Prognostic factors for mortality, ICU, MIS-C and hospital admission due to SARS-CoV-2 in paediatric patients: A systematic review and meta-analysis
Vardavas, C. I.; Nikitara, K.; Mathioudakis, A.; Delialis, D.; Marou, V.; Ramesh, N.; Stamatelopoulos, K.; Georgiopoulos, G.; Phalkey, R.; Leonardi-Bee, J.; Deogan, C.; Lamb, F.; Mougkou, A.; Pharris, A.; Suk, J. E.
Show abstract
BackgroundThere is a paucity of data on the factors associated with severe COVID-19 disease, especially in children. This systematic review and meta-analysis aim to identify the risk factors for acute adverse outcomes of COVID-19 within paediatric populations, using the recruitment setting as a proxy of initial disease severity. MethodsA systematic review and meta-analysis were performed representing published evidence from the start of the pandemic up to 14 February 2022. Our primary outcome was the identification of risk factors for adverse outcomes, stratified by recruitment setting (community, hospital). No geographical restrictions were imposed. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to evaluate the certainty in the body of evidence for each meta-analysis. In anticipation of significant clinical and methodological heterogeneity in the meta-analyses, we fitted logistic regression models with random effects. FindingsOur review identified 47 studies involving 94,210 paediatric cases of COVID-19. Infants up to 3 months were more likely to be hospitalised than older children. Gender and ethnicity were not associated with an increased likelihood of adverse outcomes among children within the community setting. Concerning comorbidities, having at least one pre-existing disease increased the odds of hospitalisation. Concerning BMI, underweight children and severely obese were noted to have an increased likelihood of hospital admission. The presence of metabolic disorders and children with underlying cardiovascular diseases, respiratory disorders, neuromuscular disorders and neurologic conditions were also more likely to be hospitalised. Concerning underlying comorbidities, paediatric hospitalised patients with congenital/genetic disease, those obese, with malignancy, cardiovascular diseases and respiratory disease were associated with higher odds of being admitted to ICU or ventilated. InterpretationOur findings suggest that age, male, gender, and paediatric comorbidities increased the likelihood of hospital and ICU admission. Obesity, malignancy, and respiratory and cardiovascular disorders were among the most important risk factors for hospital and ICU admission among children with COVID-19. The extent to which these factors were linked to actual severity or where the application of cautious preventive care is an area in which further research is needed.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Risks for death after admission to pediatric intensive care (PICU) - a comparison with the general population 95%
- Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis 95%
- Clinical profile and factors associated with COVID-19 in Cameroon: a prospective cohort study 94%
Similar papers in this journal
- Risk factors for severe outcomes of COVID-19: a rapid review 94%
- Risk factors for SARS-CoV-2 infection and hospitalisation in children and adolescents in Norway: A nationwide population-based study. 94%
- Prevalence and determinants of persistent symptoms after infection with SARS-CoV-2: Protocol for an observational cohort study (LongCOVID-study) 93%
Similar papers in this journal
- Covid-19 Incidence And Mortality By Age Strata And Comorbidities In Mexico City: A Focus In The Pediatric Population 93%
- Gender differences in patients with COVID-19: Focus on severity and mortality 91%
- Long term impact on lung function of patients with moderate and severe COVID-19. A prospective cohort study 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.