RT Journal Article
T1 Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.
A1 Nuñez-Garcia, Jean C
A1 Sánchez-Puente, Antonio
A1 Sampedro-Gómez, Jesús
A1 Vicente-Palacios, Victor
A1 Jiménez-Navarro, Manuel
A1 Oterino-Manzanas, Armando
A1 Jiménez-Candil, Javier
A1 Dorado-Diaz, P Ignacio
A1 Sánchez, Pedro L
K1 atrial fibrillation
K1 electrical cardioversion
K1 machine-learning
K1 pharmacologic cardioversion
K1 rhythm control
AB 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
SN 2077-0383
YR 2022
FD 2022-05-07
LK http://hdl.handle.net/10668/21282
UL http://hdl.handle.net/10668/21282
LA en
DS RISalud
RD Apr 6, 2025