Evidence map›Paper›PMID 35039557›Full record

ArticleScientific reports2022

Accuracy of vascular tortuosity measures using computational modelling.

Vishesh Kashyap, Ramtin Gharleghi, Darson D Li, Lucy McGrath-Cadell, Robert M Graham, Chris Ellis, Mark Webster, Susann Beier

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
9.6field-weighted citation impact, top 1% of its field
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

21 citing papers in PubMed, 53 citations in OpenAlex.

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  15. Measuring arterial tortuosity in the cerebrovascular system using Time-of-Flight MRI.medRxiv : the preprint server for health sciences · 2024
    Article
  16. Article
  17. Magnetic resonance lymphangiography: Establishing normal.Journal of vascular surgery. Venous and lymphatic disorders · 2024
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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

8 authors at 5 institutions in 3 countries.

Vishesh Kashyap *Mechanical and Aerospace Engineering Department, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, USA.
Ramtin Gharleghi *School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia. r.gharleghi@student.unsw.edu.au.
Darson D LiSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia.
Lucy McGrath-CadellMolecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.
Robert M GrahamMolecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.
Chris EllisAuckland Heart Group, Auckland, New Zealand.
Mark WebsterAuckland City Hospital, Auckland, New Zealand.
Susann BeierSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia.
UNSW Sydney · AUVictor Chang Cardiac Research Institute · AUAuckland City Hospital · NZNational Heart Foundation of New Zealand · NZSamueli Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe coronary tortuosity has previously been linked to low shear stresses at the luminal surface, yet this relationship is not fully understood. Several previous studies considered different tortuosity metrics when exploring its impact of on the wall shear stress (WSS), which has likely contributed to the ambiguous findings in the literature. Here, we aim to analyze different tortuosity metrics to determine a benchmark for the highest correlating metric with low time-averaged WSS (TAWSS). Using Computed Tomography Coronary Angiogram (CTCA) data from 127 patients without coronary artery disease, we applied all previously used tortuosity metrics to the left main coronary artery bifurcation, and to its left anterior descending and left circumflex branches, before modelling their TAWSS using computational fluid dynamics (CFD). The tortuosity measures included tortuosity index, average absolute-curvature, root-mean-squared (RMS) curvature, and average squared-derivative-curvature. Each tortuosity measure was then correlated with the percentage of vessel area that showed a < 0.4 Pa TAWSS, a threshold associated with altered endothelial cell cytoarchitecture and potentially higher disease risk. Our results showed a stronger correlation between curvature-based versus non-curvature-based tortuosity measures and low TAWSS, with the average-absolute-curvature showing the highest coefficient of determination across all left main branches (p < 0.001), followed by the average-squared-derivative-curvature (p = 0.001), and RMS-curvature (p = 0.002). The tortuosity index, the most widely used measure in literature, showed no significant correlation to low TAWSS (p = 0.86). We thus recommend the use of average-absolute-curvature as a tortuosity measure for future studies.

Indexed as

Computer SimulationCoronary AngiographyCoronary Vessel AnomaliesCoronary VesselsHumansSensitivity and SpecificityShear StrengthTomography, X-Ray Computed

Identifiers

PMID35039557
PMCPMC8764056
OpenAlexW4206509634

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Registered trials

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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 OpenQuestion graph.