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...

Enterobacteriaceae showing resistance to cephalosporins due to extended-spectrum b-lactamases (ESBLs) or plasmid-mediated AmpC enzymes, and those producing carbapenemases have spread worldwide during the last decades. Many of these isolates are also resistant to other first-line agents such as fluoroquinolones or aminoglycosides, leaving few available options for therapy. Thus, older drugs such as colistin and fosfomycin are being increasingly used. Infections caused by these bacteria are associated with increased morbidity and mortality compared with those caused by their susceptible counterparts. Most of the evidence supporting the present recommendations is from in vitro data, animal studies, and observational studies. While carbapenems are considered the drugs of choice for ESBL and AmpC producers, recent data suggest that certain alternatives may be suitable for some types of infections. Combined therapy seems superior to monotherapy in the treatment of invasive infections caused by carbapenemase-producing Enterobacteriaceae. Optimization of dosage according to pharmacokinetics/pharmacodynamics data is important for the treatment of infections caused by isolates with borderline minimum inhibitory concentration due to lowlevel resistance mechanisms. The increasing frequency and the rapid spread of multidrug resistance among the Enterobacteriaceae is a true and complex public health problem. IntroductionThe traditional first-line available options for treating serious infections caused by enterobacteria include penicillins, cephalosporins, monobactams, carbapenems, fluorquinolones, and in certain situations, aminoglycosides. The frequency of resistance to these first-line agents has been rising worldwide during the last decades [Paterson 2006;Rhomberg and Jones 2009;Houghton et al. 2010;Nordmann, et al. 2011] and now reach high proportions in many areas of the world.

This randomized clinical trial investigates noninferiority of fosfomycin vs ceftriaxone or meropenem in achieving clinical and microbiological cure among patients with urinary tract infections due to multidrug-resistant Escherichia coli .