↓ Skip to main content

Model-based Indices Describing Cerebrovascular Dynamics

Overview of attention for article published in Neurocritical Care, October 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Readers on

mendeley
80 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Model-based Indices Describing Cerebrovascular Dynamics
Published in
Neurocritical Care, October 2013
DOI 10.1007/s12028-013-9868-4
Pubmed ID
Authors

Georgios V. Varsos, Magdalena Kasprowicz, Peter Smielewski, Marek Czosnyka

Abstract

Understanding the dynamic relationship between cerebral blood flow (CBF) and the circulation of cerebrospinal fluid (CSF) can facilitate management of cerebral pathologies. For this reason, various hydrodynamic models have been introduced in order to simulate the phenomena governing the interaction between CBF and CSF. The identification of hydrodynamic models requires an array of signals as input, with the most common of them being arterial blood pressure, intracranial pressure, and cerebral blood flow velocity; monitoring all of them is considered as a standard practice in neurointensive care. Based on these signals, physiological parameters like cerebrovascular resistance, compliances of cerebrovascular bed, and CSF space could then be estimated. Various secondary model-based indices describing cerebrovascular dynamics have been introduced, like the cerebral arterial time constant or critical closing pressure. This review presents model-derived indices that describe cerebrovascular phenomena, the nature of which is both physiological (carbon dioxide reactivity and arterial hypotension) and pathological (cerebral artery stenosis, intracranial hypertension, and cerebral vasospasm). In a neurointensive environment, real-time monitoring of a patient with these indices may be able to provide a detection of the onset of a cerebrovascular phenomenon, which could have otherwise been missed. This potentially "early warning" indicator may then prove to be important for the therapeutic management of the patient.

X Demographics

X Demographics

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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Poland 1 1%
Canada 1 1%
South Africa 1 1%
Unknown 76 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Researcher 16 20%
Student > Master 6 8%
Student > Bachelor 5 6%
Student > Doctoral Student 4 5%
Other 16 20%
Unknown 15 19%
Readers by discipline Count As %
Medicine and Dentistry 23 29%
Engineering 11 14%
Neuroscience 9 11%
Agricultural and Biological Sciences 5 6%
Mathematics 3 4%
Other 12 15%
Unknown 17 21%
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 28 February 2014.
All research outputs
#17,718,054
of 22,753,345 outputs
Outputs from Neurocritical Care
#1,229
of 1,495 outputs
Outputs of similar age
#148,460
of 207,519 outputs
Outputs of similar age from Neurocritical Care
#13
of 22 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,495 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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 207,519 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.