Tomography · 2022 · 14 citations · 21 references
MedicineInfectious DiseasesRadiologyDiagnostic ImagingDiagnostic RadiologyVirologyInfection ImagingInfectious Disease DetectionPulmonary ImagingRespiratory InfectionsRespiratory Infectious DiseasesViral InfectionsChest Radiography In InfectionCohort Study InvestigationInfectious Disease FindingsInfectious Disease ImagingInfectious Lung Disease ImagingLung Infection ImagingPneumonia DiagnosisTomographic Imaging
Background: The aim of this study was to evaluate CT (computed tomography) imaging differences for the Delta and the Omicron variant in COVID-19 infection. Methods: The study population was derived from a retrospective study cohort investigating chest CT imaging patterns in vaccinated and nonvaccinated COVID-19 patients. CT imaging patterns of COVID-19 infection were evaluated by qualitative and semiquantitative scoring systems, as well as imaging pattern analysis. Results: A total of 60 patients (70.00% male, 62.53 ± 17.3 years, Delta: 43 patients, Omicron: 17 patients) were included. Qualitative scoring systems showed a significant correlation with virus variants; “typical appearance” and “very high” degrees of suspicion were detected more often in patients with Delta (RSNA: p = 0.003; CO-RADS: p = 0.002; COV-RADS: p = 0.001). Semiquantitative assessment of lung changes revealed a significant association with virus variants in univariate (Delta: 6.3 ± 3.5; Omicron: 3.12 ± 3.2; p = 0.002) and multivariate analysis. The vacuolar sign was significantly associated with the Delta variant (OR: 14.74, 95% CI: [2.32; 2094.7], p = 0.017). Conclusion: The Delta variant had significantly more extensive lung involvement and showed changes classified as “typical” more often than the Omicron variant, while the Omicron variant was more likely associated with CT findings such as “absence of pulmonary changes”. A significant correlation between the Delta variant and the vacuolar sign was observed.
21
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
A Novel Coronavirus from Patients with Pneumonia in China, 2019
Na Zhu, Dingyu Zhang, Xingwang Li et al. · New England Journal of Medicine · 2020 · 30.4K citations · Full text
WHO Declares COVID-19 a Pandemic.
Domenico Cucinotta, Maurizio Vanelli · PubMed · 2020 · 7.8K citations · Full text
Public Health, Infectious Disease Control, Infectious Diseases +17
Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies
Yunlong Cao, Jing Wang, Fanchong Jian et al. · Nature · 2021 · 2.1K citations · Full text