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...

Introducción: La epidemiología de la infección por virus respiratorio sincitial (VRS) ha cambiado durante la pandemia de COVID-19. Nuestros objetivos fueron describir la epidemia de VRS en 2021 y compararla con los años previos a la pandemia. Métodos: estudio retrospectivo realizado en Madrid (España) en un hospital pediátrico terciario comparando los datos epidemiológicos y clínicos de los ingresos por VRS durante 2021 y las dos temporadas anteriores. Resultados: 899 niños ingresaron por infección por VRS en el período de estudio. Durante 2021, el brote alcanzó su punto máximo en junio y los últimos casos se identificaron en julio. Las temporadas anteriores se detectaron en otoño-invierno. El número de hospitalizaciones en 2021 fue significativamente menor que en temporadas anteriores. No hubo diferencias entre temporadas en cuanto a edad, sexo o gravedad de la enfermedad. Conclusión: Las hospitalizaciones por VRS durante 2021 en España se trasladaron a verano sin casos en otoño e invierno 2020-2021. A diferencia de otros países, los datos clínicos fueron similares entre epidemias.

Gout incidence and severity appear to be increasing likely in relation to dietary habits. Switching the pattern of secretion of inflammatory mediators with maturating macrophages which contain MSU crystals may be the key to self limitation of gouty attacks. We must define better which gout is a "difficult" one.

FIGURE 1. Direct immunofluorescence microscopy with anti-IgA staining. Global deposition with mesangial and capillary loops granular pattern.