Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.

[EN]Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who wou...

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Autores: Nuñez-Garcia, Jean C, Sánchez-Puente, Antonio, Sampedro-Gómez, Jesús, Vicente-Palacios, Víctor, Jiménez-Navarro, Manuel, Oterino-Manzanas, Armando, Jiménez Candil, Francisco Javier, Dorado-Díaz, Pedro Ignacio, Sánchez, Pedro L
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/161986
Acceso en línea:http://hdl.handle.net/10366/161986
Access Level:acceso abierto
Palabra clave:Machine learning
Electrical cardioversion
Atrial fibrillation
Rhythm control
Pharmacologic cardioversion
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spelling Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.Nuñez-Garcia, Jean CSánchez-Puente, AntonioSampedro-Gómez, JesúsVicente-Palacios, VíctorJiménez-Navarro, ManuelOterino-Manzanas, ArmandoJiménez Candil, Francisco JavierDorado-Díaz, Pedro IgnacioSánchez, Pedro LMachine learningElectrical cardioversionAtrial fibrillationRhythm controlPharmacologic cardioversion[EN]Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación FEDER Funds “Una manera de hacer Europa” grants CIBERCV CB16/11/00374 Instituto de Salud Carlos III (ISCIII)MDPI202520252022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/161986reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésPI21/00369GRS2179/A/2020CIBERCV CB16/11/00374info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1619862026-06-07T06:28:51Z
dc.title.none.fl_str_mv Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
title Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
spellingShingle Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
Nuñez-Garcia, Jean C
Machine learning
Electrical cardioversion
Atrial fibrillation
Rhythm control
Pharmacologic cardioversion
title_short Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
title_full Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
title_fullStr Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
title_full_unstemmed Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
title_sort Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
dc.creator.none.fl_str_mv Nuñez-Garcia, Jean C
Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Víctor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez Candil, Francisco Javier
Dorado-Díaz, Pedro Ignacio
Sánchez, Pedro L
author Nuñez-Garcia, Jean C
author_facet Nuñez-Garcia, Jean C
Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Víctor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez Candil, Francisco Javier
Dorado-Díaz, Pedro Ignacio
Sánchez, Pedro L
author_role author
author2 Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Víctor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez Candil, Francisco Javier
Dorado-Díaz, Pedro Ignacio
Sánchez, Pedro L
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Machine learning
Electrical cardioversion
Atrial fibrillation
Rhythm control
Pharmacologic cardioversion
topic Machine learning
Electrical cardioversion
Atrial fibrillation
Rhythm control
Pharmacologic cardioversion
description [EN]Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/161986
url http://hdl.handle.net/10366/161986
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PI21/00369
GRS2179/A/2020
CIBERCV CB16/11/00374
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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