Pulmonary computed tomographic manifestations of COVID-19 in vaccinated and non-vaccinated patients
Abstract: This study aimed to analyze computed tomographic (CT) imaging features of vaccinated and non-vaccinated COVID-19 patients. The study population of this retrospective single-center cohort study consisted of hospitalized COVID-19 patients who received a chest CT at the study site between July 2021 and February 2022. Qualitative scoring systems (RSNA, CO-RADS, COV-RADS), imaging pattern analysis and semi-quantitative scoring of lung changes were assessed. 105 patients (70,47% male, 62.1 ± 16.79 years, 53.3% fully vaccinated) were included in the data analysis. A significant association between vaccination status and the presence of the crazy-paving pattern was observed in univariate analysis and persisted after step-wise adjustment for possible confounders in multivariate analysis (RR: 2.19, 95% CI: [1.23, 2.62], P = 0.024). Scoring systems for probability assessment of the presence of COVID-19 infection showed a significant correlation with the vaccination status in univariate analysis; however, the associations were attenuated after adjustment for virus variant and stage of infection. Semi-quantitative assessment of lung changes due to COVID-19 infection revealed no association with vaccination status. Non-vaccinated patients showed a two-fold higher probability of the crazy-paving pattern compared to vaccinated patients. COVID-19 variants could have a significant impact on the CT-graphic appearance of COVID-19
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Scientific reports. - 13, 1 (2023) , 6884, ISSN: 2045-2322
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
-
Universität
- (wann)
-
2023
- DOI
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10.1038/s41598-023-33942-1
- URN
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urn:nbn:de:bsz:25-freidok-2360444
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:50 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Askani, Esther Adele
- Müller-Peltzer, Katharina
- Madrid, Julian
- Knoke, Marvin
- Hasic, Dunja
- Schlett, Christopher L.
- Bamberg, Fabian
- Agarwal, Prerana
- Universität
Entstanden
- 2023
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