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 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...

Cyclosporin A (CsA) is an immunomodulator drug that has been used in the treatment of several types of advanced pulmonary interstitial disease. This beneficial effect occurs mainly in circumstances in which alveolitis due to CD4 lymphocytes is absent, suggesting that CsA acts on other types of cells. The present study was undertaken to determine the effect of CsA on inflammatory cytokine secretion by human alveolar macrophages (AMs). Human AMs were collected by bronchoalveolar lavage from four control subjects and 13 patients with interstitial lung disease. Purified human AMs were incubated with different concentrations of CsA (200, 20 and 2 ng ml-1) in the presence or absence of lipopolysaccharide (LPS). Interleukin-1 beta (IL-1 beta), tumour necrosis factor-alpha (TNF-alpha), IL-6 and IL-8 levels were measured in supernatants using specific enzyme-linked immunosorbent assays. It was found that CsA inhibits basal secretion of TNF-alpha and IL-8 at 20 and 200 ng ml-1. However, none of the different concentrations of CsA modified basal secretion of IL-1 beta nor IL-6. By contrast, a lower concentration of CsA (2 ng ml-1) inhibits LPS-stimulated secretion of all inflammatory cytokines. It is concluded that CsA exerts a modest effect on inflammatory cytokine production by human AMs.

In addition to its well-established effect on T cells, cyclosporin A (CsA) also inhibits inflammatory cytokine production by macrophages. However, little is known about the mechanism of action of CsA on macrophage cytokine production. We measured the effect of CsA on basal and phorbol-myristate-acetate (PMA)-stimulated production of interleukin-6 using the human monocyte cell line U937 differentiated with dimethylsulfoxide (DMSO). Interleukin-6 levels were measured in supernatant and cell lysates using specific enzyme-linked immunosorbent assays. We found that CsA decreases not only IL-6 release but also cytokine synthesis. The concentration of CsA used did not affect either cell viability or proliferation. Three possibilities may be advanced to explain the CsA-due decrease in IL-6 production by macrophages: (a) inhibition of the synthesis of an early common regulatory protein, (b) inhibition of cytokine gene transcription, or (c) modulation of post-transcriptional events. The first possibility was tested by measuring the effect of cycloheximide on the experimental system during the first 3 hours of culture. Although cycloheximide decreased total cytokine synthesis, the pattern of cytokine modulation by CsA persisted. These data suggest that CsA-mediated macrophage cytokine inhibition is not mediated by an early common regulatory protein. To further explore the inhibition mechanism, we measured IL-6 mRNA levels by Northern blot. IL-6 mRNA levels were unaffected by CsA both in resting and PMA-stimulated cells. We conclude that in human macrophages CsA diminishes IL-6 production at post-transcriptional level.

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