Auxiliary screening COVID-19 by computed tomography

Xiongfeng Pan; Yuyao Chen; Atipatsa C Kaminga; Shi Wu Wen; Hongying Liu; Peng Jia; Aizhong Liu

doi:10.3389/fpubh.2023.974542

Auxiliary screening COVID-19 by computed tomography

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

Authors

Xiongfeng Pan¹, Yuyao Chen¹, Atipatsa C Kaminga^{1

2}, Shi Wu Wen^{3

4

5}, Hongying Liu¹, Peng Jia^{6

7

8

9}, Aizhong Liu¹

Affiliations

¹ Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
² Department of Mathematics and Statistics, Mzuzu University, Mzuzu, Malawi.
³ Obstetrics & Maternal Newborn Investigations Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
⁴ Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
⁵ Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
⁶ School of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
⁷ Hubei Luojia Laboratory, Wuhan, China.
⁸ School of Public Health, Wuhan University, Wuhan, China.
⁹ International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China.

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.

Keywords: COVID-19; computed tomography; cross-disciplinary methods; novel coronavirus; nucleic acid detection; real world.

Publication types

Meta-Analysis
Systematic Review
Research Support, Non-U.S. Gov't

MeSH terms

COVID-19 Testing
COVID-19*
Humans
SARS-CoV-2
Sensitivity and Specificity
Tomography, X-Ray Computed

Grants and funding

This work was supported by the National Natural Science Foundation of China (42271433), Jiangxi Provincial 03 Special Foundation and 5G Program (20224ABC03A05), Wuhan University Specific Fund for Major School-level Internationalization Initiatives (WHU-GJZDZX-PT07), and the International Institute of Spatial Lifecourse Health (ISLE).