Evidence map›Paper›PMID 35154799›Full record

ArticleRoyal Society open science2022

Improving transcatheter aortic valve interventional predictability via fluid-structure interaction modelling using patient-specific anatomy.

Vijay Govindarajan, Arun Kolanjiyil, Nils P Johnson, Hyunggun Kim, Krishnan B Chandran, David D McPherson

Open access · goldAbstract read
In one paragraph

Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

6 citing papers in PubMed, 13 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

6 authors at 3 institutions in 2 countries.

Vijay GovindarajanDivision of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX 77054, USA.ORCID 0000-0001-6046-5262
Arun KolanjiyilDepartment of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, Virginia, USA.
Nils P JohnsonDivision of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX 77054, USA.
Hyunggun KimDivision of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX 77054, USA.
Krishnan B ChandranDivision of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX 77054, USA.
David D McPhersonDivision of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science at Houston, 1881 East Road, Houston, TX 77054, USA.
Sungkyunkwan University · KRUniversity of Iowa · USVirginia Commonwealth University · US

Funding

Center for Clinical and Translational Sciences (CCTS)UL1TR000371 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2012 to 2016
$13.4M
NCATS NIH HHS UL1 TR000371
6 · The paper itself

Abstract

Transcatheter aortic valve replacement (TAVR) is now a standard treatment for high-surgical-risk patients with severe aortic valve stenosis. TAVR is being explored for broader indications including degenerated bioprosthetic valves, bicuspid valves and for aortic valve (AV) insufficiency. It is, however, challenging to predict whether the chosen valve size, design or its orientation would produce the most-optimal haemodynamics in the patient. Here, we present a novel patient-specific evaluation framework to realistically predict the patient's AV performance with a high-fidelity fluid-structure interaction analysis that included the patient's left ventricle and ascending aorta (AAo). We retrospectively evaluated the pre- and post-TAVR dynamics of a patient who underwent a 23 mm TAVR and evaluated against the patient's virtually de-calcified AV serving as a hypothetical benchmark. Our model predictions were consistent with clinical data. Stenosed AV produced a turbulent flow during peak-systole, while aortic flow with TAVR and de-calcified AV were both in the laminar-to-turbulent transitional regime with an estimated fivefold reduction in viscous dissipation. For TAVR, dissipation was highest during early systole when valve deformation was the greatest, suggesting that an efficient valve opening may reduce energy loss. Our study demonstrates that such patient-specific modelling frameworks can be used to improve predictability and in the planning of AV interventions.

Indexed as

aortic valve interventionfluid–structure interactioninterventional predictabilitypatient-specific modelling

Identifiers

PMID35154799
PMCPMC8826300
OpenAlexW4210935555

What OpenQuestion holds

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LicenceCC BY
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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.