Abstract
Introduction: Early diagnosis of COVID-19 is important for disease treatment and management. Computed Tomography (CT) is a fast and easy modality for diagnosis and management plan of patients with COVID-19. In the literature, several studies were done to assess the sensitivity of CT for diagnosis of COVID-19 infection in comparison to Reverse Transcription Polymerase Chain Reaction (RT-PCR). Some studies stated that CT was more sensitive diagnostic modality for COVID-19 than RT-PCR. However, the sensitivity of CT for COVID-19 varies in these studies.
Aim: This literature review and meta-analysis was designed to determine the CT features of COVID-19 pneumonia, to verify the pooled sensitivity of CT for the diagnosis of COVID-19 and to review the different reasons (e.g., the disease stage or severity and the negative or positive RT-PCR results) for the variations in CT sensitivity.
Materials and Methods: This review analysed 31 articles selected from the Europe BMC, PubMed, Science Direct, and Scopus databases. Participant gender mean and median age, CT features of COVID-19 pneumonia were sought for and reviewed. The data was analysed using Microsoft excel version 10 and OpenMeta (Analyst) software (http://www.cebm. brown.edu/openmeta/) to verify the pooled sensitivity of CT in detection and diagnosis of COVID-19 pneumonia using meta-analyses forest plot, Receiver Operating Characteristic (ROC) curve, cumulative meta-analyses forest plot and leave one forest plot.
Results: The most common CT findings of COVID-19 pneumonia were bilateral lung involvement, Ground Glass Opacity (GGO), and consolidation, and Crazy-paving pattern. The CT finding is more prominent in symptomatic and severe cases than in a symptomatic and mild cases specifically the presence of consolidation and peripherals lesion distribution. The pooled sensitivity of CT is 90% in diagnosis and detection of COVID-19 pneumonia (ranged 60-100%).
Conclusion: Combination of CT chest and laboratory tests along with clinical manifestation and epidemiological features should be considered to confirm the final diagnosis of COVID-19 pneumonia.