Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

[EN] Background Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electro...

Descripción completa

Detalles Bibliográficos
Autores: H.Z. Kolk, Maarten, Deb, Brototo, Ruiperez-Campillo, Samuel, Bhatia, Neil K., Clopton, Paul, Wilde, Arthur A.M., Narayan, Sanjiv M., Knops, Reinoud E., Tjong, Fleur V.Y.
Tipo de recurso: artículo
Fecha de publicación:2023
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:10251/213527
Acceso en línea:https://riunet.upv.es/handle/10251/213527
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Cardiology
Electrocardiography
Machine Learning
Meta-analysis
Systematic review
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
id ES_a47f2e0e71f566d3552ebfa03fd22b2e
oai_identifier_str oai:riunet.upv.es:10251/213527
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studiesH.Z. Kolk, MaartenDeb, BrototoRuiperez-Campillo, SamuelBhatia, Neil K.Clopton, PaulWilde, Arthur A.M.Narayan, Sanjiv M.Knops, Reinoud E.Tjong, Fleur V.Y.Artificial intelligenceCardiologyElectrocardiographyMachine LearningMeta-analysisSystematic review03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades[EN] Background Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755¿0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642¿0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867¿0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies.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) .ElsevierNetherlands Organization for Scientific ResearchRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/213527reponame: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:10251/2135272026-06-13T07:49:27Z
dc.title.none.fl_str_mv Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
title Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
spellingShingle Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
H.Z. Kolk, Maarten
Artificial intelligence
Cardiology
Electrocardiography
Machine Learning
Meta-analysis
Systematic review
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
title_short Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
title_full Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
title_fullStr Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
title_full_unstemmed Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
title_sort Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
dc.creator.none.fl_str_mv H.Z. Kolk, Maarten
Deb, Brototo
Ruiperez-Campillo, Samuel
Bhatia, Neil K.
Clopton, Paul
Wilde, Arthur A.M.
Narayan, Sanjiv M.
Knops, Reinoud E.
Tjong, Fleur V.Y.
author H.Z. Kolk, Maarten
author_facet H.Z. Kolk, Maarten
Deb, Brototo
Ruiperez-Campillo, Samuel
Bhatia, Neil K.
Clopton, Paul
Wilde, Arthur A.M.
Narayan, Sanjiv M.
Knops, Reinoud E.
Tjong, Fleur V.Y.
author_role author
author2 Deb, Brototo
Ruiperez-Campillo, Samuel
Bhatia, Neil K.
Clopton, Paul
Wilde, Arthur A.M.
Narayan, Sanjiv M.
Knops, Reinoud E.
Tjong, Fleur V.Y.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Netherlands Organization for Scientific Research
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Artificial intelligence
Cardiology
Electrocardiography
Machine Learning
Meta-analysis
Systematic review
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
topic Artificial intelligence
Cardiology
Electrocardiography
Machine Learning
Meta-analysis
Systematic review
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
description [EN] Background Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755¿0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642¿0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867¿0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-03-01
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/10251/213527
url https://riunet.upv.es/handle/10251/213527
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_ 1869415502052327424
score 15,81155

Similares en LA Referencia