Evidence map›Paper›PMID 32635998›Full record

ArticleJournal of biomechanics2020

Computational modeling of the strength of the ascending thoracic aortic media tissue under physiologic biaxial loading conditions.

Spandan Maiti, James R Thunes, Ronald N Fortunato, Thomas G Gleason, David A Vorp

Open access · greenAbstract read
In one paragraph

Article in Journal of biomechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

5 authors at 2 institutions in 2 countries.

Spandan MaitiDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States; Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, United States; Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States. Electronic address: spm54@pitt.edu.
James R ThunesANSYS Canada Ltd, Waterloo, ON, Canada.
Ronald N FortunatoMechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States.
Thomas G GleasonDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States; Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA, United States; Surgery, University of Pittsburgh, Pittsburgh, PA, United States; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, United States; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, United States.
David A VorpDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States; Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, United States; Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA, United States; Surgery, University of Pittsburgh, Pittsburgh, PA, United States; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, United States; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Clinical and Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA, United States.
University of Pittsburgh · USAnsys (Canada) · CA

Funding

Cardiovascular Bioengineering Training ProgramT32HL076124 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SANJEEV G SHROFF · 2005 to 2026
$7.0M
ROS Mechanisms in BAV AortopathyR01HL109132 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GLEASON, THOMAS GILLETTE · 2012 to 2024
$6.2M
NHLBI NIH HHS R01 HL109132NHLBI NIH HHS T32 HL076124
6 · The paper itself

Abstract

Type A Aortic Dissection (TAAD) is a life-threatening condition involving delamination of ascending aortic media layers. While current clinical guidelines recommend surgical intervention for aneurysm diameter > 5.5 cm, high incidence of TAAD in patients below this diameter threshold indicates the pressing need for improved evidence-based risk prediction metrics. Construction of such metrics will require the knowledge of the biomechanical failure properties of the aortic wall tissue under biaxial loading conditions. We utilized a fiber-level finite element based structural model of the aortic tissue to quantify the relationship between aortic tissue strength and physiologically relevant biaxial stress state for nonaneurysmal and aneurysmal patient cohorts with tricuspid aortic valve phenotype. We found that the model predicted strength of the aortic tissue under physiologic biaxial loading conditions depends on the stress biaxiality ratio, defined by the ratio of the longitudinal and circumferential components of the tissue stress. We determined that predicted biaxial tissue strength is statistically similar to its uniaxial circumferential strength below biaxiality ratios of 0.68 and 0.69 for nonaneurysmal and aneurysmal cohorts, respectively. Beyond this biaxiality ratio, predicted biaxial strength for both cohorts reduced drastically to a magnitude statistically similar to its longitudinal strength. We identified fiber-level failure mechanisms operative under biaxial stress state governing aforementioned tissue failure behavior. These findings are an important first step towards the development of mechanism-based TAAD risk assessment metrics for early identification of high-risk patients.

Indexed as

Aortic Aneurysm, ThoracicAortic DissectionAortaAortic ValveBiomechanical PhenomenaHumansStress, MechanicalTensile StrengthAortic dissectionBiaxial loadingFiber orientationFinite element modelPhysiologic tissue strengthStructural modellingTissue failure

Identifiers

PMID32635998
PMCPMC8073541
OpenAlexW3035430875

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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 OpenQuestion graph.