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Scientific paper - Original scientific paper
Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia
Diagnostics, 14 (2024), 2; 148. https://doi.org/10.3390/diagnostics14020148
Brumini, Ivan; Dodig, Doris; Žuža, Iva; Višković, Klaudija; Mehmedović, Armin; Bartolović, Nina; Šušak, Helena; Cekinović Grbeša, Đurđica; Miletić, Damir

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Brumini, I., Dodig, D., Žuža, I., Višković, K., Mehmedović, A., Bartolović, N. ... Miletić, D. (2024). Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia. Diagnostics, 14. (2). doi: 10.3390/diagnostics14020148

Brumini, Ivan, et al. "Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia." Diagnostics, vol. 14, no. 2, 2024. https://doi.org/10.3390/diagnostics14020148

Brumini, Ivan, Doris Dodig, Iva Žuža, Klaudija Višković, Armin Mehmedović, Nina Bartolović, Helena Šušak, Đurđica Cekinović Grbeša and Damir Miletić. "Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia." Diagnostics 14, no. 2 (2024). https://doi.org/10.3390/diagnostics14020148

Brumini, I., et al. (2024) 'Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia', Diagnostics, 14(2). doi: 10.3390/diagnostics14020148

Brumini I, Dodig D, Žuža I, Višković K, Mehmedović A, Bartolović N, and sur.. Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia. Diagnostics [Internet]. 2024 January 08 [cited 2024 November 29];14(2). doi: 10.3390/diagnostics14020148

I. Brumini, et al., "Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia", Diagnostics, vol. 14, no. 2, January 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:184:041000. [Accessed: 29 November 2024]

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Metadata
Title (english)Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia
AuthorIvan Brumini
AuthorDoris Dodig
AuthorIva Žuža
AuthorKlaudija Višković
AuthorArmin Mehmedović
AuthorNina Bartolović
AuthorHelena Šušak
AuthorĐurđica Cekinović Grbeša
AuthorDamir Miletić
Author's institutionUniversity of Rijeka
Faculty of Medicine
(Department of Radiology)
Author's institutionUniversity of Rijeka
Faculty of Medicine
(Department of Infectious Diseases)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Radiology
Abstract (english)
The aim of our study was to establish and compare the diagnostic accuracy and clinical applicability of published chest CT severity scoring systems used for COVID-19 pneumonia assessment and to propose the most efficient CT scoring system with the highest diagnostic performance and the most accurate prediction of disease severity. This retrospective study included 218 patients with PCR-confirmed SARS-CoV-2 infection and chest CT. Two radiologists blindly evaluated CT scans and calculated nine different CT severity scores (CT SSs). The diagnostic validity of CT SSs was tested by ROC analysis. Interobserver agreement was excellent (intraclass correlation coefficient: 0.982–0.995). The predominance of either consolidations or a combination of consolidations and ground-glass opacities (GGOs) was a predictor of more severe disease (both p < 0.005), while GGO prevalence alone was not. Correlation between all CT SSs was high, ranging from 0.848 to 0.971. CT SS 30 had the highest diagnostic accuracy (AUC = 0.805) in discriminating mild from severe COVID-19 disease compared to all the other proposed scoring systems (AUC range 0.755–0.788). In conclusion, CT SS 30 achieved the highest diagnostic accuracy in predicting the severity of COVID-19 disease while maintaining simplicity, reproducibility, and applicability in complex clinical settings.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleDiagnostics
Numberingvol. 14, no. 2, 148
e-ISSN2075-4418
DOIhttps://doi.org/10.3390/diagnostics14020148
URN:NBNurn:nbn:hr:184:041000
Publication2024-01-08
Document URLhttps://www.mdpi.com/2075-4418/14/2/148
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-01-10 13:19:01