Auxiliary screening COVID-19 by computed tomography

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

doi:10.3389/fpubh.2023.974542

Frontiers in Public Health (Jun 2023)

Auxiliary screening COVID-19 by computed tomography

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

Affiliations

Xiongfeng Pan: Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Yuyao Chen: Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Atipatsa C. Kaminga: Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Atipatsa C. Kaminga: Department of Mathematics and Statistics, Mzuzu University, Mzuzu, Malawi
Shi Wu Wen: Obstetrics & Maternal Newborn Investigations Research Group, Ottawa Hospital Research Institute, Ottawa, ON, Canada
Shi Wu Wen: Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
Shi Wu Wen: Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
Hongying Liu: Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
Peng Jia: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Peng Jia: Hubei Luojia Laboratory, Wuhan, China
Peng Jia: School of Public Health, Wuhan University, Wuhan, China
Peng Jia: International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
Aizhong Liu: Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China

DOI: https://doi.org/10.3389/fpubh.2023.974542
Journal volume & issue: Vol. 11

Abstract

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BackgroundThe 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.MethodsWeb 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.ResultsOne 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).ConclusionOur findings support that CT detection may be the main auxiliary screening method for COVID-19 infection in the real world.

Published in Frontiers in Public Health

ISSN: 2296-2565 (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Public aspects of medicine
Website: https://www.frontiersin.org/journals/public-health

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