Auxiliary screening COVID-19 by computed tomography.

Background: The 2019 novel coronavirus (COVID-19) pandemic remains rampant in many countries/regions. Improving the positive detection rate of COVID-19 infection is an important measure for the control and prevention of this pandemic. This meta-analysis aims to systematically summarize the current characteristics of the computed tomography (CT) auxiliary screening methods for COVID-19 infection in the real world.
Methods: Web of Science, Cochrane Library, Embase, PubMed, CNKI, and Wanfang databases were searched for relevant articles published prior to 1 September 2022. Data on specificity, sensitivity, positive/negative likelihood ratio, area under curve (AUC), and diagnostic odds ratio (dOR) were calculated purposefully.
Results: One hundred and fifteen studies were included with 51,500 participants in the meta-analysis. Among these studies, the pooled estimates for AUC of CT in confirmed cases, and CT in suspected cases to predict COVID-19 diagnosis were 0.76 and 0.85, respectively. The CT in confirmed cases dOR was 5.51 (95% CI: 3.78-8.02). The CT in suspected cases dOR was 13.12 (95% CI: 11.07-15.55).
Conclusion: Our findings support that CT detection may be the main auxiliary screening method for COVID-19 infection in the real world.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Pan, Chen, Kaminga, Wen, Liu, Jia and Liu.)