Development of new signal analysis methods as preoperative predictors of the Cox-Maze procedure outcome in atrial fibrillation

• Autores: Antonio Hernández Alonso
• Directores de la Tesis: Raúl Alcaraz Martínez (dir. tes.), José Joaquín Rieta Ibáñez (dir. tes.), Fernando Hornero Sos (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2017
• Idioma: español
• Tribunal Calificador de la Tesis: Roberto Hornero Sánchez (presid.), Lorenzo Fácila Rubio (secret.), Pedro Antonio Carrión Pérez (voc.)
• Materias:
  • Ciencias médicas
    • Ciencias clínicas
    • Medicina interna
      • Cardiología
  • Ciencias tecnológicas
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • Atrial fibrilation (AF) is the most common cardiac arrhythmia, however, the knowledge about its causes and mechanisms is still uncompleted. Several studies suggest that atrial structural and electrophysiological remodeling are directly related to its development and perpetuation. To this respect, ECG and preoperative clinical data have been studied to analyze different aspects of atrial remodeling. Nonetheless, there is a lack of studies using ECG parameters to provide valuable clinical information in the study of AF aggressive treatments, such as the Cox-Maze surgery. In this work, ECG parameters such as fibrillatory (f) waves organization and amplitude are studied to predict patient's rhythm from the discharge after the Cox-Maze surgery until a twelve months follow up period. On the other hand, widely used clinical parameters such as age, AF duration and left atrial size (LA size) are studied to assess electrocardiographic results. In addition, clinical information known as a risk factor to develop AF such as weight and body mass index has also been analyze. After assess the individual indices, classification models were created in order to optimize the prediction capability. The results obtained reported that the ECG indices outperform the cinical indices. Nevertheless, the information contained in both types of indices is complementary as the generation of a classification model combining the indices shows. This model exceeded 90% accuracy in each period analyzed. In conclusion, studying the AF information contained in an ECG could provide new data to understand the AF and also could help to develop a reliable method to predict preoperatively the Cox-Maze outcome.