Categories
Nevin Manimala Statistics

Auxiliary screening COVID-19 by computed tomography

Front Public Health. 2023 Jun 5;11:974542. doi: 10.3389/fpubh.2023.974542. eCollection 2023.

ABSTRACT

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.

PMID:37342278 | PMC:PMC10278544 | DOI:10.3389/fpubh.2023.974542

By Nevin Manimala

Portfolio Website for Nevin Manimala