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

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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… Click to show full abstract

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 with 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: 1115 patients; disease 2019; patients coronavirus; 2019 covid; coronavirus disease; meta analysis

Journal Title: Academic Radiology
Year Published: 2020

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