Chest CT for detecting COVID-19: a systematic review and meta-analysis of diagnostic accuracy

Buyun Xu1, Yangbo Xing1, Jiahao Peng2

  • 1Department of Cardiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), No. 568, Zhongxing North Road, Shaoxing, 312000, Zhejiang Province, People's Republic of China.

European Radiology
|May 17, 2020
PubMed

Insights

Chest CT scans show high sensitivity for detecting coronavirus disease 2019 (COVID-19), particularly in severely affected areas. While useful for early identification of suspected cases, specificity remains low.

Area of Science:

  • Radiology
  • Infectious Diseases
  • Diagnostic Accuracy

Background:

  • Coronavirus disease 2019 (COVID-19) presents a global health challenge.
  • Accurate and rapid diagnostic methods are crucial for disease control.
  • Chest computed tomography (CT) is increasingly utilized for COVID-19 detection.

Purpose of the Study:

  • To systematically review and meta-analyze the diagnostic test accuracy of chest CT for COVID-19.
  • To assess the pooled sensitivity and specificity of chest CT in detecting COVID-19.

Main Methods:

  • Systematic literature search of PubMed, Embase, Web of Science, and CNKI up to March 12, 2020.
  • Inclusion of studies reporting diagnostic accuracy of chest CT for COVID-19.
  • Quality assessment using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
  • Pooled analysis of sensitivity and specificity data.

Main Results:

  • Sixteen studies with 3186 patients were included.
  • Pooled sensitivity for chest CT was 92% (95% CI: 86-96%).
  • Specificity varied significantly (25-33%), indicating a notable limitation.
  • Higher sensitivity was observed in Wuhan (96-99%) compared to other regions (61-98%).

Conclusions:

  • Chest CT demonstrates high sensitivity for COVID-19 detection, especially in epidemic hotspots.
  • The low specificity necessitates caution in interpreting results.
  • Chest CT serves as a valuable tool for rapid screening and early recognition of suspected COVID-19 cases during outbreaks.
Abstract

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