Evidence map›Paper›PMID 36745287›Full record

ArticleJournal of cardiovascular translational research2023

Aortic Stenosis: Haemodynamic Benchmark and Metric Reliability Study.

Harminder Gill, Joao Filipe Fernandes, Amanda Nio, Cameron Dockerill, Nili Shah, Naajia Ahmed, Jason Raymond, Shu Wang, Julio Sotelo, Jesus Urbina and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of cardiovascular translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 98% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

14 authors at 5 institutions in 2 countries.

Harminder GillSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK. harminder.gill@kcl.ac.uk.ORCID http://orcid.org/0000-0003-1527-2263
Joao Filipe FernandesSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Amanda NioSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Cameron DockerillSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Nili ShahSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Naajia AhmedSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Jason RaymondUniversity of Oxford, Oxford, UK.
Shu WangSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Julio SoteloSchool of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile.
Jesus UrbinaBiomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
Sergio UribeBiomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
Ronak RajaniSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Kawal RhodeSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
Pablo LamataSchool of Biomedical Engineering and Imaging Sciences, King's College London, Becket House, 1 Lambeth Palace Road, SE1 7EU, London, UK.
King's College London · GBPontificia Universidad Católica de Chile · CLGuy's Hospital · GBSt Thomas' Hospital · GBUniversity of Oxford · GB

Funding

British Heart Foundation TG/17/3/33406Medical Research Council MR/N013700/1Wellcome TrustWellcome Trust 209450/Z/17/ZWellcome Trust WT203148/Z/16/Z
6 · The paper itself

Abstract

Aortic stenosis is a condition which is fatal if left untreated. Novel quantitative imaging techniques which better characterise transvalvular pressure drops are being developed but require refinement and validation. A customisable and cost-effective workbench valve phantom circuit capable of replicating valve mechanics and pathology was created. The reproducibility and relationship of differing haemodynamic metrics were assessed from ground truth pressure data alongside imaging compatibility. The phantom met the requirements to capture ground truth pressure data alongside ultrasound and magnetic resonance image compatibility. The reproducibility was successfully tested. The robustness of three different pressure drop metrics was assessed: whilst the peak and net pressure drops provide a robust assessment of the stenotic burden in our phantom, the peak-to-peak pressure drop is a metric that is confounded by non-valvular factors such as wave reflection. The peak-to-peak pressure drop is a metric that should be reconsidered in clinical practice. The left panel shows manufacture of low cost, functional valves. The central section demonstrates circuit layout, representative MRI and US images alongside gross valve morphologies. The right panel shows the different pressure drop metrics that were assessed for reproducibility.

Indexed as

Aortic ValveAortic Valve StenosisBenchmarkingHemodynamicsHumansReproducibility of Results3D printingAortic stenosisAortic valveHaemodynamics

Identifiers

PMID36745287
PMCPMC10480252
OpenAlexW4319294335

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.