Evidence map›Paper›PMID 42315675›Full record

SynthesisJournal of cardiovascular translational research2026

A Systematic Review of Modeling Platforms for Atrioventricular Valves in Atrioventricular Septal Defects.

Chen Chia Wang, Murray Marx, Eric Barth, Antonio D'Amore, Haoxiang Luo, Garrett Coyan

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of cardiovascular translational research, 2026. 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
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.

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

6 authors.

Chen Chia WangDepartment of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Murray MarxDepartment of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Eric BarthDepartment of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA.
Antonio D'AmoreDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Haoxiang LuoDepartment of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA.
Garrett CoyanDepartment of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA. garrett.coyan@vumc.org.ORCID 0000-0003-0889-1592

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given the high rate of re-intervention in patients with atrioventricular septal defects (AVSD), there is a continuing need to accurately model the complex valve anatomy and surgical repair strategies. This systematic review examined the current scientific landscape for modeling platforms designed to study abnormal congenital heart valves that are used or could be used for AVSD investigation. Of 1,050 sources screened, 47 were included (14 in silico; 26 in vitro; 7 in vivo). In silico models studying valve pathologies have advanced in recent years, but few targeted AVSD valves. In vitro platforms allow comparisons of repair techniques, but have yet to be applied for AVSD valve repair. In vivo studies could offer physiologically accurate platforms, but face challenges such as replicating AVSD pathologies. Multidisciplinary approaches to creating high-fidelity, reproducible, physiologically accurate AVSD valve models can transform the understanding of AVSD valve properties and repair strategies.

Indexed as

Computer SimulationHeart Septal DefectsMitral ValveModels, CardiovascularAnimalsHemodynamicsHumansPrinting, Three-Dimensional3D printAtrioventricular septal defectAtrioventricular valveFlow loopHeart valveIn silicoIn vitroIn vivoModelPulse duplicator

Identifiers

PMID42315675
PMCPMC13279604

What OpenQuestion holds

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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.