Evidence map›Paper›PMID 34806052›Full record

ArticleJVS-vascular science2021

Relative strain is a novel predictor of aneurysmal degeneration of the thoracic aorta: An ex vivo mechanical study.

Peter Chiu, Hong-Pyo Lee, Alex R Dalal, Tiffany Koyano, Marie Nguyen, Andrew J Connolly, Ovijit Chaudhuri, Michael P Fischbein

Open access · goldAbstract read
In one paragraph

Article in JVS-vascular science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

8 citing papers in PubMed, 9 citations in OpenAlex.

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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 2 institutions in 1 country.

Peter ChiuDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford University, Stanford, Calif.
Hong-Pyo LeeDepartment of Mechanical Engineering, Stanford University, Stanford, Calif.
Alex R DalalDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford University, Stanford, Calif.
Tiffany KoyanoDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford University, Stanford, Calif.
Marie NguyenDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford University, Stanford, Calif.
Andrew J ConnollyDepartment of Pathology, University of California San Francisco, San Francisco, Calif.
Ovijit ChaudhuriDepartment of Mechanical Engineering, Stanford University, Stanford, Calif.
Michael P FischbeinDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford University, Stanford, Calif.
Stanford University · USUniversity of California, San Francisco · US

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationKL2TR001083 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$4.9M
Stanford Integrated Cardiovascular/Pulmonary Residency Research Training ProgramR38HL143615 · NHLBI · STANFORD UNIVERSITY · PI Michael Peter Fischbein, Marlene Rabinovitch · 2020 to 2026
$2.2M
Marfan Aortic Embryologic Origin Influences miR-29b Regulators and TargetsR01AR066629 · NIAMS · STANFORD UNIVERSITY · PI FISCHBEIN, MICHAEL PETER · 2016 to 2020
$1.5M
NCATS NIH HHS KL2 TR001083NHLBI NIH HHS R38 HL143615NIAMS NIH HHS R01 AR066629
6 · The paper itself

Abstract

objectiveCurrent guidelines for prophylactic replacement of the thoracic aorta, primarily based on size alone, may not be adequate in identifying patients at risk for either progression of disease or aortic catastrophe. We undertook the current study to determine whether the mechanical properties of the aorta might be able to predict aneurysmal dilatation of the aorta using a clinical database and benchtop mechanical testing of human aortic tissue.

methodsUsing over 400 samples from 31 patients, mechanical properties were studied in (a) normal aorta and then (b) between normal and diseased aorta using linear mixed-effects models. A machine learning technique was used to predict aortic growth rate over time using mechanical properties and baseline clinical characteristics.

resultsHealthy aortic tissue under in vivo loading conditions, after accounting for aortic segment location, had lower longitudinal elastic modulus compared with circumferential elastic modulus: -166.8 kPa (95% confidence interval [CI]: -210.8 to -122.7,

conclusionsIncreasing relative strain was identified as a novel independent predictor of aneurysmal degeneration. Noninvasive measurement of relative strain may aid in the identification and monitoring of patients at risk for aneurysmal degeneration. (JVS-Vascular Science 2021;2:1-12.). CLINICAL RELEVANCE: Aortic aneurysm surveillance and prophylactic surgical recommendations are based on computed tomographic angiogram aortic dimensions and growth rate measurements. However, aortic catastrophes may occur at small sizes, confounding current risk stratification models. Herein, we report that increasing aortic relative strain, that is, greater distensibility, is associated with growth over time, thus potentially identifying patients at risk for dissection/rupture.

Indexed as

Elastic modulusFracture toughnessMachine learningRelative strainThoracic aortic aneurysm

Identifiers

PMID34806052
PMCPMC8585654
OpenAlexW3204509072

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.