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

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 r...

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Autores principales: Nuñez-Garcia, Jean C., Sánchez-Puente, Antonio, Sampedro-Gómez, Jesús, Vicente-Palacios, Victor, Jiménez-Navarro, Manuel, Oterino-Manzanas, Armando, Jiménez-Candil, Javier, Dorado-Diaz, P. Ignacio, Sánchez, Pedro L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101912/
https://www.ncbi.nlm.nih.gov/pubmed/35566761
http://dx.doi.org/10.3390/jcm11092636
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author Nuñez-Garcia, Jean C.
Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Victor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez-Candil, Javier
Dorado-Diaz, P. Ignacio
Sánchez, Pedro L.
author_facet Nuñez-Garcia, Jean C.
Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Victor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez-Candil, Javier
Dorado-Diaz, P. Ignacio
Sánchez, Pedro L.
author_sort Nuñez-Garcia, Jean C.
collection PubMed
description 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.
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spelling pubmed-91019122022-05-14 Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model Nuñez-Garcia, Jean C. Sánchez-Puente, Antonio Sampedro-Gómez, Jesús Vicente-Palacios, Victor Jiménez-Navarro, Manuel Oterino-Manzanas, Armando Jiménez-Candil, Javier Dorado-Diaz, P. Ignacio Sánchez, Pedro L. J Clin Med Article 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. MDPI 2022-05-07 /pmc/articles/PMC9101912/ /pubmed/35566761 http://dx.doi.org/10.3390/jcm11092636 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nuñez-Garcia, Jean C.
Sánchez-Puente, Antonio
Sampedro-Gómez, Jesús
Vicente-Palacios, Victor
Jiménez-Navarro, Manuel
Oterino-Manzanas, Armando
Jiménez-Candil, Javier
Dorado-Diaz, P. Ignacio
Sánchez, Pedro L.
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
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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101912/
https://www.ncbi.nlm.nih.gov/pubmed/35566761
http://dx.doi.org/10.3390/jcm11092636
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