ABSTRACT
Background: This study aimed to identify the different syndromes presented in hospitalized children with SARS-CoV-2, to analyze if the clinical features and biomarkers confer different risk depending on the syndromes, and to create a predictive model to anticipate the probability of the need for critical care Methods: We conducted a multicenter, prospective study of children aged 0 to 18 years old with SARS-CoV-2 infection in 52 Spanish hospitals. The primary outcome was the need for critical care: defined as the combined outcome of admission into a PICU, and/or need for respiratory support beyond nasal prongs. To understand the probability of needing critical care according to the diagnostic group and for each risk factor, a Bayesian multivariable model was applied. To build a predictive model of critical care, a naïve Bayes algorithm was implemented in a web app. Results: 292 children were hospitalized from March 12th, 2020 to July 1st, 2020;Of them, 214 (73.3%) were considered to have relevant COVID-19 (r-COVID-19). Among patients with r-COVID-19, 24.2% needed critical care. Out of 214 patients, 22.4% were admitted into a pediatric intensive care unit, 41.6% required respiratory support, and 38.8% presented complications (mostly cardiological). Four patients (1.8%) died, all of them had severe comorbidities. We identified 11 primaries diagnoses and grouped them into 4 large syndromes of decreasing severity: MIS-C (17.3%), bronchopulmonary (51.4%), gastrointestinal (11.6%), and mild syndrome with complications (19.6%). In the predictive model, the predictors with higher relative importance were high C-reactive protein, anemia, lymphopenia, platelets <220 000/mm3, type of syndrome, high creatinine, and days of fever. The different risk factors increase the risk differently depending on the patient's syndrome: the more severe the syndrome, the more risk the factor confers. We developed an online risk prediction tool to quantify the risk of critical disease (https://rserver.h12o. es/pediatria/EPICOAPP/, username: user, password:0000) Conclusion: We described the spectrum of r-COVID-19 in hospitalized children, consisting of 4 large syndromes of decreasing severity: MIS-C, bronchopulmonary syndrome, gastrointestinal syndrome, and a mild syndrome with complications. The risk factors increase the risk differently depending on the syndrome. A Bayesian model was implemented in an online app to anticipate the individual risk of critical care.