To determine the overall rate of chest imaging findings in asymptomatic cases, describe the most common patterns found, and determine the rate of later symptom development in these initially asymptomatic cases. MATERIALS AND METHODS: The PubMed and EMBASE databases were searched until 1 May 2020, for studies examining the proportion of positive chest imaging findings in asymptomatic cases diagnosed with COVID-19 and a random-effects meta-analysis of proportions was performed. Heterogeneity was assessed using the I 2 statistic. RESULTS: Among 858 non-duplicate studies, seven studies with a total of 231 asymptomatic cases met the inclusion criteria. In the primary analysis, the pooled estimate of the overall rate of positive chest computed tomography (CT) findings among asymptomatic cases was 63% (95% confidence interval [CI]: 44e78%). Among 155/231 cases that were followed up for later symptom development, 90/155 remained asymptomatic and 65/155 developed symptoms during the study period (that ranged between seven and 30 days of follow-up). The pooled estimate of the rate of positive chest CT findings was 62% (95% CI: 38e81%) in cases that remained asymptomatic, while it was 90% (95% CI: 49e99%) in cases that developed symptoms. Among CT findings, the pooled estimate of the overall rate of ground-glass opacities (GGO) at CT alone was 71% (95% CI: 50e86%). Among other CT findings reported, 22/231 patients had GGO with consolidation, 7/231 patients had stripe shadows with or without GGO, and 8/231 patients had GGO with interlobular septal thickening. Among initially asymptomatic cases with positive CT findings, the pooled estimate of the overall rate of later symptom development was 26% (95% CI: 14e43%). CONCLUSION: In COVID-19, asymptomatic cases can have positive chest CT findings, and COVID-19 should be considered among cases with CT abnormalities even when there are no other symptoms. There is a need for close clinical monitoring of asymptomatic cases with radiographic findings as a significant percentage will develop symptoms.
Vaccination remains the most effective way to prevent COVID-19. The aim of the present study was to assess the incidence of COVID-19 hospitalizations after vaccination, as well as the effect of prior vaccination on hospitalization outcomes among patients with COVID-19. We analyzed and compared all consecutive patients, with or without prior vaccination, who were admitted to our hospital network due to COVID-19 from January to April 2021. Our primary outcome was to identify and describe cases of COVID-19 hospitalized after vaccination. We also utilized a multivariate logistic regression model to investigate the association of previous vaccination with hospitalization outcomes. We identified 915 consecutive patients hospitalized due to COVID-19 with 91/915 (10%) previously vaccinated with at least one dose of a COVID-19 vaccine. Utilizing our multivariate logistic regression model, we found that prior vaccination, regardless of the number of doses or days since vaccination, was associated with decreased mortality (aOR 0.44, 95% CI: 0.20–0.98) when compared to unvaccinated individuals. Our study showed that COVID-19 related hospitalization after vaccination may occur to a small percentage of patients, mainly those who are partially vaccinated. However, our findings underline that prior vaccination, even when partial, is associated with a decreased risk of death. Ongoing vaccination efforts should remain an absolute priority.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.