Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction
Background Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle‐branch block (LBBB) and right bundle‐branch block or nonspecific intraventricular conduction delay. This categorization, although physiologically accurate, may fail...
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Wiley
2025-07-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.124.040814 |
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| author | Mehak Gurnani Konstantinos Patlatzoglou Joseph Barker Derek Bivona Libor Pastika Ewa Sieliwonczyk Boroumand Zeidaabadi Paolo Inglese Lara Curran Ahran D. Arnold Declan O'Regan Zachary Whinnett Kenneth C. Bilchick Nicholas S. Peters Daniel B. Kramer Jonathan W. Waks Arunashis Sau Fu Siong Ng |
| author_facet | Mehak Gurnani Konstantinos Patlatzoglou Joseph Barker Derek Bivona Libor Pastika Ewa Sieliwonczyk Boroumand Zeidaabadi Paolo Inglese Lara Curran Ahran D. Arnold Declan O'Regan Zachary Whinnett Kenneth C. Bilchick Nicholas S. Peters Daniel B. Kramer Jonathan W. Waks Arunashis Sau Fu Siong Ng |
| author_sort | Mehak Gurnani |
| collection | DOAJ |
| description | Background Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle‐branch block (LBBB) and right bundle‐branch block or nonspecific intraventricular conduction delay. This categorization, although physiologically accurate, may fail to capture the nuances of diseases subtypes. Methods We used unsupervised machine learning to identify and characterize novel broad QRS phenogroups. First, we trained a variational autoencoder on 1.1 million ECGs and discovered 51 latent features that showed high disentanglement and ECG reconstruction accuracy. We then extracted these features from 42 538 ECGs with QRS durations >120 milliseconds and employed a reversed graph embedding method to model population heterogeneity as a tree structure with different branches representing phenogroups. Results Six phenogroups were identified, including phenogroups of right bundle‐branch block and LBBB with varying risk of cardiovascular disease and mortality. The higher risk right bundle‐branch block phenogroup exhibited increased risk of cardiovascular death (adjusted hazard ratio [aHR], 1.46 [1.30–1.63], P<0.0001) and all‐cause mortality (aHR, 1.24 [1.16–1.33], P<0.0001) compared with the baseline phenogroup. Within LBBB ECGs, tree position predicted future cardiovascular disease risk differentially. Additionally, for subjects with LBBB undergoing cardiac resynchronization therapy, tree position predicted cardiac resynchronization therapy response independent of covariates, including QRS duration (adjusted odds ratio [aOR], 0.47 [0.25–0.86], P<0.05). Conclusions Our findings challenge the current paradigm, highlighting the potential for these phenogroups to enhance cardiac resynchronization therapy patient selection for subjects with LBBB and guide investigation and follow‐up strategies for subjects with higher risk right bundle‐branch block. |
| format | Article |
| id | doaj-art-71ee1545ec854ec2bfefecd204d24228 |
| institution | DOAJ |
| issn | 2047-9980 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj-art-71ee1545ec854ec2bfefecd204d242282025-08-20T02:46:24ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802025-07-01141310.1161/JAHA.124.040814Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality ReductionMehak Gurnani0Konstantinos Patlatzoglou1Joseph Barker2Derek Bivona3Libor Pastika4Ewa Sieliwonczyk5Boroumand Zeidaabadi6Paolo Inglese7Lara Curran8Ahran D. Arnold9Declan O'Regan10Zachary Whinnett11Kenneth C. Bilchick12Nicholas S. Peters13Daniel B. Kramer14Jonathan W. Waks15Arunashis Sau16Fu Siong Ng17National Heart and Lung Institute, Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKDepartment of Biomedical Engineering University of Virginia Charlottesville VA USANational Heart and Lung Institute, Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKIstituto Italiano di Tecnologia Genoa ItalyMRC Laboratory of Medical Sciences Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKMRC Laboratory of Medical Sciences Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKDepartment of Medicine University of Virginia Health System Charlottesville VA USANational Heart and Lung Institute, Imperial College London London UKRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USAHarvard‐Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USANational Heart and Lung Institute, Imperial College London London UKNational Heart and Lung Institute, Imperial College London London UKBackground Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle‐branch block (LBBB) and right bundle‐branch block or nonspecific intraventricular conduction delay. This categorization, although physiologically accurate, may fail to capture the nuances of diseases subtypes. Methods We used unsupervised machine learning to identify and characterize novel broad QRS phenogroups. First, we trained a variational autoencoder on 1.1 million ECGs and discovered 51 latent features that showed high disentanglement and ECG reconstruction accuracy. We then extracted these features from 42 538 ECGs with QRS durations >120 milliseconds and employed a reversed graph embedding method to model population heterogeneity as a tree structure with different branches representing phenogroups. Results Six phenogroups were identified, including phenogroups of right bundle‐branch block and LBBB with varying risk of cardiovascular disease and mortality. The higher risk right bundle‐branch block phenogroup exhibited increased risk of cardiovascular death (adjusted hazard ratio [aHR], 1.46 [1.30–1.63], P<0.0001) and all‐cause mortality (aHR, 1.24 [1.16–1.33], P<0.0001) compared with the baseline phenogroup. Within LBBB ECGs, tree position predicted future cardiovascular disease risk differentially. Additionally, for subjects with LBBB undergoing cardiac resynchronization therapy, tree position predicted cardiac resynchronization therapy response independent of covariates, including QRS duration (adjusted odds ratio [aOR], 0.47 [0.25–0.86], P<0.05). Conclusions Our findings challenge the current paradigm, highlighting the potential for these phenogroups to enhance cardiac resynchronization therapy patient selection for subjects with LBBB and guide investigation and follow‐up strategies for subjects with higher risk right bundle‐branch block.https://www.ahajournals.org/doi/10.1161/JAHA.124.040814bundle‐branch blockclusteringECGmachine learningphenotyping |
| spellingShingle | Mehak Gurnani Konstantinos Patlatzoglou Joseph Barker Derek Bivona Libor Pastika Ewa Sieliwonczyk Boroumand Zeidaabadi Paolo Inglese Lara Curran Ahran D. Arnold Declan O'Regan Zachary Whinnett Kenneth C. Bilchick Nicholas S. Peters Daniel B. Kramer Jonathan W. Waks Arunashis Sau Fu Siong Ng Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease bundle‐branch block clustering ECG machine learning phenotyping |
| title | Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction |
| title_full | Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction |
| title_fullStr | Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction |
| title_full_unstemmed | Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction |
| title_short | Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction |
| title_sort | revisiting abnormalities of ventricular depolarization redefining phenotypes and associated outcomes using tree based dimensionality reduction |
| topic | bundle‐branch block clustering ECG machine learning phenotyping |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.124.040814 |
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