Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator

[EN] Background Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural netwo...

Descripción completa

Detalles Bibliográficos
Autores: Kolk, Maarten Z. H., Ruiperez-Campillo, Samuel, Alvarez-Florez, Laura, Deb, Brototo, Bekkers, Erik J., Allaart, Cornelis P., Van Der Lingen, Anne-Lotte C.J., Clopton, Paul, Isgum, Ivana, Wilde, Arthur A. M., Knops, Reinoud E., Narayan, Sanjiv M., Tjong, Fleur V. Y.
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220607
Acceso en línea:https://riunet.upv.es/handle/10251/220607
Access Level:acceso abierto
Palabra clave:Cardiology
Machine learning
Deep learning
Electrocardiography
Sudden cardiac death
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
id ES_0aa7ffd60c0fd5afc8efd977a9eebca5
oai_identifier_str oai:riunet.upv.es:10251/220607
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillatorKolk, Maarten Z. H.Ruiperez-Campillo, SamuelAlvarez-Florez, LauraDeb, BrototoBekkers, Erik J.Allaart, Cornelis P.Van Der Lingen, Anne-Lotte C.J.Clopton, PaulIsgum, IvanaWilde, Arthur A. M.Knops, Reinoud E.Narayan, Sanjiv M.Tjong, Fleur V. Y.CardiologyMachine learningDeep learningElectrocardiographySudden cardiac death03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades[EN] Background Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models.This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).ElsevierAmsterdam Cardiovascular SciencesNetherlands Organization for Scientific ResearchRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220607reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengNetherlands Organization for Scientific Research https://doi.org/10.13039/501100003246 452019308open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2206072026-06-13T07:49:27Z
dc.title.none.fl_str_mv Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
title Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
spellingShingle Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
Kolk, Maarten Z. H.
Cardiology
Machine learning
Deep learning
Electrocardiography
Sudden cardiac death
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
title_short Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
title_full Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
title_fullStr Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
title_full_unstemmed Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
title_sort Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator
dc.creator.none.fl_str_mv Kolk, Maarten Z. H.
Ruiperez-Campillo, Samuel
Alvarez-Florez, Laura
Deb, Brototo
Bekkers, Erik J.
Allaart, Cornelis P.
Van Der Lingen, Anne-Lotte C.J.
Clopton, Paul
Isgum, Ivana
Wilde, Arthur A. M.
Knops, Reinoud E.
Narayan, Sanjiv M.
Tjong, Fleur V. Y.
author Kolk, Maarten Z. H.
author_facet Kolk, Maarten Z. H.
Ruiperez-Campillo, Samuel
Alvarez-Florez, Laura
Deb, Brototo
Bekkers, Erik J.
Allaart, Cornelis P.
Van Der Lingen, Anne-Lotte C.J.
Clopton, Paul
Isgum, Ivana
Wilde, Arthur A. M.
Knops, Reinoud E.
Narayan, Sanjiv M.
Tjong, Fleur V. Y.
author_role author
author2 Ruiperez-Campillo, Samuel
Alvarez-Florez, Laura
Deb, Brototo
Bekkers, Erik J.
Allaart, Cornelis P.
Van Der Lingen, Anne-Lotte C.J.
Clopton, Paul
Isgum, Ivana
Wilde, Arthur A. M.
Knops, Reinoud E.
Narayan, Sanjiv M.
Tjong, Fleur V. Y.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Amsterdam Cardiovascular Sciences
Netherlands Organization for Scientific Research
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Cardiology
Machine learning
Deep learning
Electrocardiography
Sudden cardiac death
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
topic Cardiology
Machine learning
Deep learning
Electrocardiography
Sudden cardiac death
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
description [EN] Background Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220607
url https://riunet.upv.es/handle/10251/220607
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Netherlands Organization for Scientific Research https://doi.org/10.13039/501100003246 452019308
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403176349728768
score 15,81155

Similares en LA Referencia