CT Manifestations and Clinical Characteristics of 1115 Patients with Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis

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.

Keywords: COVID-19; CT manifestations; Clinical features; Laboratory; Meta-analysis.

Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

Flow diagram of the study selection process.

Figure 2

Forest plots of the incidence of lesion patterns on chest CT images in COVID-19 cases. A, B, C, and D represent the prevalence of pure GGO, consolidation (with or without GGO), “air bronchogram sign”, and “crazy-paving pattern”, respectively.

Figure 3

Forest plots of the incidence of extent and involvement in COVID-19 patients. A, B, C, and D represent the right lower lobe involvement, three or more lobes involvement (lobes ≥3), all five lobes involvement, and peripheral distribution, respectively.

Figure 4

(a). The sensitivity analysis for the feature of five lobes involvement. The sensitivity analysis investigates the influence of each individual study on the overall meta-analysis summary estimate, presenting a forest plot of the results of an influence analysis in which the meta-analysis is re-estimated after omitting each study in turn. The full, “combined” results are shown as the solid vertical lines and the influence of each study is defined as a point estimate. An individual study is suspected of excessive influence if the point estimate of its “omitted” analysis lies outside the confidence interval of the “combined” analysis or it is far away from the solid vertical lines. Some attention should be paid to potential reasons for its excessive influence. (b). Forest plot of the re-estimated prevalence of the incidence for this feature in COVID-19 patients.

Figure 5

(a). The sensitivity analysis of the feature of three or more lobes involvement (lobes ≥3), (b). Forest plot of re-estimated prevalence of the incidence for this feature in COVID-19 patients.