Predictive factors for COVID-19 severity and mortality in hospitalized children

Abstract

Abstract Background Understanding the factors influencing disease progression and severity in pediatric COVID-19 cases is essential for effective management and intervention strategies. This study aimed to evaluate the discriminative ability of clinical and laboratory parameters to identify predictors of COVID-19 severity and mortality in hospitalized children. Methods In this multicenter retrospective cohort study, we included 468 pediatric patients with COVID-19. We developed a predictive model using their demographic, clinical, and laboratory data. The performance of the model was assessed using various metrics including sensitivity, specificity, positive predictive value rates, and receiver operating characteristics (ROC). Results Our findings demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.818 for severity and 0.873 for mortality prediction. Key risk factors for severe COVID-19 in children include low albumin levels, elevated C-reactive protein (CRP), lactate dehydrogenase (LDH), and underlying medical conditions. Furthermore, ROC curve analysis highlights the predictive value of CRP, LDH, and albumin, with AUC values of 0.789, 0.752, and 0.758, respectively. Conclusion Our study indicates that laboratory values are valuable in predicting COVID-19 severity in children. Various factors, including CRP, LDH, and albumin levels, demonstrated statistically significant differences between patient groups, suggesting their potential as predictive markers for disease severity. Implementing predictive analyses based on these markers could aid clinicians in making informed decisions regarding patient management.

Keywords

• COVID-19
• Prediction
• Severity
• Mortality
• Children