Evidence map›Paper›PMID 39432541›Full record

ArticlePloS one2024

A human cadaveric model for venous air embolism detection tool development.

Nathaniel L Robinson, Chris Marcellino, Matthew Johnston, Arnoley S Abcejo

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Nathaniel L RobinsonDepartment of Cardiovascular Surgery, Mayo Clinic, Rochester, MN, United States of America.ORCID 0000-0003-4460-6701
Chris MarcellinoDepartment of Anesthesia, Mayo Clinic, Rochester, MN, United States of America.ORCID 0000-0002-8046-5902
Matthew JohnstonDepartment of Anesthesia, Mayo Clinic, Rochester, MN, United States of America.
Arnoley S AbcejoDepartment of Anesthesia, Mayo Clinic, Rochester, MN, United States of America.ORCID 0000-0003-3838-5287

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
NCATS NIH HHS UL1 TR002377
6 · The paper itself

Abstract

purposeA human cadaveric model combining standard lung protective mechanical ventilation and modified cardiac bypass techniques was developed to allow investigation into automated modes of detection of venous air emboli (VAE) prior to in vivo human or animal investigations.

methodsIn this study, in order to create an artificial cardiopulmonary circuit in a cadaver that could mimic VAE physiology, the direction of flow was reversed from conventional cardiac bypass. Normal saline was circulated in isolation through the heart and lungs as opposed to the peripheral organs by placing the venous cannula into the aorta and the arterial cannula into the inferior vena cava with selective ligation of other vessels.

resultsMechanical ventilation and this reversed cardiac bypass scheme allowed preliminary detection of VAE independently but not in concert in our current simulation scheme due to pulmonary edema in the cadaver. A limited dissection approach was used initially followed by a radical exposure of the great vessels, and both proved feasible in terms of air signal detection. We used electrical impendence as a preliminary tool to validate detection in this cadaveric model however we theorize that it would work for echocardiographic, intravenous ultrasound or other novel modalities as well.

conclusionA cadaveric model allows monitoring technology development with reduced use of animal and conventional human testing.

Indexed as

CadaverEmbolism, AirCardiopulmonary BypassHumansRespiration, Artificial

Identifiers

PMID39432541
PMCPMC11493288

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.