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.

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The impact of antimicrobial resistance on clinical outcomes is the subject of ongoing investigations, although uncertainty remains about its contribution to mortality. We investigated the impact of carbapenem resistance on mortality in Pseudomonas aeruginosa bacteremia in a prospective multicenter (10 teaching hospitals) observational study of patients with monomicrobial bacteremia followed up for 30 days after the onset of bacteremia. The adjusted influence of carbapenem resistance on mortality was studied by using Cox regression analysis. Of 632 episodes, 487 (77%) were caused by carbapenem-susceptible P. aeruginosa (CSPA) isolates, and 145 (23%) were caused by carbapenem-resistant P. aeruginosa (CRPA) isolates. The median incidence density of nosocomial CRPA bacteremia was 2.3 episodes per 100,000 patient-days (95% confidence interval [CI], 1.9 to 2.8). The regression demonstrated a time-dependent effect of carbapenem resistance on mortality as well as a significant interaction with the Charlson index: the deleterious effect of carbapenem resistance on mortality decreased with higher Charlson index scores. The impact of resistance on mortality was statistically significant only from the fifth day after the onset of the bacteremia, reaching its peak values at day 30 (adjusted hazard ratio for a Charlson score of 0 at day 30, 9.9 [95% CI, 3.3 to 29.4]; adjusted hazard ratio for a Charlson score of 5 at day 30, 2.6 [95% CI, 0.8 to 8]). This study clarifies the relationship between carbapenem resistance and mortality in patients with P. aeruginosa bacteremia. Although resistance was associated with a higher risk of mortality, the study suggested that this deleterious effect may not be as great during the first days of the bacteremia or in the presence of comorbidities. Pseudomonas aeruginosa is a leading cause of nosocomial infections, which are often severe (26, 43) and difficult to treat because of their limited susceptibility to antimicrobial agents (35) and the frequent emergence of antibiotic-resistant mutants during therapy (8). Factors related to the host, the organism, and the treatment may increase mortality. With regard to the host, the severity of the underlying disease may be synergistic with infection due to resistant organisms; concomitantly, increased virulence could explain the adverse impact of resistant pathogens on clinical outcomes, although this association has not been demonstrated to date. In addition, factors such as decreased antibiotic effectiveness or a delay in the initiation of therapy may contribute to adverse outcomes in patients infected by resistant pathogens (17).The problem of antibiotic-resistant organisms is increasing (18), and the impact of antimicrobial resistance on clinical outcomes is the subject of ongoing investigations (2, 5, 9, 15, 25-28, 32, 33, 40, 41, 43), although its contribution to mortality remains uncertain. Measurements of its impact on patients are, by necessity, derived essentially from observational studies, and therefore, an adequate control of confound...

The clinical and microbiological characteristics of community-onset healthcare-associated (HCA) bacteraemia of urinary source are not well defined. We conducted a prospective cohort study at eight tertiary-care hospitals in Spain, from October 2010 to June 2011. All consecutive adult patients hospitalized with bacteraemic urinary tract infection (BUTI) were included. HCA-BUTI episodes were compared with community-acquired (CA) and hospital-acquired (HA) BUTI. A logistic regression analysis was performed to identify 30-day mortality risk factors. We included 667 episodes of BUTI (246 HCA, 279 CA and 142 HA). Differences between HCA-BUTI and CA-BUTI were female gender (40% vs 69%, p <0.001), McCabe score II-III (48% vs 14%, p <0.001), Pitt score ≥2 (40% vs 31%, p 0.03), isolation of extended spectrum β-lactamase-producing Enterobacteriaciae (13% vs 5%, p <0.001), median hospital stay (9 vs 7 days, p 0.03), inappropriate empirical antimicrobial therapy (21% vs 13%, p 0.02) and mortality (11.4% vs 3.9%, p 0.001). Pseudomonas aeruginosa was more frequently isolated in HA-BUTI (16%) than in HCA-BUTI (4%, p <0.001). Independent factors for mortality were age (OR 1.04; 95% CI 1.01-1.07), McCabe score II-III (OR 3.2; 95% CI 1.8-5.5), Pitt score ≥2 (OR 3.2 (1.8-5.5) and HA-BUTI OR 3.4 (1.2-9.0)). Patients with HCA-BUTI are a specific group with significant clinical and microbiological differences from patients with CA-BUTI, and some similarities with patients with HA-BUTI. Mortality was associated with patient condition, the severity of infection and hospital acquisition.

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...