable results (AUC 0.72–0.93), with RF and SVM performing best. Conclusions Incorporating engineered parameters related to QRS polarity direction and shifts can yield effective WCT differentiation, presenting a promising approach for automated CEI algorithms. |
| format | Article |
| id | doaj-art-5970e16e581e452daff6810caa593d77 |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-5970e16e581e452daff6810caa593d772025-01-05T12:44:11ZengNature PortfolioCommunications Medicine2730-664X2024-12-014111210.1038/s43856-024-00725-2Automated differentiation of wide QRS complex tachycardia using QRS complex polarityAdam M. May0Bhavesh B. Katbamna1Preet A. Shaikh2Sarah LoCoco3Elena Deych4Ruiwen Zhou5Lei Liu6Krasimira M. Mikhova7Rugheed Ghadban8Phillip S. Cuculich9Daniel H. Cooper10Thomas M. Maddox11Peter A. Noseworthy12Anthony Kashou13Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDivision of Cardiovascular Diseases, Loyola University Chicago, Stritch School of MedicineDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDivision of Biostatistics, Washington University School of Medicine in St. LouisDivision of Biostatistics, Washington University School of Medicine in St. LouisDivision of Biostatistics, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. LouisDepartment of Cardiovascular Medicine, Mayo ClinicDepartment of Cardiovascular Medicine, Mayo ClinicAbstract Background Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]). Methods In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. Part 1 uses WCT ECG measurements alone, Part 2 pairs WCT and baseline ECG features, and Part 3 combines all features used in Parts 1 and 2 Results Among 235 WCT patients (158 SWCT, 77 VT), 103 had gold standard diagnoses. Part 1 models achieved AUCs of 0.86–0.88 using WCT ECG features alone. Part 2 improved accuracy with paired ECGs (AUCs 0.90–0.93). Part 3 showed variable results (AUC 0.72–0.93), with RF and SVM performing best. Conclusions Incorporating engineered parameters related to QRS polarity direction and shifts can yield effective WCT differentiation, presenting a promising approach for automated CEI algorithms.https://doi.org/10.1038/s43856-024-00725-2 |
| spellingShingle | Adam M. May Bhavesh B. Katbamna Preet A. Shaikh Sarah LoCoco Elena Deych Ruiwen Zhou Lei Liu Krasimira M. Mikhova Rugheed Ghadban Phillip S. Cuculich Daniel H. Cooper Thomas M. Maddox Peter A. Noseworthy Anthony Kashou Automated differentiation of wide QRS complex tachycardia using QRS complex polarity Communications Medicine |
| title | Automated differentiation of wide QRS complex tachycardia using QRS complex polarity |
| title_full | Automated differentiation of wide QRS complex tachycardia using QRS complex polarity |
| title_fullStr | Automated differentiation of wide QRS complex tachycardia using QRS complex polarity |
| title_full_unstemmed | Automated differentiation of wide QRS complex tachycardia using QRS complex polarity |
| title_short | Automated differentiation of wide QRS complex tachycardia using QRS complex polarity |
| title_sort | automated differentiation of wide qrs complex tachycardia using qrs complex polarity |
| url | https://doi.org/10.1038/s43856-024-00725-2 |
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