Digital Twins: New Personalized Strategies for Atrial Fibrillation Ablation

• Beatriz Aldana ^[1] ; Carlos López-Barrera ^[2] ; Juan López-Dóriga ^[3] ; Pablo Ávila ^[3] ; Alejandro Carta Bergaz ^[3] ; Felipe Atienza ^[3] ; Esteban Gonzáles Torrecilla ^[3] ; Caroline Roney ^[2] ; Ángel Arenal ^[3] ; Gonzalo R. Ríos-Muñoz ^[1]
  1. [1] Universidad Carlos III de Madrid

     Universidad Carlos III de Madrid

     Madrid, España

     
  2. [2] Queen Mary University of London

     Queen Mary University of London

     Reino Unido

     
  3. [3] Hospital General Universitario Gregorio Marañón

     Hospital General Universitario Gregorio Marañón

     Madrid, España

     

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• Localización: CASEIB 2024. Libro de Actas del XLII Congreso Anual de la Sociedad Española de Ingeniería Biomédica, 2024, ISBN 978-84-09-67332-2, págs. 129-132
• Idioma: inglés
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• Resumen

  • Atrial fibrillation (AF) catheter ablation often presents high recurrence rates and suboptimal outcomes. This study examines new ablation techniques to address these shortcomings, particularly in patients with persistent AF, where success rates are around 50%. Data from 41 persistent AF patients were used to create cardiac digital twins that replicate atrial features and analyze impulse propagation. These twins enabled personalized in silico modeling, improving the understanding of AF-sustaining drivers and optimizing patient outcomes. Simulations validated with patient data tested current and new ablation strategies, including pulmonary vein isolation (PVI), additional ablation lines, and a novel radial ablation method. Results showed that PVI reduced AF cases by 37.14%, expertise-based approaches by 91.43%, and radial ablation achieved 100% AF termination. These findings underscore the limited effectiveness of PVI alone and highlight the need for novel ablation strategies to effectively terminate AF and prevent recurrence.