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Bridging the gap between measurements and modelling: a cardiovascular functional avatar

Overview of attention for article published in Scientific Reports, July 2017
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Title
Bridging the gap between measurements and modelling: a cardiovascular functional avatar
Published in
Scientific Reports, July 2017
DOI 10.1038/s41598-017-06339-0
Pubmed ID
Authors

Belén Casas, Jonas Lantz, Federica Viola, Gunnar Cedersund, Ann F. Bolger, Carl-Johan Carlhäll, Matts Karlsson, Tino Ebbers

Abstract

Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 21%
Student > Ph. D. Student 24 20%
Student > Master 11 9%
Student > Bachelor 10 8%
Student > Doctoral Student 6 5%
Other 15 13%
Unknown 28 24%
Readers by discipline Count As %
Engineering 42 35%
Computer Science 7 6%
Medicine and Dentistry 5 4%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Nursing and Health Professions 3 3%
Other 16 13%
Unknown 43 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 July 2017.
All research outputs
#20,436,330
of 22,990,068 outputs
Outputs from Scientific Reports
#106,114
of 124,126 outputs
Outputs of similar age
#276,272
of 316,512 outputs
Outputs of similar age from Scientific Reports
#4,844
of 5,906 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 124,126 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 5,906 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.