ArticlePloS one2022
Development and validation of a clinical predictive model for severe and critical pediatric COVID-19 infection.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- A simple, rapid, and cost-effective model for predicting critical influenza a infection in children: a multicentre, retrospective cohort study.BMC pediatrics · 2025Article
- Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.Scientific reports · 2025Article
- Hemoglobin and Its Relationship with Fatigue in Long-COVID Patients Three to Six Months after SARS-CoV-2 Infection.Biomedicines · 2024Article
- Hematocrit and Albumin Levels at Admission Predict in-Hospital Mortality in Pediatric COVID-19 Omicron Variant Patients.Infection and drug resistance · 2024Article
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Authors and funding
14 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionChildren infected with COVID-19 are susceptible to severe manifestations. We aimed to develop and validate a predictive model for severe/ critical pediatric COVID-19 infection utilizing routinely available hospital level data to ascertain the likelihood of developing severe manifestations.
methodsThe predictive model was based on an analysis of registry data from COVID-19 positive patients admitted to five tertiary pediatric hospitals across Asia [Singapore, Malaysia, Indonesia (two centers) and Pakistan]. Independent predictors of severe/critical COVID-19 infection were determined using multivariable logistic regression. A training cohort (n = 802, 70%) was used to develop the prediction model which was then validated in a test cohort (n = 345, 30%). The discriminative ability and performance of this model was assessed by calculating the Area Under the Curve (AUC) and 95% confidence interval (CI) from final Receiver Operating Characteristics Curve (ROC).
resultsA total of 1147 patients were included in this analysis. In the multivariable model, infant age group, presence of comorbidities, fever, vomiting, seizures and higher absolute neutrophil count were associated with an increased risk of developing severe/critical COVID-19 infection. The presence of coryza at presentation, higher hemoglobin and platelet count were associated with a decreased risk of severe/critical COVID-19 infection. The AUC (95%CI) generated for this model from the training and validation cohort were 0.96 (0.94, 0.98) and 0.92 (0.86, 0.97), respectively.
conclusionThis predictive model using clinical history and commonly used laboratory values was valuable in estimating the risk of developing a severe/critical COVID-19 infection in hospitalized children. Further validation is needed to provide more insights into its utility in clinical practice.
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