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.

Background Healthcare workers (HCWs) are especially vulnerable to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aim The aim of this study was to describe the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) among HCWs from February 24 th to April 30 th , 2020, in a hospital in Madrid, Spain. Methods This was a retrospective cohort study. Cumulative COVID-19 incidence was calculated for all HCWs and categorized according to presumed level of COVID-19 exposure (high, medium, and low). Findings Among 1911 HCWs, 213 (11.1%) had COVID-19 during the study period. Cases increased gradually from March 8 th , peaking on March 17 th and declining thereafter. The peak of cases among HCWs was reached 14 days before the peak in admitted COVID-19 cases in the hospital. There were no significant differences in the proportion of COVID-19 cases according to level of occupational exposure ( P = 0.123). There were five departments and two professions in which >20% of the workers had confirmed COVID-19. Temporal clusters were identified in three of these departments and one profession, with most of the cases occurring over a period of less than five days. The prevalence of comorbidities was low and 91.5% of patients had mild or moderate symptoms. Eleven patients were admitted to the hospital and one patient needed intensive care. None of the patients died. The median time of sick leave was 20 (interquartile range: 15–26) days. Conclusion The results suggest that HCW–HCW transmission accounted for part of the cases. In spite of a low prevalence of comorbidities and a mild clinical course in most cases, COVID-19 caused long periods of sick leave.

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...

The setting for this retrospective cohort study was a specialised tuberculosis unit in Madrid, Spain. The objective was to describe the risk factors for multidrug-resistant tuberculosis (MDR-TB). The medical records of all patients admitted to the unit were reviewed retrospectively to identify factors associated with multidrug resistance. Patients with positive culture for M. tuberculosis and with available drug-susceptibility tests were included. The variables assessed were age, gender, country of origin, homelessness, alcohol consumption, intravenous drug use, methadone substitution therapy, contact with a tuberculosis patient, sputum smear, site of disease, previous tuberculosis treatment, HIV infection, history of imprisonment, diabetes mellitus and chronic obstructive pulmonary disease. Thirty patients with MDR-TB and 666 patients with non-MDR-TB were included from the years 1997 to 2006. The only factors associated with MDR-TB in multivariate analysis were previous tuberculosis treatment (OR: 3.44; 95% CI: 1.58-7.50; p = 0.003), age group 45-64 years (OR: 3.24; 95% CI: 1.34-7.81; p = 0.009) and alcohol abuse (OR: 0.12; 95% CI: 0.03 to 0.55; p = 0.003). In our study, patients who had had previous treatment for tuberculosis, who were 45-64 years of age or who had no history of alcohol abuse were more likely to have MDR-TB.