Evidence map›Paper›PMID 34779269›Full record

ArticleActa radiologica (Stockholm, Sweden : 1987)2022

Chest radiography findings of COVID-19 pneumonia: a specific pattern for a confident differential diagnosis.

Nicholas Landini, Giulia Colzani, Pierluigi Ciet, Giovanni Tessarin, Alberto Dorigo, Luca Bertana, Carla Felice, Luca Scaldaferri, Martina Orlandi, Cosimo Nardi and 4 more

Abstract read
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Article in Acta radiologica (Stockholm, Sweden : 1987), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Nicholas LandiniDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.ORCID 0000-0002-2928-3003
Giulia ColzaniDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.
Pierluigi CietDepartment of Radiology and Nuclear Medicine, 6993Erasmus MC, Rotterdam, The Netherlands.
Giovanni TessarinDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.ORCID 0000-0003-3810-0560
Alberto DorigoDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.
Luca BertanaDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.
Carla FeliceDepartment of Medicine (DIMED), University of Padua, Medicine 1, 18703Ca' Foncello General Hospital, Treviso, Italy.
Luca ScaldaferriAcute and Emergency Department, Ca' Foncello Hospital, Treviso, Italy.
Martina OrlandiDivision of Rheumatology, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
Cosimo NardiDepartment of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit no. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Micaela RomagnoliPulmonology Unit, Ca' Foncello General Hospital, Treviso, Italy.
Luca SabaDepartment of Radiology and Department of Medical Science, 60276University of Cagliari, Cagliari, Italy.
Roberto RigoliDepartment of Specialistic and Laboratory Medicine, Microbiology Unit, Ca' Foncello Hospital, Treviso, Italy.
Giovanni MoranaDepartment of Radiology, Ca' Foncello General Hospital, Treviso, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChest radiography (CR) patterns for the diagnosis of COVID-19 have been established. However, they were not ideated comparing CR features with those of other pulmonary diseases. PURPOSE: To create the most accurate COVID-19 pneumonia pattern comparing CR findings of COVID-19 and non-COVID-19 pulmonary diseases and to test the model against the British Society of Thoracic Imaging (BSTI) criteria. MATERIAL AND

methodsCR of COVID-19 and non-COVID-19 pulmonary diseases, admitted to the emergency department, were evaluated. Assessed features were interstitial opacities, ground glass opacities, and/or consolidations and the predominant lung alteration. We also assessed uni-/bilaterality, location (upper/middle/lower), and distribution (peripheral/perihilar), as well as pleural effusion and perihilar vessels blurring. A binary logistic regression was adopted to obtain the most accurate CR COVID-19 pattern, and sensitivity and specificity were computed. The newly defined pattern was compared to BSTI criteria.

resultsCR of 274 patients were evaluated (146 COVID-19, 128 non-COVID-19). The most accurate COVID-19 pneumonia pattern consisted of four features: bilateral alterations (Expß=2.8,

conclusionBilaterality, peripheral distribution of the predominant lung alteration, no pleural effusion, and perihilar vessels contour not blurred determine the most accurate COVID-19 pneumonia pattern. Lower field involvement, proposed by BSTI criteria, was not a distinctive finding. The BSTI criteria has lower specificity.

Indexed as

COVID-19Pleural EffusionDiagnosis, DifferentialHumansLungRadiographyRadiography, ThoracicRetrospective StudiesSARS-CoV-2Tomography, X-Ray ComputedCOVID 19diagnostic X-raydifferential diagnosispneumonitisSARS-CoV-2 virus

Identifiers

PMID34779269
PMCPMC9659507

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