Evidence map›Paper›PMID 32555300›Full record

ArticleScientific reports2020

A validated computational framework to predict outcomes in TAVI.

Giorgia M Bosi, Claudio Capelli, Mun Hong Cheang, Nicola Delahunty, Michael Mullen, Andrew M Taylor, Silvia Schievano

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  12. AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
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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

7 authors.

Giorgia M BosiCardiovascular Engineering Laboratory, UCL Mechanical Engineering, London, UK. g.bosi@ucl.ac.uk.
Claudio CapelliCentre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children, London, UK.
Mun Hong CheangBarts Health NHS Trust, University College London Hospital, London, UK.
Nicola DelahuntyBarts Health NHS Trust, University College London Hospital, London, UK.
Michael MullenUCL Institute of Cardiovascular Science, London, UK.
Andrew M TaylorCentre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children, London, UK.
Silvia SchievanoCentre for Cardiovascular Imaging, UCL Institute of Cardiovascular Science & Great Ormond Street Hospital for Children, London, UK.

Funding

British Heart Foundation FS/13/11/30056British Heart Foundation PG/17/6/32797
6 · The paper itself

Abstract

Transcatheter aortic valve implantation (TAVI) still presents complications: paravalvular leakage (PVL) and onset of conduction abnormalities leading to permanent pacemaker implantation. Our aim was testing a validated patient-specific computational framework for prediction of TAVI outcomes and possible complications. Twenty-eight TAVI patients (14 SapienXT and 14 CoreValve) were retrospectively selected. Pre-procedural CT images were post-processed to create 3D patient-specific implantation sites. The procedures were simulated with finite element analysis. Simulations' results were compared against post-procedural clinical fluoroscopy and echocardiography images. The computational model was in good agreement with clinical findings: the overall stent diameter difference was 2.6% and PVL was correctly identified with a post-processing algorithm in 83% of cases. Strains in the implantation site were studied to assess the risk of conduction system disturbance and were found highest in the patient who required pacemaker implantation. This study suggests that computational tool could support safe planning and broadening of TAVI.

Indexed as

Computer SimulationAgedAged, 80 and overAlgorithmsAortic ValveAortic Valve StenosisBalloon ValvuloplastyEchocardiographyFemaleFinite Element AnalysisHumansMalePostoperative ComplicationsProsthesis DesignRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID32555300
PMCPMC7303192

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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.