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Tytuł pozycji:

Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

Tytuł:
Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia
Autorzy:
Alejandro Lopez-Perez
Rafael Sebastian
M. Izquierdo
Ricardo Ruiz
Martin Bishop
Jose M. Ferrero
Temat:
myocardial infarction (MI)
ventricular tachycardia (VT)
border zone (BZ)
electrical remodeling (ER)
fibrosis
slow conducting channel (SCC)
Physiology
QP1-981
Źródło:
Frontiers in Physiology, Vol 10 (2019)
Wydawca:
Frontiers Media S.A., 2019.
Rok publikacji:
2019
Kolekcja:
LCC:Physiology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1664-042X
Relacje:
https://www.frontiersin.org/article/10.3389/fphys.2019.00580/full; https://doaj.org/toc/1664-042X
DOI:
10.3389/fphys.2019.00580
Dostęp URL:
https://doaj.org/article/6ec63a4a794343eb877e4705c12f572a  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.6ec63a4a794343eb877e4705c12f572a
Czasopismo naukowe
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.

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