IntroductionThe QuantiFERON-TB Gold Plus (QFT-Plus) assay, which features two antigen-stimulated tubes (TB1 and TB2) instead of a single tube used in previous-generation interferon-gamma release assays (IGRAs), was launched in 2016. Despite this, data regarding the assay’s performance in the paediatric setting remain scarce. This study aimed to determine the performance of QFT-Plus in a large cohort of children and adolescents at risk of tuberculosis (TB) in a low-burden setting.MethodsCross-sectional, multicentre study at healthcare institutions participating in the Spanish Paediatric TB Research Network, including patients <18 years who had a QFT-Plus performed between September 2016 and June 2020.ResultsOf 1726 patients (52.8% male, median age: 8.4 years), 260 (15.1%) underwent testing during contact tracing, 288 (16.7%) on clinical/radiological suspicion of tuberculosis disease (TBD), 649 (37.6%) during new-entrant migrant screening and 529 (30.6%) prior to initiation of immunosuppressive treatment. Overall, the sensitivity of QFT-Plus for TBD (n=189) and for latent tuberculosis infection (LTBI, n=195) was 83.6% and 68.2%, respectively. The agreement between QFT-Plus TB1 and TB2 antigen tubes was excellent (98.9%, κ=0.961). Only five (2.5%) patients with TBD had discordance between TB1 and TB2 results (TB1+/TB2−, n=2; TB1−/TB2+, n=3). Indeterminate assay results (n=54, 3.1%) were associated with young age, lymphopenia and elevated C reactive protein concentrations.ConclusionsOur non-comparative study indicates that QFT-Plus does not have greater sensitivity than previous-generation IGRAs in children in both TBD and LTBI. In TBD, the addition of the second antigen tube, TB2, does not enhance the assay’s performance substantially.

Fever without source (FWS) in infants is a frequent cause of consultation at the emergency department, and the emergence of SARS-CoV-2 could affect the approach to those infants. The aim of this study is to define the clinical characteristics and rates of bacterial coinfections of infants < 90 days with FWS as the first manifestation of SARS-CoV-2 infection. This is a cross-sectional study of infants under 90 days of age with FWS and positive SARS-CoV2 PCR in nasopharyngeal swab/aspirate, attended at the emergency departments of 49 Spanish hospitals (EPICO-AEP cohort) from March 1 to June 26, 2020. Three hundred and thirty-three children with COVID-19 were included in EPICO-AEP. A total of 67/336 (20%) were infants less than 90 days old, and 27/67(40%) presented with FWS. Blood cultures were performed in 24/27(89%) and were negative in all but one (4%) who presented a Streptococcus mitis bacteremia. Urine culture was performed in 26/27(97%) children and was negative in all, except in two (7%) patients. Lumbar puncture was performed in 6/27(22%) cases, with no growth of bacteria. Two children had bacterial coinfections: 1 had UTI and bacteremia, and 1 had UTI. C-reactive was protein over 20 mg/L in two children (one with bacterial coinfection), and procalcitonin was normal in all. One child was admitted to the pediatric intensive care unit because of apnea episodes. No patients died. Conclusion : FWS was frequent in infants under 90 days of age with SARS-CoV-2 infection. Standardized markers to rule out bacterial infections remain useful in this population, and the outcome is generally good. What is Known: • Fever without source (FWS) in infants is a common cause of consultation at the emergency department, and young infants have a higher risk of serious bacterial infections (SBI). • The emergence of the new coronavirus SARS-CoV-2 could affect the approach to young infants with FWS in the emergency department. management of those children is a challenge because information about bacterial coinfection and prognosis is scarce. What is New: • SARS-CoV-2 infection should be ruled out in young infants (< 90 days of age) with FWS in areas with community transmission. • Bacterial coinfection rarely coexists in those infants. • Inflammatory markers were not increased in children without bacterial coinfection. • Outcome is good in most patients.

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In this cross-sectional study of 284 children and adolescents with clinically or radiologically suspected tuberculosis in a low-endemic country, the QuantiFERON-TB Gold Plus assay specificity, sensitivity, positive predictive value and negative predictive value were 91.5%, 87.3%, 86.4%, and 91.2%, respectively. The specificity was higher than that observed in tuberculin skin tests performed simultaneously, but similar to previousgeneration interferon-gamma release assays.

Background:We aimed to identify risk factors causing critical disease in hospitalized children with COVID-19 and to build a predictive model to anticipate the probability of need for critical care. Methods: We conducted a multicenter, prospective study of children with SARS-CoV-2 infection in 52 Spanish hospitals. The primary outcome was the need for critical care. We used a multivariable Bayesian model to estimate the probability of needing critical care. Results:The study enrolled 350 children from March 12, 2020, to July 1, 2020: 292 (83.4%) and 214 (73.7%) were considered to have relevant COVID-19, of whom 24.2% required critical care. Four major clinical syndromes of decreasing severity were identified: multi-inflammatory syndrome (MIS-C) (17.3%), bronchopulmonary (51.4%), gastrointestinal (11.6%), and mild syndrome (19.6%). Main risk factors were high C-reactive protein and creatinine concentration, lymphopenia, low platelets, anemia, tachycardia, age, neutrophilia, leukocytosis, and low oxygen saturation. These risk factors increased the risk of critical disease depending on the syndrome: the more severe the syndrome, the more risk the factors conferred. Based on our findings, we developed an online risk prediction tool (https://rserver.h12o.es/pediatria/EPICOAPP/, username: user, password: 0000). Conclusions: Risk factors for severe COVID-19 include inflammation, cytopenia, age, comorbidities, and organ dysfunction. The more severe the syndrome, the more the risk factor increases the risk of critical illness. Risk of severe disease can be predicted with a Bayesian model.