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.

In addition to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), humans are also susceptible to six other coronaviruses, for which consecutive exposures to antigenically related and divergent seasonal coronaviruses are frequent. Despite the prevalence of COVID-19 pandemic and ongoing research, the nature of the antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. Here we longitudinally profile the early humoral immune response against SARS-CoV-2 in hospitalized coronavirus disease 2019 (COVID-19) patients and quantify levels of pre-existing immunity to OC43, HKU1 and 229E seasonal coronaviruses, and find a strong back-boosting effect to conserved but not variable regions of OC43 and HKU1 betacoronaviruses spike protein. However, such antibody memory boost to human coronaviruses negatively correlates with the induction of IgG and IgM against SARS-CoV-2 spike and nucleocapsid protein. Our findings thus provide evidence of immunological imprinting by previous seasonal coronavirus infections that can potentially modulate the antibody profile to SARS-CoV-2 infection.

To the Editor-The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a public health problem of historic dimensions. However, this pandemic is occurring in the setting of an antimicrobial resistance crisis that is increasing at an alarming pace worldwide. Of concern, countries with a particularly high incidence of COVID-19 also have significant rates of infection caused by multidrug-resistant bacteria. During the 2009 influenza pandemic, coinfection with bacteria was identified as a prognostic factor for the worse outcomes. 1 This finding has led to empirical antibiotic therapy being recommended for patients with suspected influenza pneumonia, 2 and it has probably been a major reason underpinning the initial World Health Organization's recommendation to use empirical antibiotics in cases of COVID-19 pneumonia. 3 Although this guideline advocated for early antimicrobial de-escalation, a couple of factors may have hindered this practice. First, processing microbiological samples in saturated emergency rooms and overloaded laboratories is difficult. Second, no evidence-based antiviral treatment for COVID-19 has been developed in the setting of a highly stressful situation. Together, these factors may have prompted clinicians to prescribe broad-spectrum antimicrobials more often than they may otherwise have. Therefore, antimicrobial stewardship approaches urgently need to be reinforced during the COVID-19 pandemic. 4 To date, however, no study has evaluated the impact of the COVID-19 pandemic on antibiotic consumption. We conducted a before-and-after cross-sectional study comparing data in 2019 (before the COVID-19 pandemic began) and 2020 (COVID-19) for the periods from January 1 to April 30. Bellvitge University Hospital is a 700-bed hospital that serves as a public referral center of 1 million inhabitants in Catalonia, the second worst pandemic-affected area in Spain. 5 As of April 30, 2020, this hospital had had >1,293 hospital admissions for COVID-19, with a 317% increase in critical care bed use. In this study, we calculated the defined daily dose per 100 patient days, as described elsewhere, and based on the dispensing data of our electronic prescribing system. Medians for continuous variables were compared using the Wilcoxon log-rank test.

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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While the current pandemic remains a thread to human health, the polyclonal nature of the antibody response against SARS-CoV-2 is not fully understood. Other than SARS-CoV-2, humans are susceptible to six different coronaviruses, and previous exposure to antigenically related and divergent seasonal coronaviruses is frequent. We longitudinally profiled the early humoral immune response against SARS-CoV-2 on hospitalized COVID-19 patients, and quantify levels of pre-existing immunity to OC43, HKU1 and 223E seasonal coronaviruses. A strong back-boosting effect to conserved, but not variable regions of OC43 and HKU1 betacoronaviruses spike protein was observed. All patients developed antibodies against SARS-CoV-2 spike and nucleoprotein, with peak induction at day 7 post hospitalization. However a negative correlation was found between antibody memory boost to human coronaviruses and induction of IgG and IgM against SARS-CoV-2 spike. Our findings provide evidence of immunological imprinting that determine the antibody profile to COVID-19 patients in an original antigenic sin fashion.

SARS-CoV-2 RNAemia is associated with severe chronic underlying diseases but not with nasopharyngeal viral load Dear Editor,

Objectives The effect of the use of immunomodulatory drugs on the risk of developing hospital-acquired bloodstream infection (BSI) in patients with COVID-19 has not been specifically assessed. We aim to identify risk factors for, and outcomes of, BSI among hospitalized patients with severe COVID-19 pneumonia. Methods We performed a severity matched case-control study (1:1 ratio) nested in a large multicenter prospective cohort of hospitalized adults with COVID-19. Cases with BSI were identified from the cohort database. Controls were matched for age, sex, and acute respiratory distress syndrome. A Cox proportional hazard ratio model was performed. Results Of 2005 patients, 100 (4.98%) presented 142 episodes of BSI, mainly caused by coagulase-negative staphylococci, Enterococcus faecalis , and Pseudomonas aeruginosa . Polymicrobial infection accounted for 23 episodes. The median time from admission to the first episode of BSI was 15 days (IQR 9 - 20), and the most frequent source was catheter-related infection. The characteristics of patients with and without BSI were similar, including the use of tocilizumab, corticosteroids, and combinations. In the multivariate analysis, the use of these immunomodulatory drugs was not associated with an increased risk of BSI. A Cox proportional hazard ratio (HR) model showed that after adjusting for the time factor, BSI was associated with a higher in-hospital mortality risk (HR 2.59 [1.65 – 4.07]; <0.001). Conclusions Hospital-acquired BSI in patients with severe COVID-19 pneumonia was uncommon and the use of immunomodulatory drugs was not associated with its development. When adjusting for the time factor, BSI was associated with a higher mortality risk.