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.

Although hemolysin is the main virulence factor by which E. coli causes acute prostatitis, the association between hemolysin and biofilm formation may result in increased ability of E. coli strains to persist in the prostate.

Escherichia coli is the most frequent microorganism involved in urinary tract infection (UTI). Acute UTI caused by uropathogenic E. coli (UPEC) can lead to recurrent infection, which can be defined as either re-infection or relapse. E. coli strains causing relapse (n = 27) and re-infection (n = 53) were analysed. In-vitro production of biofilm, yersiniabactin and aerobactin was significantly more frequent among strains causing relapse. Biofilm assays may be helpful in selecting patients who require a therapeutic approach to eradicate persistent biofilm-forming E. coli strains and prevent subsequent relapses.

The prevalence of 31 virulence factors was analyzed among nalidixic acid-susceptible and -resistant Escherichia coli strains from phylogenetic group B2. Hemolysin, cytotoxic necrotizing factor 1, and S and F1C fimbriae genes were less prevalent among nalidixic acid-resistant E. coli strains. Quinolone resistance may be associated with a decrease in the presence of some virulence factors.Extraintestinal pathogenic Escherichia coli (ExPEC) strains have multiple virulence factors (VFs) that confer the potential for pathogenicity (6). Recently, extended virulence genotypes have been reported for ExPEC isolates from patients with diverse extraintestinal syndromes (9). E. coli strains derive from different phylogenetic groups (5). Pathogenic E. coli strains derive mainly from the more virulent phylogenetic group B2 (3,7,13).Recent data suggest that quinolone-resistant ExPEC are less able to cause upper urinary tract infection and have fewer VFs than quinolone-susceptible E. coli (14,15). Some studies have related quinolone resistance and low virulence with phylogenetic origin (8). However, in vitro studies (unpublished data) suggest a decreased pathogenicity of E. coli associated with the acquisition of quinolone resistance itself. To study whether the absence of VFs is associated with resistance specifically within phylogenetic group B2, we investigated the prevalence of 31 VFs among quinolone-resistant versus quinolone-susceptible E. coli urinary tract infection (UTI) isolates, all belonging to phylogenetic group B2 (the most virulent; not intrinsically related to quinolone resistance, as shown for phylogenetic group A) (10). The prevalence of the studied VFs according to susceptibility to ampicillin, cotrimoxazole, and gentamicin was also assessed.E. coli strains isolated from urine from patients with acute pyelonephritis, acute cystitis, or acute prostatitis who presented at our department were identified by conventional biochemical tests. Cystitis, acute pyelonephritis, and acute prostatitis were defined as they were defined previously elsewhere (12). Fifty-three E. coli isolates causing acute pyelonephritis in women, 19 causing cystitis in women, and 13 causing prostatitis in men were analyzed.Susceptibilities to nalidixic acid, ciprofloxacin, ampicillin, cotrimoxazole, and gentamicin were tested by the E-test method (AB Biodisk, Sölna, Sweden). All isolates were assigned to phylogenetic group B2 with the use of the multiplex PCR-based method (1), and all isolates belonged to different clones by Rep-PCR. Extended virulence genotypes, including 31 individual VFs and papA alleles, were determined by multiplex PCR assays and dot blot hybridization, as previously described (7). In addition, sat (secreted autotransporter toxin) was detected using previously described PCR conditions and primers (15). Each isolate was tested in duplicate, in parallel with appropriate positive and negative controls.Statistical analyses were performed by using Fisher's exact and chi-square tests. Stratified analysis was performed by mean...

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...