Evidence map›Paper›PMID 34249645›Full record

ArticleQuantitative imaging in medicine and surgery2021

Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience.

Joël Greffier, Adel Hoballah, Alexandre Sadate, Fabien de Oliveira, Pierre-Geraud Claret, Hélène de Forges, Paul Loubet, Jean-Marc Mauboussin, Aymeric Hamard, Jean-Paul Beregi and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

18 citing papers in PubMed, 26 citations in OpenAlex.

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  9. Clinical and CT diagnosis of 50 cases ofQuantitative imaging in medicine and surgery · 2023
    Article
  10. Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?Radiation physics and chemistry (Oxford, England : 1993) · 2023
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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

11 authors at 5 institutions in 1 country.

Joël GreffierDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Adel HoballahDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Alexandre SadateDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Fabien de OliveiraDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Pierre-Geraud ClaretDepartment of Anesthesia Resuscitation Pain Emergencies, Nîmes University Hospital, University of Montpellier, Nîmes, France.
Hélène de ForgesDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Paul LoubetVBMI, INSERM U1047, Department of Infectious and Tropical Disease, CHU Nîmes, Univ Montpellier, Nîmes, France.
Jean-Marc MauboussinVBMI, INSERM U1047, Department of Infectious and Tropical Disease, CHU Nîmes, Univ Montpellier, Nîmes, France.
Aymeric HamardDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Jean-Paul BeregiDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Julien FrandonDepartment of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, Nîmes, France.
Centre Hospitalier Universitaire de Nîmes · FRInserm · FRInstitut de Recherche en Cancérologie de Montpellier · FRUniversité de Montpellier · FRUniversité de Nîmes · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUltra low dose chest computed tomography (CT) acquisitions have been used for selected emergency room patients with acute dyspnea or minor thoracic trauma. The purpose of this study was to evaluate the diagnostic performance of ultra-low-dose (ULD) chest CT for detecting viral pneumonia patterns compared to standard (STD) dose chest CT.

methodsAll consecutive adult patients with two non-enhanced chest CT acquisitions, one STD and one ULD, for suspicion of viral pneumonia between March 5

resultsThe study included 380 patients with 97 CTs (25.5%) compatible with viral pneumonia. The mean effective doses (EDs) were 1.66 (1.29; 2.18) mSv for STD and 0.20 (0.18; 0.22) mSv for ULD CT (P<0.001). The sensitivity and specificity of ULD CT for viral pneumonia detection were 98.9% and 99.0%, respectively. GGO, consolidation and fibrous stripes were equally visible in STD and ULD in 100% (n=97), 36% (n=35) and 23% (n=22) of compatible viral pneumonia-CT patients, respectively. Air bronchogram sign detection was equivalent, concerning 23% (n=22) of patients in STD and 22% (n=21) in ULD. Crazy paving was visible in 24% (n=23) of patients in STD and only 8% (n=8) in ULD (P=0.003).

conclusionsIn comparison to STD dose chest CT, ULD chest CT, with a mean reduction dose of 88.0%, has comparable diagnostic performance for detecting viral pneumonia on CT.

Indexed as

coronavirus infectionsMultidetector computed tomography (multidetector CT)ultra-low-dose CT (ULD CT)

Identifiers

PMID34249645
PMCPMC8250025
OpenAlexW3156733428

What OpenQuestion holds

Textmetadata
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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.