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Review
. 2021;9(4):341-351.
doi: 10.1007/s40336-021-00434-z. Epub 2021 May 26.

Chest computed tomography as a primary tool in COVID-19 detection: an update meta-analysis

Caishuang Pang  1 , Qingtao Hou  2 , Zhaowei Yang  1 , Liwei Ren  1
Affiliations
Review

Chest computed tomography as a primary tool in COVID-19 detection: an update meta-analysis

Caishuang Pang et al. Clin Transl Imaging. 2021.

Abstract

Purpose: A growing number of publications have paid close attention to the chest computed tomography (CT) detection of COVID-19 with inconsistent diagnostic accuracy, the present meta-analysis assessed the available evidence regarding the overall performance of chest CT for COVID-19.

Methods: 2 × 2 diagnostic table was extracted from each of the included studies. Data on specificity (SPE), sensitivity (SEN), negative likelihood ratio (LR-), positive likelihood ratio (LR+), and diagnostic odds ratio (DOR) were calculated purposefully.

Results: Fifteen COVID-19 related publications met our inclusion criteria and were judged qualified for the meta-analysis. The following were summary estimates for diagnostic parameters of chest CT for COVID-19: SPE, 0.49 (95% CI 46-52%); SEN, 0.94 (95% CI 93-95%); LR-, 0.15 (95% CI 11-20%); LR+, 1.93 (95% CI 145-256%); DOR, 17.14 (95% CI 918-3199%); and the area under the receiver operating characteristic curve (AUC), 0.93.

Conclusion: Chest CT has high SEN, but the SPE is not ideal. It is highly recommended to use a combination of different diagnostic tools to achieve sufficient SEN and SPE. It should be taken into account as a diagnostic tool for current COVID-19 detection, especially for patients with symptoms.

Supplementary information: The online version contains supplementary material available at 10.1007/s40336-021-00434-z.

Keywords: COVID-19 detection; Chest CT; Meta-analysis; RT-PCR.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Studies selection process for the meta-analysis
Fig. 2
Fig. 2
Summary of QUADAS-2 assessments of included studies. QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies-2
Fig. 3
Fig. 3
Forest plot of the summary sensitivity and specificity of pleural effusion. The sensitivity/specificity of individual study is represented by a circle, through which runs a horizontal line (95% CI). The diamond at the bottom represents the pooled sensitivity/specificity from the studies. df degrees of freedom
Fig. 4
Fig. 4
Summary receiver operating characteristic (SROC) curve of pleural effusion. AUC area under the curve
Fig. 5
Fig. 5
Deeks funnel plot to assess the likelihood of publication bias
See this image and copyright information in PMC

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