Objectives To describe the burden, epidemiology and outcomes of co-infections and superinfections occurring in hospitalized patients with coronavirus disease 2019 (COVID-19). Methods We performed an observational cohort study of all consecutive patients admitted for ≥48 hours to the Hospital Clinic of Barcelona for COVID-19 (28 February to 22 April 2020) who were discharged or dead. We describe demographic, epidemiologic, laboratory and microbiologic results, as well as outcome data retrieved from electronic health records. Results Of a total of 989 consecutive patients with COVID-19, 72 (7.2%) had 88 other microbiologically confirmed infections: 74 were bacterial, seven fungal and seven viral. Community-acquired co-infection at COVID-19 diagnosis was uncommon (31/989, 3.1%) and mainly caused by Streptococcus pneumoniae and Staphylococcus aureus . A total of 51 hospital-acquired bacterial superinfections, mostly caused by Pseudomonas aeruginosa and Escherichia coli , were diagnosed in 43 patients (4.7%), with a mean (SD) time from hospital admission to superinfection diagnosis of 10.6 (6.6) days. Overall mortality was 9.8% (97/989). Patients with community-acquired co-infections and hospital-acquired superinfections had worse outcomes. Conclusions Co-infection at COVID-19 diagnosis is uncommon. Few patients developed superinfections during hospitalization. These findings are different compared to those of other viral pandemics. As it relates to hospitalized patients with COVID-19, such findings could prove essential in defining the role of empiric antimicrobial therapy or stewardship strategies.

Objectives To analyse the characteristics and predictors of death in hospitalized patients with coronavirus disease 2019 (COVID-19) in Spain. Methods A retrospective observational study was performed of the first consecutive patients hospitalized with COVID-19 confirmed by real-time PCR assay in 127 Spanish centres until 17 March 2020. The follow-up censoring date was 17 April 2020. We collected demographic, clinical, laboratory, treatment and complications data. The primary endpoint was all-cause mortality. Univariable and multivariable Cox regression analyses were performed to identify factors associated with death. Results Of the 4035 patients, male subjects accounted for 2433 (61.0%) of 3987, the median age was 70 years and 2539 (73.8%) of 3439 had one or more comorbidity. The most common symptoms were a history of fever, cough, malaise and dyspnoea. During hospitalization, 1255 (31.5%) of 3979 patients developed acute respiratory distress syndrome, 736 (18.5%) of 3988 were admitted to intensive care units and 619 (15.5%) of 3992 underwent mechanical ventilation. Virus- or host-targeted medications included lopinavir/ritonavir (2820/4005, 70.4%), hydroxychloroquine (2618/3995, 65.5%), interferon beta (1153/3950, 29.2%), corticosteroids (1109/3965, 28.0%) and tocilizumab (373/3951, 9.4%). Overall, 1131 (28%) of 4035 patients died. Mortality increased with age (85.6% occurring in older than 65 years). Seventeen factors were independently associated with an increased hazard of death, the strongest among them including advanced age, liver cirrhosis, low age-adjusted oxygen saturation, higher concentrations of C-reactive protein and lower estimated glomerular filtration rate. Conclusions Our findings provide comprehensive information about characteristics and complications of severe COVID-19, and may help clinicians identify patients at a higher risk of death.

En el trascurso de su enfermedad: 1. ¿Ha tenido usted pérdida del olfato o sabor de la comida? Sí ____ / No ____ 2. Por favor, cruce la línea horizontal con una línea vertical en el punto que considere apropiado, donde 0 es SIN PÉRDIDA de olfato/sabor y 10 es PÉRDIDA TOTAL del olfato de la comida o la bebida. 0 10 3. ¿Tiene usted pérdida del gusto (dulce, salado, ácido, amargo)? Sí ____ / No ____ 4. Por favor, cruce la línea horizontal con una línea vertical en el punto que considere apropiado, donde 0 es SIN PÉRDIDA del gusto y 10 es PÉRDIDA TOTAL del gusto. El GUSTO se refiere sólo a la percepción salada, dulce, ácida o amarga.

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Describir las características clínicas y socio-demográficas de las personas con infección por el VIH atendidas en los servicios hospitalarios y su evolución temporal.  Estimar la prevalencia de conductas de riesgo para el VIH en la población de estudio.  Estimar la prevalencia de los pacientes que siguen tratamiento antirretroviral y definir las características de estos.  Describir las características particulares de los pacientes con origen en otros países. Métodos: Tipo de estudio: Estudio observacional, descriptivo de corte transversal realizado en un día prefijado. Población de estudio: Pacientes con diagnóstico de VIH en contacto con el Sistema Nacional de Salud. Ámbito: Hospitales del Sistema Nacional de Salud en las comunidades autónomas que participan de forma voluntaria en el estudio. Periodo: 2003-2018. Criterios de inclusión: Pacientes con diagnóstico VIH que se encuentren el día de la encuesta en régimen de hospitalización, consulta externa u hospital de día. Criterios de exclusión: Pacientes con diagnóstico VIH ingresados o tratados en otros servicios ajenos a la unidad VIH o enfermedades infecciosas en el día de la encuesta, que no hayan sido objeto de interconsulta. Recogida de datos: Cuestionario cumplimentado por el personal médico responsable de cada paciente. Análisis: Descriptivo y bivariante. Para analizar la evolución anual de proporciones se ha utilizado el test de χ 2 de tendencia. Resultados: Se presentan los resultados correspondientes a la encuesta realizada en 2018 y el análisis del periodo 2003-2018. Conclusiones: Encuesta Hospitalaria de pacientes con infección por el VIH. Resultados 2018.

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...

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Highlights Almost two thirds of patients with SARS-CoV-2 infection present with hypocalcemia at hospital admission. Hypocalcemia at admission is related to high oxygen support requirement any time during hospitalization. Patients with hypocalcemia at admission had two times more probability to be admitted to the Intensive Care Unit during hospitalization than patients with normal calcium at admission.