BACKGROUND: We prepared a meta-analysis on case reports in children with COVID-19, aiming to identify potential risk factors for severe illness and to develop a prediction model for risk assessment .
METHODS: Literature retrieval, case report selection, and data extraction were independently completed by two authors . STATA software (version 14.1) and R programming environment (v4.0.2) were used for data handling .
RESULTS: This meta-analysis was conducted based on 52 case reports, including 203 children (96 boys) with COVID-19 . By severity , 26 (12.94 %), 160 (79.60 %), and 15 (7.46 %) children were diagnosed as asymptomatic, mild/moderate, and severe cases, respectively . After adjusting for age and sex , 11 factors were found to be significantly associated with the risk of severe illness relative to asymptomatic or mild/moderate illness, especially for dyspnea/tachypnea (odds ratio , 95% confidence interval, P : 6.61 , 4.12-9.09, <0.001) and abnormal chest X-ray (3.33 , 1.84-4.82, <0.001). A nomogram modeling age, comorbidity, cough, dyspnea or tachypnea, CRP, and LDH was developed, and prediction performance was good as reflected by the C-index .
CONCLUSIONS: Our findings provide systematic evidence for the contribution of comorbidity, cough, dyspnea or tachypnea, CRP, and LDH, both individually and jointly, to develop severe symptoms in children with asymptomatic or mild/moderate COVID-19. IMPACT: We have identified potential risk factors for severe illness in children with COVID-19 . We have developed a prediction model to facilitate risk assessment in children with COVID-19 . We found the contribution of five risk factors to develop severe symptoms in children with asymptomatic or mild/moderate COVID-19.