TREATMENTS for MIS-C in CHILDREN-DISCHARGE, FEVER and SECOND-LINE THERAPIES

Background: We aimed to analyse the effects of steroids, intravenous immunoglobulin (IVIG), and their combination on the probability of discharge over time, probability of switching to second-line treatment over time, and persistent fever after 2 days of treatment. Methods: We did a retrospective study to investigate the effect of treatments (IVIG plus steroids, steroids alone or IVIG alone) of children with MIS-C in a nationwide study, from 1 March to 1 June 2021. We used a Markovian multi-state model with the clock-forward approach and unidirectional arrows to build a multi-state model. Three transitions were defined: initiation of treatment to hospital discharge (t1), initiation of treatment to second-line therapy (t2), and second-line therapy to hospital discharge (t3). A treatment was considered as second-line if initiated >2 days after the first therapy. We estimated the time-to-event probability using a Cox model weighted by the propensity score to balance the baseline characteristics. Results: 30/132 (22.7%) patients were initially treated with steroids alone, 29/132 (21.9%) with IVIG alone, and 73/132 (55%) with IVIG plus steroids. The probability of early discharge was higher with IVIG than with IVIG plus steroids (hazard ratio [HR] 1.65, 95% CI 1.11-2.45, p=0.013), but with a higher probability of needing second-line therapy versus IVIG plus steroids (HR 3.05, 95% CI 1.12-8.25, p=0.028). Patients on steroids had a lower probability of persistent fever after 2 days of treatment (odds ratio [OR] 0.55, 95% CI, 0.28-1.05, p=0.081) versus patients on IVIG plus steroids, and those on the combination had with a lower probability versus IVIG alone (OR 0.21, 95% CI, 0.09-0.46, p=0.0001). We also directly compared the IVig-and steroid-alone treatments. The probability of early discharge of the patients on steroids and on IVig were not different (HR 0·58, 95% CI 0·27-1·24, p=0·166). The probability of transition second-line therapy was also similar (HR 0·71, 95% CI 0·29-1·74, p=0·456). IVIG had a 4-fold higher probability of persistent fever after treatment initiation than steroids (OR 4·23 95% CI 1·43-13·5, p=0·011). Conclusion: IVIG seemed to increase the probability of discharge over time but increased the probability of needing second-line treatment over time. Steroids seemed to reduce persistent fever after 2 days of treatment, and combination therapy reduced the need for escalating treatment.

COVID-19 in children and young adults with moderate/severe inborn errors of immunity in a high burden area in pre-vaccine era.

BACKGROUND: Information regarding inborn error of immunity (IEI) as a risk factor for severe COVID-19 is scarce. We aimed to determine if paediatric patients with moderate/severe IEI got COVID-19 at the same level as the general population, and to describe COVID-19 expression. MATERIAL AND METHODS: We included patients with moderate/severe IEI aged 0-21 years old: cross-sectional study (June2020) to determine the prevalence of COVID-19; prospective study (January2020-January2021) including IEI patients with COVID-19. Assays used: nasopharyngeal swab SARS-CoV-2 PCR and SARS-CoV-2-specific immunoglobulins. RESULTS: Seven from sixty-five patients tested positive (prevalence: 10.7% (7%-13%)) after the first SARS-COV-2 wave and 13/15 patients diagnosed with COVID-19 had an asymptomatic/mild course. CONCLUSIONS: In our area, prevalence of COVID-19 in moderate/severe IEI paediatric patients after the first wave was slightly higher than in the general population. The majority of patients presented a benign course, suggesting a possible protective factor related with age despite IEI.

Clinical syndromes caused by covid-19 and a bayesian model to predict severity

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.

COVID-19 and Africa: Surviving between a rock and a hard place

With over 575,000 deaths and about 13.3 million cases globally, the COVID-19 pandemic has had a terrible impact globally during the 6 months since cases were first detected in China. Conscious of the many challenges presented in settings with abundance of resources and with robust health systems, where mortality has been significant and transmission difficult to control, there was a logical concern to see how the virus could impact African countries, and their fragile and weak health systems. Such an anticipated "tsunami", with potentially devastating consequences, seems however to not have yet arrived, and African countries, albeit witnessing an increasing degree of autochthonous transmission, seem to this day relatively unaffected by the pandemic. In this article we review the current situation of the pandemic in the African continent, trying to understand the determinants of its slow progress. (C) 2020 Published by Elsevier Espana, S.L.U. on behalf of Asociacion Espanola de Pediatria. This is an open access article under the CC BY-NC-ND license.