Cancer Research, Statistics, and Treatment (Jan 2021)
Assessment of COVID-19 severity using computed tomography imaging: A systematic review and meta-analysis
Abstract
Computed tomography (CT) imaging has been reported to be a reliable tool for the evaluation of suspected cases and follow-up of confirmed cases of coronavirus disease 2019 (COVID-19). Despite the generation of a considerable amount of imaging data related to COVID-19, there is a need for an updated systematic review and meta-analysis pertaining to the questions of clinical significance. We aimed to analyze the correlation between abnormal chest CT findings and disease severity in patients with COVID-19. We searched for case series/studies published in the English language until March 24, 2020 that reported the clinical and chest CT imaging features of confirmed cases of COVID-19 in the PubMed database. A total of 208 studies were screened, and 71 were finally included in the meta-analysis. Study characteristics and relative risk (RR) estimates were extracted from each article and pooled using the random-effects meta-analysis model. There were a total of 6406 patients studied in a total of 71 studies; the male to female ratio was 1.08:1, and the mean age was 45.76 years; of these, 2057 patients from 14 studies were categorized into severe (24.3%) and mild (75.7%) disease groups. Imaging features that were more frequently noted in patients with severe disease than in those with mild disease included bilateral lung involvement (88.7% vs. 49.8%), scattered distribution (80.4% vs. 46.5%), multiple lobe involvement (95.7% vs. 59.6%), consolidation (88.3% vs. 60.3%), crazy-paving pattern (45.4% vs. 27.6%), air-bronchogram sign (29.7% vs. 15.1%), interlobular septal thickening (84.2% vs. 55.8%), and subpleural line (36.8% vs. 26.4%) differences between the two disease groups were statistically significant (P < 0.001). For 3778 patients in 29 studies, a significant pooled RR estimate was associated with abnormal chest CT findings in patients with COVID-19 (RR, 5.46%; 95% confidence interval [CI], 3.72%–8.04%; I2 = 86%). Individual assessment of the CT features revealed that a significant pooled RR estimate was associated with pure ground-glass opacity (GGO) (RR, 1.63%; 95% CI, 1.12%–2.38%; I2 = 79%), while lower pooled RR estimates were associated with CT features like crazy-paving pattern (RR, 1.37%; 95% CI, 1.10%–1.71%; I2 = 60%), consolidation (RR, 0.47%; 95% CI, 0.32%–0.7%; I2 = 83.5%), GGO with consolidation (RR, 0.73%; 95% CI, 0.52%–1.02%; I2 = 75%), and air-bronchogram sign (RR, 0.58%; 95% CI, 0.36%–0.96%; I2 = 94%). In conclusion, the number, location, extent, and type of radiological lesions are associated with COVID-19 progression and severity, suggesting the feasibility of using CT imaging in the assessment of disease severity in all age groups and efficient allocation of resources for patient management at the institutional level.
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