Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a new human betacoronavirus responsible for coronavirus disease 2019 (COVID-19), which is an ongoing pandemic worldwide. This pandemic outbreak has resulted in a massive demand for healthcare services. Healthcare workers (HCWs) are one of the most affected groups, accounting for 10% of all confirmed cases. Infected HCWs with symptoms and, particularly, those who are asymptomatic represent a high risk for hospital-acquired COVID-19 [1,2]. It is well known that the virus is mainly transmitted through droplets when an infected person sneezes, coughs or talks. These droplets land on the floor or other surfaces where the virus may survive for some time and constitute a major source of infection.Our aim was to evaluate the presence of viral RNA in fomites in our microbiology laboratory, particularly in the area used for SARS-CoV-2 diagnosis during this pandemic outbreak. We sampled 22 locations inside the laboratory, selecting surfaces subject to frequent contact (high-touch surfaces). Details about sampling features are given in Table I. World Health Organization guidelines were followed for collection of samples [3]. NucliSENS EasyMAG (bioMérieux, Marcy l'Etoile, France) was used for nucleic acid extraction. TaqMan 2019-nCoV Assay Kit v1 (Thermo Fisher Scientific Inc., Franklin, MA, USA) was used for detection of viral RNA. This kit targets ORF1ab, gene N and gene S, and uses human RNase P as the internal control. In addition, an environmental control sample and a positive control sample were included. The environmental control sample was SARS-CoV-2 and RNase P negative. Within the 22 samples, four were positive (one sample for two

Background: Real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the reference laboratory method to diagnose SARS-CoV-2 infection then requires equipment and is time-consuming. There is a crucial demand for rapid techniques such as antigen detection test. Considering the different diagnostic accuracy of tests with other respiratory viruses in adults and children, SARS-CoV-2 antigen test must be evaluated specifically in children. Methods:The purpose of this study was to evaluate the performance of Panbio COVID-19 Ag Rapid Test Device (Abbott) as a point-of-care test for diagnosis of SARS-CoV-2 in comparison to RT-qPCR in a pediatric population. Results: Four hundred forty nasopharyngeal swabs were tested. Amongst the 18 positive RT-qPCR samples, 14 were detected by the rapid antigen test, given an overall sensitivity of 77.7%. All the samples detected positive with the antigen rapid test were also positive with RT-qPCR. Conclusion:The sensitivity of Panbio COVID-19 Ag Rapid Test Device is lower in children than in adults. Nevertheless, considering the good values of specificity, negative and positive predictive values this test could be used as a frontline test to obtain quick results, although the negative values with COVID-19 high clinical suspicion should be confirmed using RT-qPCR.

Lateral flow immunoassays (LFIA) for rapid detection of specific antibodies (IgM and IgG) against SARS-CoV-2 in different human specimens have been developed in response to the pandemic. The aim of this study is to evaluate three immunocromathographic assays (Sienna®, Wondfo® and Prometheus®) for detection of antibodies against SARS-CoV-2 in serum samples, considering RT-qPCR as a reference. A total of 145 serum samples from 145 patients with clinical suspicion of COVID-19 were collected: all of the samples were tested with Sienna®, 117 with Wondfo® and 89 with Prometheus®. The overall results of sensitivity, specificity, positive predictive value and negative predictive value obtained were as follows: 64.

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