Happy new year everybody! The year of the lockdowns in Europe is over and we begin with the year of the vaccines. We will have to be in a “soft” lockdown some more time before receiving the vaccine, but hey, it is said to be the last year of the pandemic – let’s be optimistic!
With this enthusiastic spirit is how I want to start and move through 2021, because this will be the year in which the PersonalizeAF consortium – hopefully including our group here at IDIBAPS – will get the first results of the research in atrial fibrillation (AF) we started back in last summer. And it is something to be cheerful about!
Thinking of this, of obtaining a first grasp of my research outcomes, my family considered it cute to give me a heart stuffed-toy in order to encourage me. And here in the right you can see me, happy of being involved in cardiac research.
How is it that I start the year with such eagerness? I think the answer is clear to me: the project we are working on seems to be more and more defined, with clearer paths to follow, more explicit data to analyse and more tools to play with. Since I think I still haven’t had the chance to explain my project with higher detail, I will use this post to clarify what we are investigating here in Barcelona.
The basis of my project is in validating late gadolinium enhancement MRI (LGE-MRI) as a tool to predict atrial fibrosis. Several groups have published and are currently studying the association of MRI predicted fibrosis with other techniques which evaluate fibrosis invasively, such as voltage and conduction velocity measured with electroanatomical mapping (EAM). This has been studied both in the atria [1, 2, 3] and in the ventricle [4, 5].
In the case of my project, I will continue this validation by crossing the fibrotic data obtained with LGE-MRI to EAM and electrocardiographic imaging (ECGi) data. ECGi is a tool that other members of the consortium work with, but in other groups (like the Universitat Politècnica de València one) they are trying to look at, analyse and improve the tracking of reentrant patterns seen with ECGi (as Carlos explained in this previous post). In our case, however, we want to compare these patterns to other data and see how they associate to each other. Following this, the idea is to analyse how good do fibrotic regions found with LGE-MRI correlate to areas of slow conduction found with ECGi. This will help in validating both techniques as tools to help in the treatment and in the ablation procedure of atrial fibrillation.
In order to get a first grasp of these associations, we will retrospectively analyse the data of previous studies, while preparing a protocol for a prospective study which will allow us to more specifically and properly validate ECGi and LGE-MRI.
In addition to this, we will also try to see the effect of tissue stressing when it comes to uncovering slow conduction areas and how these slow conduction areas correlate better or worse to fibrotic regions found with LGE-MRI, as explained in the figure below, where IIR stands for Image Intensity Ratio, a magnitude used to measure fibrosis with LGE-MRI.
I know it’s still early and I know there is a long journey ahead full of hard work, success, mistakes, stress, relief and other emotions while working with the data and getting some results, but first steps are always like this, and I am encouraged to begin this sail. Let’s go for it, 2021!
 Caixal, Gala, et al. “Accuracy of left atrial fibrosis detection with cardiac magnetic resonance: correlation of late gadolinium enhancement with endocardial voltage and conduction velocity.” EP Europace (2020).
 Fukumoto, Kotaro, et al. “Association of left atrial local conduction velocity with late gadolinium enhancement on cardiac magnetic resonance in patients with atrial fibrillation.” Circulation: Arrhythmia and Electrophysiology 9.3 (2016)
 Aronis, Konstantinos N., et al. “Accurate Conduction Velocity Maps and Their Association With Scar Distribution on Magnetic Resonance Imaging in Patients With Postinfarction Ventricular Tachycardias.” Circulation: Arrhythmia and Electrophysiology 13.4 (2020)
 Ustunkaya, Tuna, et al. “Association of regional myocardial conduction velocity with the distribution of hypoattenuation on contrast-enhanced perfusion computed tomography in patients with postinfarct ventricular tachycardia.” Heart Rhythm 16.4 (2019)
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