Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study
This model was capable of predicting short-term increases or decreases in arrhythmic episodes with reasonable sensitivity and specificity using data collected through remote monitoring of implantable devices. The model's performance is expected to improve progressively as more data samples become available, including demographic data and clinical records.
Authors
- Joaquín Fernández de la Concha (ORCID: https://orcid.org/0000-0002-1004-5988)
- Virginie Lucas
- Javier Paniagua Olmedillas
- S Briongos Figuero (ORCID: https://orcid.org/0000-0002-7893-5581)
- Juan Benezet Mazuecos
- Javier García Fernández
- M. López-De la Fuente
- Javier Ramos Maqueda
- Sarai Paz Fernández
- Tomás Datino Romaniega
- Javier Jiménez Candil
- Ignacio Fernández Lozano
- Juan López-Dóriga Costales
Institutions
- Hospital Clínico San Carlos (ES)
- Clínica Rementería (ES)
- Hospital La Luz (ES)
- Complejo Hospitalario de Salamanca (ES)
- Hospital Universitario 12 De Octubre (ES)
- Hospital Universitario Puerta de Hierro Majadahonda (ES)
- Hospital Universitario Quirónsalud Madrid (ES)
- Hospital Universitario de Burgos (ES)
- Observatorio de la Inmigración de Tenerife (ES)
- The Arrhythmia Institute (US)
- Hospital Virgen de la Concha (ES)
- Hospital Clínico Universitario Lozano Blesa (ES)
- Centro de Implantología Cirugía Oral y Maxilofacial (ES)
- Hospital Universitario Infanta Leonor (ES)
Publication Details
- Journal
- JMIR Cardio
- Published
- 2026-02-16
- DOI
- https://doi.org/10.2196/85841
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00