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Topics in Antiviral Medicine ; 29(1):238, 2021.
Article in English | EMBASE | ID: covidwho-1250842


Background: The accuracy of rapid antigen tests (RAT) SARS-CoV-2 for in children is unknown. Our aim was to determine the diagnostic accuracy and concordance of the RAT PanBioTM (Abbott) compared to RT-PCR in nasopharyngeal smear (NPS) samples, in symptomatic pediatric population. Methods: This is a descriptive, retrospective, multicentre clinical study nested in a prospective, observational, multicenter cohort study. We included pediatric patients aged 0 to 16 years with symptoms consistent with COVID-19 of ≤5 days of evolution, attended in the Emergency Departments of the seven centers involved. A total of two consecutive NPS were obtained from each patient: one was employed to perform the RAT and the other to perform RT-PCR. Sample size for a non-inferiority study was calculated considering 80% power, for a 5% prevalence and a 90% sensitivity, using RT-PCR as the gold standard reference. A confusion matrix was displayed. Non-inferiority of sensitivity and specificity between diagnostic tests was assessed using the McNemar's test. The agreement between the two methods was calculated using Cohen's kappa index. Results: A total of 1620 patients were tested in 7 hospitals. The overall sensitivity for RAT PanBioTM was 45.4% (95%CI, 34.1-57.2), and specificity was 99.8% (95%CI, 99.4-99.9) (Figure 1). The positive predictive value (PPV) for this 4.8% prevalence was 92.5% (95%CI, 78.6-97.4). The negative predictive value was 97.3 % (95%CI, 96.8-97.8). Positive likelihood ratio (PLR) was high - 233.8 (IC 95%, 73.5-743.3), and negative likelihood ratio (NLR) was low - 0.54 (95%CI, 0.44-0.67). Conclusion: Compared to RT-PCR, the sensitivity of the RAT PanBioTM was low in children with <5 days of symptoms of COVID-19. The specificity and PLR were good, and the NLR and concordance with RT-PCR were only moderate. These results suggest that the test is very good when the result is positive, and that the test has only a limited value when the result is negative. In relation with screening and public health policy, these results should be interpreted considering also rapidness, availability and false positives ratio compared to RT-PCR or other tests.

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.