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Revista Espanola de Salud Publica ; 96:07, 2022.
Article in Spanish | MEDLINE | ID: covidwho-2012129


After about a year and a half (at the moment these lines are being written) since the start of the massive vaccination campaign in which, thanks to the high coverage achieved in all groups eligible for vaccination, it has been possible to significantly reduce the morbidity and mortality due to COVID-19, it is important to review the scientific basics that have supported the recommendations implemented to date and those that could be adopted in the near future taking into consideration the epidemiological situation. The objective of this article is, therefore, to address the foundations of some of the technical decisions proposed by the Committee on Programme and Registry of Vaccinations (National Immunization Technical Advisory Group in Spain) and the Technical Working Group on Vaccination against COVID-19. Throughout the eleven updates of the Vaccination Strategy against COVID-19 in Spain, several issues pose intense debate as the vaccination intervals between doses, the convenience of using different types of vaccines, the use of heterologous schemes of vaccination, the benefits of hybrid immunity and the use of a fourth dose (second booster dose) for selected populations. All this without forgetting essential aspects of safety of vaccines. This article is divided into the following sections: Vaccination intervals;Heterologous or mixed scheme;Hybrid immunity (vaccination after infection and infection after vaccination [breakthrough]);Second booster dose.

Topics in Antiviral Medicine ; 30(1 SUPPL):330-331, 2022.
Article in English | EMBASE | ID: covidwho-1880604


Background: Testing using nasopharyngeal swabs (NPS) samples is the cornerstone for the control of the COVID-19 pandemic, but the procedure is uncomfortable and generates anxiety, especially in children. We aimed to evaluate the adequacy of oral saliva swab analysis using RT-PCR comparing to NPS by RT-PCR and Antigen Rapid Test (AgRT) on NPS in children. Methods: Cross-sectional multicenter diagnostic study nested in a prospective, observational cohort (EPICO-AEP) carried out between February and March 2021 at 10 hospitals in Spain. Participants were children 0 to 18 years old with symptoms compatible with SARS-CoV-2 infection of ≤5 days of duration attending at emergency departments. Three samples were collected, two NPS (for AgRT and for RT-PCR) and one oral saliva swab for RT-PCR. In patients with discordant results, new NPS was collected for viral culture and original samples were tested for viral RNA subgenomic (sgRNA) study. Results: 1174 children were included in the analysis, aged 3.8 years (IQR, 1.7-9.0), 647/1174 (55.1%) were male and 760/1174 (64.7%) presented fever 1 day before emergency department admission (IQR 1.0-2.0). Overall, 73/1174 (6.2%) patients tested positive in at least one of the techniques. Sensitivity for RT-PCR in oral saliva swab was 72.1% (95%CI, 59.7-81.9) and specificity 99.6% (95%CI, 99.0-99.9);AgRT in NPS was 61.8% (95%CI, 49.1-73.0) and 99.9% (95%CI, 99.4-100). Kappa index for RT-PCR oral saliva swab was 0.80 (95%CI, 0.72-0.88), and for AgRT was 0.74 (95%CI, 0.65-0.84) vs RT-PCR in NPS. A Bayesian model was used to estimate the accuracy assuming that RT-PCR in NPS is not a perfect gold standard. In this model, sensitivity for RT-PCR oral saliva swab was 84.8% (95%Cr 71.5-93.6), and for AgRT, it was 72.5% (95%Cr, 58.8-83.6). Specificity for RT-PCR oral saliva swab was 99.7% (95%Cr, 99.2-99.9), and for AgRT it was 99.9% (95% Cr, 99.6-100). The Cts were higher in oral saliva swabs compared with NPS;being Ct (NPS)=0.5 x (Ct saliva) + 4.5 (p=0.027). Overall, 4 (10.8%) patients with discordant results had a positive culture. In 3 of the 4 patients, the discordance consisted of positive result on oral saliva swab and nasopharyngeal swabs RT-PCR but negative by antigen rapid diagnostic test. No patient had (+) culture, (+)NP, (-)oral swab. Conclusion: RT-PCR on oral saliva swab is an accurate option for SARS-CoV-2 testing in children. A friendlier technique for younger patients, who must be tested very frequently, may help to increase the number of patients tested.

Topics in Antiviral Medicine ; 29(1):238-239, 2021.
Article in English | EMBASE | ID: covidwho-1250766


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.