Development and validation of a clinical predictive model for severe and critical pediatric COVID-19 infection

Wong, Judith Ju Ming and Abbas, Qalab and Liauw, Felix and Malisie, Ririe Fachrina and Gan, Chin Seng and Abid, Muhammad and Efar, Pustika and Gloriana, Josephine and Chuah, Soo Lin and Sultana, Rehena and Thoon, Koh Cheng and Yung, Chee Fu and Lee, Jan Hau and Grp, PACCMAN Res (2022) Development and validation of a clinical predictive model for severe and critical pediatric COVID-19 infection. PLoS ONE, 17 (10). ISSN 1932-6203, DOI

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Introduction Children 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. Methods The 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). Results A 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. Conclusion This 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.

Item Type: Article
Funders: SingHealth Duke-NUS Global Health Institute Pilot Research Grant Duke-NUS/SDGHI_RGA (Khoo)/2020/0006, SingHealth Duke-NUS Academic Medicine COVID-19 Rapid Response Research Grant AM/COV001/2020, SRDUKAMC2001
Uncontrolled Keywords: Children; Admissions; Risk
Subjects: R Medicine > RJ Pediatrics
R Medicine > RJ Pediatrics > Child health. Child health services
Divisions: Faculty of Medicine > Paediatrics Department
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Oct 2023 04:02
Last Modified: 24 Oct 2023 04:33

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