Evidence map›Paper›PMID 35364704›Full record

ArticleAnnals of biomedical engineering2022

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach.

Ivan Benemerito, Ana Paula Narata, Andrew Narracott, Alberto Marzo

Abstract read
In one paragraph

Article in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ivan BenemeritoINSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK. i.benemerito@sheffield.ac.uk.ORCID http://orcid.org/0000-0002-4942-7852
Ana Paula NarataDepartment of Neuroradiology, University Hospital of Southampton, Southampton, UK.
Andrew NarracottINSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK.
Alberto MarzoINSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK.

Funding

Horizon 2020 675451Horizon 2020 823712
6 · The paper itself

Abstract

Assessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pre- and post-stroke velocity and flow wave propagation in a typical arterial network, and Sobol's sensitivity analysis, supported by the use of Gaussian process emulators, to identify biomarkers linked to cerebral perfusion. We showed that values of pulsatility index of the right anterior cerebral artery > 1.6 are associated with poor perfusion and may require immediate intervention. Three additional biomarkers with similar behaviour, all related to pulsatility indices, were identified. These results suggest that flow pulsatility measured at specific locations could be used to effectively estimate distal cerebral perfusion rates, and ultimately improve clinical diagnosis and management of ischaemic stroke.

Indexed as

Brain IschemiaIschemic StrokeStrokeBiomarkersBlood Flow VelocityCerebrovascular CirculationHumansMachine LearningBiomarkersBiomarkerBrain circulationCardiovascular modellingGaussian process emulatorIschaemic strokeLeptomeningeal collateralSensitivity analysisWave propagation

Identifiers

PMID35364704
PMCPMC9079032

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.