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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-318409

ABSTRACT

Background: In clinical practice, the striking similarities observed at computed tomography (CT) between the diseases make it difficult to distinguish a COVID-19 pneumonia from a progression of interstitial lung disease (ILD) secondary to Systemic sclerosis (SSc). The aim of the present study was to identify the main CT features that may help distinguishing SSc-ILD from COVID-19 pneumonia. Methods: This multicentric study included 22 international readers divided in the radiologist group (RAD) and non-radiologist group (nRAD). A total of 99 patients, 52 with COVID-19 and 47 with SSc-ILD, were included in the study.Findings: Fibrosis inside focal ground glass opacities (GGO) in the upper lobes;fibrosis in the lower lobe GGO;reticulations in lower lobes (especially if bilateral and symmetrical or associated with signs of fibrosis) were the CT features most frequently associated with SSc-ILD. The CT features most frequently associated with COVID- 19 pneumonia were: consolidation (CONS) in the lower lobes, CONS with peripheral (both central/peripheral or patchy distributions), anterior and posterior CONS and rounded-shaped GGOs in the lower lobes. After multivariate analysis, the presence of CONS in the lower lobes (p <0.0001) and signs of fibrosis in GGO in the lower lobes (p <0.0001) remained independently associated with COVID-19 pneumonia or SSc-ILD, respectively. A predictive score weas created which resulted positively associated with the COVID-19 diagnosis (96.1% sensitivity and 83.3% specificity).Interpretation: The CT differential diagnosis between COVID-19 pneumonia and SSc-ILD is possible through the combination our score and the radiologic expertise. If an overlap of both diseases is suspected, the presence of consolidation in the lower lobes may suggest a COVID-19 pneumonia while the presence of fibrosis inside GGO may indicate a SSc-ILD.Funding: No Funding were received for this study.Declaration of Interests: SC reports personal fees from NOVARTIS-SANOFI-LILLY-CELTHER-PFIZER-JANSSEN;MK reports grants and personal fees from Boehringer-Ingelheim, personal fees from Corbus, grants and personal fees from Chugai, grants and personal fees from Ono Pharmeceuticals, personal fees from Tanabe-Mitsubishi, personal fees from Astellas, personal fees from Gilead, personal fees from Mochida;ST reports personal fees from Boehringer Ingelheim, personal fees from Roche, outside the submitted work;GS reports personal fees from Boehringer Ingelheim;CB reports personal fees from Actelion, personal fees from Eli Lilly, grants from European Scleroderma Trial and Research (EUSTAR) group, grants from New Horizon Fellowship, grants from Foundation for Research in Rheumatology (FOREUM), grants from Fondazione Italiana per la Ricerca sull'Artrite (FIRA);CV reports grants and personal fees from Boehringer Ingelheim, grants and personal fees from F. Hoffmann-La Roche Ltd.;FL reports lectures fee from Roche and from Boehringer- Ingelheim;CPD reports grants and personal fees from GSK, personal fees from Boerhinger Ingelheim, grants from Servier, grants and personal fees from Inventiva, grants and personal fees from Arxx Therapeutics, personal fees from Corbus, personal fees from Sanofi, personal fees from Roche;FL reports grants and personal fees from GSK, personal fees from Boehringer Ingelheim, personal fees from Orion Pharma, personal fees from AstraZeneca, grants from MSD, personal fees from HIKMA, personal fees from Trudell International, grants and personal fees from Chiesi Farmaceutici, personal fees from Novartis Pharma;MH reports personal fees from Speaking fees from Actelion, Eli lilly and Pfizer;D K reports personal fees from Actelion, grants and personal fees from Bayer, grants and personal fees from Boehringer Ingelhem, personal fees from CSL Behring, grants and personal fees from Horizon, grants from Pfizer, personal fees from Corbus, grants and personal fees from BMS, outside the submitted work;and Dr Khanna is the Chief Medical officer of Eicos Sciences Inc and has s ock options. All the mentioned authors declared previous feed outside the submitted work. All other authors declare no competing interests.Ethics Approval Statement: This retrospective, observational, multicentric, international study was approved by the Institutional Ethics Committee of Florence Careggi hospital (protocol number 17104_oss).

2.
Acta Radiol ; : 2841851211055163, 2021 Nov 13.
Article in English | MEDLINE | ID: covidwho-1511628

ABSTRACT

BACKGROUND: Chest 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 METHODS: CR 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. RESULTS: CR 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, P=0.002), peripheral distribution of the predominant (Expß=2.3, P=0.013), no pleural effusion (Expß=0.4, P=0.009), and perihilar vessels' contour not blurred (Expß=0.3, P=0.002). The pattern showed 49% sensitivity, 81% specificity, and 64% accuracy, while BSTI criteria showed 51%, 77%, and 63%, respectively. CONCLUSION: Bilaterality, 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.

3.
Rheumatology (Oxford) ; 61(4): 1600-1609, 2022 Apr 11.
Article in English | MEDLINE | ID: covidwho-1328934

ABSTRACT

OBJECTIVE: The aim of this study was to identify the main CT features that may help in distinguishing a progression of interstitial lung disease (ILD) secondary to SSc from COVID-19 pneumonia. METHODS: This multicentric study included 22 international readers grouped into a radiologist group (RADs) and a non-radiologist group (nRADs). A total of 99 patients, 52 with COVID-19 and 47 with SSc-ILD, were included in the study. RESULTS: Fibrosis inside focal ground-glass opacities (GGOs) in the upper lobes; fibrosis in the lower lobe GGOs; reticulations in lower lobes (especially if bilateral and symmetrical or associated with signs of fibrosis) were the CT features most frequently associated with SSc-ILD. The CT features most frequently associated with COVID- 19 pneumonia were: consolidation (CONS) in the lower lobes, CONS with peripheral (both central/peripheral or patchy distributions), anterior and posterior CONS and rounded-shaped GGOs in the lower lobes. After multivariate analysis, the presence of CONs in the lower lobes (P < 0.0001) and signs of fibrosis in GGOs in the lower lobes (P < 0.0001) remained independently associated with COVID-19 pneumonia and SSc-ILD, respectively. A predictive score was created that was positively associated with COVID-19 diagnosis (96.1% sensitivity and 83.3% specificity). CONCLUSION: CT diagnosis differentiating between COVID-19 pneumonia and SSc-ILD is possible through a combination of the proposed score and radiologic expertise. The presence of consolidation in the lower lobes may suggest COVID-19 pneumonia, while the presence of fibrosis inside GGOs may indicate SSc-ILD.


Subject(s)
COVID-19 , Lung Diseases, Interstitial , Scleroderma, Systemic , COVID-19/complications , COVID-19/diagnostic imaging , COVID-19 Testing , Fibrosis , Humans , Lung/diagnostic imaging , Lung/pathology , Lung Diseases, Interstitial/complications , Lung Diseases, Interstitial/etiology , Scleroderma, Systemic/complications , Scleroderma, Systemic/diagnostic imaging , Scleroderma, Systemic/pathology , Tomography, X-Ray Computed
4.
J Pers Med ; 11(4)2021 Apr 14.
Article in English | MEDLINE | ID: covidwho-1186995

ABSTRACT

BACKGROUND: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. METHODS: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. RESULTS: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. CONCLUSIONS: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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