Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Wong JJM

PROVIDER: S-EPMC9612577 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Wong Judith Ju Ming JJM Abbas Qalab Q Liauw Felix F Malisie Ririe Fachrina RF Gan Chin Seng CS Abid Muhammad M Efar Pustika P Gloriana Josephine J Chuah Soo Lin SL Sultana Rehena R Thoon Koh Cheng KC Yung Chee Fu CF Lee Jan Hau JH

PloS one 20221027 10

<h4>Introduction</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 36301941

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Project description:BackgroundImmune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission.MethodsWe retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset.ResultsSix features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance.ConclusionsThe developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients.Trial registrationThis study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.

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Dataset Information

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

Introduction

Methods

Results

Conclusion

Publications

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

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