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CT manifestations and clinical characteristics of 1115 patients with coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis

Authors :
Mingqi Li
Zheng Ye
Shang Wan
Shaofeng Duan
Qian Cai
Bin Song
Caiwei Yang
Source :
Academic Radiology
Publication Year :
2020
Publisher :
Published by Elsevier Inc. on behalf of The Association of University Radiologists., 2020.

Abstract

Rationale and Objectives We aimed to assess the prevalence of significant computed tomographic(CT) manifestations and describe some notable features based on chest CT images, as well as the main clinical features of patients with coronavirus disease 2019(COVID-19). Materials and Methods A systematic literature search of PubMed, EMBASE, the Cochrane Library, and Web of Science was performed to identify studies assessing CT features, clinical, and laboratory results of COVID-19 patients. A single-arm meta-analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI). Results A total of 14 articles (including 1115 patients) based on chest CT images were retrieved. In the lesion patterns on chest CTs, we found that pure ground-glass opacities (GGO) (69%, 95% CI 58–80%), consolidation (47%, 35–60%) and “air bronchogram sign” (46%, 25–66%) were more common than the atypical lesion of “crazy-paving pattern” (15%, 8–22%). With regard to disease extent and involvement, 70% (95% CI 46–95%) of cases showed a location preference for the right lower lobe, 65% (58–73%) of patients presented with ≥3 lobes involvement, and meanwhile, 42% (32–53%) of patients had involvement of all five lobes, while 67% (55–78%) of patients showed a predominant peripheral distribution. An understanding of some important CT features might be helpful for medical surveillance and management. In terms of clinical features, muscle soreness (21%, 95% CI 15–26%) and diarrhea (7%, 4–10%) were minor symptoms compared to fever (80%, 74–87%) and cough (53%, 33–72%). Conclusion Chest CT manifestations in patients with COVID-19, as well as its main clinical characteristics, might be helpful in disease evolution and management.

Details

Language :
English
ISSN :
18784046 and 10766332
Database :
OpenAIRE
Journal :
Academic Radiology
Accession number :
edsair.doi.dedup.....5dae307d46d2b2905b99ee19cf89d349