Evidence map›Paper›PMID 34630842›Full record

ArticleThe Pan African medical journal2021

[Lesions on CT scan in patients hospitalized for coronavirus pneumonia during the first wave of the SARS-CoV-2 pandemic at the University Clinics in Kinshasa (DRC)].

Frederick Tshibasu Tshienda, Michel Lelo Tshikwela, Jean-Robert Makulo Risasi, Hippolyte Nani Tuma Situakibanza, Randa Salem, Patrick Sekele Marob Ndjock, René Ngiyulu Makwala, Antoine Molua Aundu, Joseph Bodi Mabiala, Jean-Marie Kayembe Ntumba and 1 more

Open access · greenAbstract read
In one paragraph

Article in The Pan African medical journal, 2021. 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
0.1field-weighted citation impact, top 51% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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 1 institution in 1 country.

Frederick Tshibasu TshiendaService d´Imagerie Médicale, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Michel Lelo TshikwelaService d´Imagerie Médicale, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Jean-Robert Makulo RisasiService de Néphrologie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Hippolyte Nani Tuma SituakibanzaService des Maladies Infectieuses, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Randa SalemService d´Imagerie Médicale, Hôpital Universitaire Fatouma Bourkiba, Monastir, Tunisie.
Patrick Sekele Marob NdjockDépartement d´Odontostomatologie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
René Ngiyulu MakwalaDépartement de Pédiatrie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Antoine Molua AunduService d´Imagerie Médicale, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Joseph Bodi MabialaDépartement de Pédiatrie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Jean-Marie Kayembe NtumbaService de Pneumologie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
Benjamin Longo-MbenzaService de Cardiologie, Cliniques Universitaires de Kinshasa, Kinshasa, République Démocratique du Congo.
University of Kinshasa · CD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionthe main purpose of this study is to describe chest computed tomography (CT) findings in 26 patients hospitalized with COVID-19 pneumonia during the first wave of the SARS-CoV-2 pandemic at the University Clinics in Kinshasa (UCK).

methodswe conducted a descriptive study of chest CT findings in 26 patients hospitalized with coronavirus pneumonia at the UCK over a 9-month period, from March 17 to November 17, 2020. Hitachi - CT-scanner 16 slice was used in all our patients. After analyzing lesions, these were divided into lesions suggestive and non-suggestive of SARS-CoV-2 infection.

resultsthe average age of patients was 53.02 years. Male sex was the most affected (76.9%). Respiratory distress was the most common clinical symptom (61.5%). Arterial hypertension and renal failure were the most common comorbidities (3O% and 6%). Bilateral ground-glass opacities, with a predominantly peripheral distribution, accounted for 69.2% of cases, followed by condensations (57.7%) and crazy paving (19.2%). Severe COVID-19 was most frequently found (34.61%). Distal and proximal pulmonary embolism was the most common complication (11.5%). Among the associated diseases, pleurisy and pulmonary PAH were most frequently found (30.8%). The majority of our patients had parenchymal lung lesions, corresponding to early-stage disease on CT (50%).

conclusionat the UCK, during the first wave of SARS-CoV-2 pandemic, lesions on CT suggestive of COVID-19 were dominated by plaque-like ground-glass opacities, followed by nonsystematized parenchymatous condensations and crazy paving. The less observed atypical lesions consisted of unilateral, peribronchovascular pseudo-nodular condensations and infection in the remodeled lung. Severe COVID-19 was the most common CT finding. Proximal and distal pulmonary embolism was the most common complication. This study highlights that these findings are consistent with those reported in the literature.

Indexed as

HospitalizationAdolescentAdultAgedChildChild, PreschoolCOVID-19Democratic Republic of the CongoFemaleHumansInfantMaleMiddle AgedPneumonia, ViralSeverity of Illness IndexSex DistributionCOVID-19CTKinshasamanagement

Identifiers

PMID34630842
PMCPMC8486946
OpenAlexW3187340939

What OpenQuestion holds

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LicenceCC BY
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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.