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Towards a Computational Framework for Modeling the Impact of Aortic Coarctations Upon Left Ventricular Load

Overview of attention for article published in Frontiers in Physiology, May 2018
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  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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Title
Towards a Computational Framework for Modeling the Impact of Aortic Coarctations Upon Left Ventricular Load
Published in
Frontiers in Physiology, May 2018
DOI 10.3389/fphys.2018.00538
Pubmed ID
Authors

Elias Karabelas, Matthias A. F. Gsell, Christoph M. Augustin, Laura Marx, Aurel Neic, Anton J. Prassl, Leonid Goubergrits, Titus Kuehne, Gernot Plank

Abstract

Computational fluid dynamics (CFD) models of blood flow in the left ventricle (LV) and aorta are important tools for analyzing the mechanistic links between myocardial deformation and flow patterns. Typically, the use of image-based kinematic CFD models prevails in applications such as predicting the acute response to interventions which alter LV afterload conditions. However, such models are limited in their ability to analyze any impacts upon LV load or key biomarkers known to be implicated in driving remodeling processes as LV function is not accounted for in a mechanistic sense. This study addresses these limitations by reporting on progress made toward a novel electro-mechano-fluidic (EMF) model that represents the entire physics of LV electromechanics (EM) based on first principles. A biophysically detailed finite element (FE) model of LV EM was coupled with a FE-based CFD solver for moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two clinical cases of patients suffering from aortic coarctations (CoA) were built and parameterized based on clinical data under pre-treatment conditions. For one patient case simulations under post-treatment conditions after geometric repair of CoA by a virtual stenting procedure were compared against pre-treatment results. Numerical stability of the approach was demonstrated by analyzing mesh quality and solver performance under the significantly large deformations of the LV blood pool. Further, computational tractability and compatibility with clinical time scales were investigated by performing strong scaling benchmarks up to 1536 compute cores. The overall cost of the entire workflow for building, fitting and executing EMF simulations was comparable to those reported for image-based kinematic models, suggesting that EMF models show potential of evolving into a viable clinical research tool.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 11 19%
Professor 4 7%
Student > Bachelor 3 5%
Student > Doctoral Student 3 5%
Other 8 14%
Unknown 14 24%
Readers by discipline Count As %
Engineering 22 38%
Computer Science 7 12%
Medicine and Dentistry 6 10%
Mathematics 5 9%
Sports and Recreations 1 2%
Other 0 0%
Unknown 17 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 January 2019.
All research outputs
#13,265,775
of 23,090,520 outputs
Outputs from Frontiers in Physiology
#4,306
of 13,836 outputs
Outputs of similar age
#163,367
of 330,907 outputs
Outputs of similar age from Frontiers in Physiology
#200
of 482 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,836 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,907 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 482 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.