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...

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has emerged from Wuhan City (Hubei, China) and has become pandemic across the world. 1 This infection can cause severe respiratory disease with important rate of intensive care unit (ICU) admissions. 2 Bacterial and fungal infections are complications of this viral pneumonia 3,4 due to the severe damage of lung tissue, cytokine storm and immune-paralysis caused by viral infection-induced acute respiratory distress syndrome (ARDS). 1,5 Invasive pulmonary aspergillosis (IPA) has been reported in other respiratory virus such as Influenza viruses. 6 In this case, risk factors are haematological malignancies, solid transplant recipients, ICU stay, diabetes, chronic obstructive pulmonary disease (COPD), systemic corticosteroid administration or chronic kidney disease, among others. 5,7 It is essential to study if these factors or similar are related to the risk of developing COVID-19 associated pulmonary aspergillosis (CAPA), as well as to clarify clinical significance of isolation of Aspergillus spp. in respiratory samples in these patients.

The aim of this study is to review bacterial isolates from respiratory samples of patients with severe COVID-19 disease during the first two months of the first wave of in our hospital. A single-centre retrospective observational study in critically ill adult patients was performed. A total of 1251 respiratory samples from 1195 patients were processed. Samples from 66 patients (5.52%) were determined to be microbiologically significant by a semi-quantitative culture. All patients received broad spectrum antibiotherapy as an empirical treatment. The isolated bacteria were mainly Enterobacterales followed by Staphylococcus aureus and Pseudomonas aeruginosa . Bacterial co-infections in ICU stay could seem not dependant on the virus that has produced the viral pneumonia similarly as with other respiratory viruses such as Influenza virus.

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