ISSN 1671-5411 CN 11-5329/R
Juan Lacalzada-Almeida, Javier Garcia-Niebla. How to detect atrial fibrosis. J Geriatr Cardiol 2017; 14(3): 185-194. doi: 10.11909/j.issn.1671-5411.2017.03.008
Citation: Juan Lacalzada-Almeida, Javier Garcia-Niebla. How to detect atrial fibrosis. J Geriatr Cardiol 2017; 14(3): 185-194. doi: 10.11909/j.issn.1671-5411.2017.03.008

How to detect atrial fibrosis

doi: 10.11909/j.issn.1671-5411.2017.03.008
  • Received Date: 2017-01-25
  • Rev Recd Date: 2017-04-01
  • Publish Date: 2017-03-28
  • In the last twenty years, new imaging techniques to assess atrial function and to predict the risk of recurrence of atrial fibrillation after treatment have been developed. The present review deals with the role of these techniques in the detection of structural and functional changes of the atrium and diagnosis of atrial remodeling, particularly atrial fibrosis. Echocardiography allows the detection of anatomical, functional changes and deformation of the atrial wall during the phases of the cardiac cycle. For this, adequate acquisition of atrial images is necessary using speckle tracking imaging and interpretation of the resulting strain and strain rate curves. This allows to predict new-onset atrial fibrillation and recurrences. Its main limitations are inter-observer variability, the existence of different software manufacturers, and the fact that the software used were originally developed for the evaluation of the ventricular function and are now applied to the atria. Cardiac magnetic resonance, using contrast enhancement with gadolinium, plays a key role in the visualization and quantification of atrial fibrosis. This is the established method for in vivo visualization of myocardial fibrotic tissue. The non-invasive evaluation of atrial fibrosis is associated with the risk of recurrence of atrial fibrillation and with electro-anatomical endocardial mapping. We discuss the limitations of these techniques, derived from the difficulty of demonstrating the correlation between fibrosis imaging and histology, and poor intra- and inter- observer reproducibility. The sources of discordance are described, mainly due to image acquisition and processing, and the challenges ahead in an attempt to eliminate differences between operators.
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