Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms

Autores
Sanz García, Ancor; Cecconi, Alberto; Vera, Alberto; Camarasaltas, Juan Miguel; Alfonso, Fernando; Ortega, Guillermo José; Jimenez Borreguero, Jesus
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.
Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Vera, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; España
Fil: Camarasaltas, Juan Miguel. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Alfonso, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Ortega, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Jimenez Borreguero, Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Materia
ATRIAL FIBRILLATION
BIOMARKERS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/166910
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/166910
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiogramsSanz García, AncorCecconi, AlbertoVera, AlbertoCamarasaltas, Juan MiguelAlfonso, FernandoOrtega, Guillermo JoséJimenez Borreguero, JesusATRIAL FIBRILLATIONBIOMARKERShttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Vera, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Camarasaltas, Juan Miguel. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Alfonso, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Ortega, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Jimenez Borreguero, Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaB M J Publishing Group2021-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/166910Sanz García, Ancor; Cecconi, Alberto; Vera, Alberto; Camarasaltas, Juan Miguel; Alfonso, Fernando; et al.; Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms; B M J Publishing Group; Heart (british Cardiac Society); 107; 22; 6-2021; 1813-18191355-6037CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1136/heartjnl-2021-319120info:eu-repo/semantics/altIdentifier/url/https://heart.bmj.com/content/107/22/1813info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:24:21Zoai:ri.conicet.gov.ar:11336/166910instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:24:21.934CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
title Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
spellingShingle Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
Sanz García, Ancor
ATRIAL FIBRILLATION
BIOMARKERS
title_short Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
title_full Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
title_fullStr Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
title_full_unstemmed Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
title_sort Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms
dc.creator.none.fl_str_mv Sanz García, Ancor
Cecconi, Alberto
Vera, Alberto
Camarasaltas, Juan Miguel
Alfonso, Fernando
Ortega, Guillermo José
Jimenez Borreguero, Jesus
author Sanz García, Ancor
author_facet Sanz García, Ancor
Cecconi, Alberto
Vera, Alberto
Camarasaltas, Juan Miguel
Alfonso, Fernando
Ortega, Guillermo José
Jimenez Borreguero, Jesus
author_role author
author2 Cecconi, Alberto
Vera, Alberto
Camarasaltas, Juan Miguel
Alfonso, Fernando
Ortega, Guillermo José
Jimenez Borreguero, Jesus
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv ATRIAL FIBRILLATION
BIOMARKERS
topic ATRIAL FIBRILLATION
BIOMARKERS
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.
Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Vera, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; España
Fil: Camarasaltas, Juan Miguel. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Alfonso, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Ortega, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Jimenez Borreguero, Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
description Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.
publishDate 2021
dc.date.none.fl_str_mv 2021-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/166910
Sanz García, Ancor; Cecconi, Alberto; Vera, Alberto; Camarasaltas, Juan Miguel; Alfonso, Fernando; et al.; Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms; B M J Publishing Group; Heart (british Cardiac Society); 107; 22; 6-2021; 1813-1819
1355-6037
CONICET Digital
CONICET
url http://hdl.handle.net/11336/166910
identifier_str_mv Sanz García, Ancor; Cecconi, Alberto; Vera, Alberto; Camarasaltas, Juan Miguel; Alfonso, Fernando; et al.; Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms; B M J Publishing Group; Heart (british Cardiac Society); 107; 22; 6-2021; 1813-1819
1355-6037
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1136/heartjnl-2021-319120
info:eu-repo/semantics/altIdentifier/url/https://heart.bmj.com/content/107/22/1813
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv B M J Publishing Group
publisher.none.fl_str_mv B M J Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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