Evidence map›Paper›PMID 29475456›Full record

ArticleCritical care (London, England)2018

Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model.

Stefan Boehme, Frédéric P R Toemboel, Erik K Hartmann, Alexander H Bentley, Oliver Weinheimer, Yang Yang, Tobias Achenbach, Michael Hagmann, Eugenijus Kaniusas, James E Baumgardner and 1 more

Open access · goldAbstract read
In one paragraph

Article in Critical care (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 99% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Observational
  2. Review
  3. Article
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

11 authors at 4 institutions in 3 countries.

Stefan BoehmeDepartment of Anesthesia, General Intensive Care Medicine and Pain Management, Medical University Vienna, Waehringer Guertel, 18-20, Vienna, Austria. stefan.boehme@meduniwien.ac.at.ORCID http://orcid.org/0000-0003-3132-2486
Frédéric P R ToemboelDepartment of Anesthesia, General Intensive Care Medicine and Pain Management, Medical University Vienna, Waehringer Guertel, 18-20, Vienna, Austria.
Erik K HartmannDepartment of Anesthesiology, Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
Alexander H BentleyDepartment of Anesthesiology, Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
Oliver WeinheimerDepartment of Diagnostic and Interventional Radiology, Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
Yang YangDepartment of Diagnostic and Interventional Radiology, Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
Tobias AchenbachDepartment of Diagnostic and Interventional Radiology, Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
Michael HagmannCenter for Medical Statistics, Informatics, and Intelligent Systems, Medical University Vienna, Vienna, Austria.
Eugenijus KaniusasInstitute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Vienna, Austria.
James E BaumgardnerDepartment of Anesthesiology, University of Pittsburgh Medical Center, Pittsburgh, PA, 15261, USA.
Klaus MarkstallerDepartment of Anesthesia, General Intensive Care Medicine and Pain Management, Medical University Vienna, Waehringer Guertel, 18-20, Vienna, Austria.
Johannes Gutenberg University Mainz · DEStatistics Austria · ATTU Wien · ATUniversity of Pittsburgh Medical Center · US

Funding

German Research Council DFG Pak 415: Ma 2398/6
6 · The paper itself

Abstract

backgroundCyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT).

methodsIn ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH

resultsIn healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R

conclusionsc-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D.

Indexed as

Respiratory SoundsAnimalsArea Under CurveDisease Models, AnimalFour-Dimensional Computed TomographyInhalationLungMonitoring, PhysiologicPulmonary AtelectasisRespiration, ArtificialROC CurveSwineVentilator-Induced Lung InjuryAtelectasisCyclic recruitmentDynamic computed tomographyLung soundsPositive end-expiratory pressure

Identifiers

PMID29475456
PMCPMC6389194
OpenAlexW2788427513

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.