Highlights The amount of material collected by nasopharyngeal swabs is imprecise. The determinations of SARS-CoV-2 viral loads from CTS ignore this error source. SARS-CoV-2 CTS should be normalized with an internal marker to correct this errors.

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...

We aimed to analyze whether the lack of inclusion of specific recommendations for the management of candidemia is an independent risk factor for early and overall mortality. Multicenter study of adult patients with candidemia in 13 hospitals. We assessed the proportion of patients on whom nine specific ESCMID and IDSA guidelines recommendations had been applied, and analyzed its impact on mortality. 455 episodes of candidemia were documented. Patients who died within the first 48 hours were excluded. Sixty-two percent of patients received an appropriate antifungal treatment. Either echinocandin or amphotericin B therapy were administered in 43% of patients presenting septic shock and in 71% of those with neutropenia. Sixty-one percent of patients with breakthrough candidemia underwent a change in antifungal drug class. Venous catheters were removed in 79% of cases. Follow-up blood cultures were performed in 72% of cases. Ophthalmoscopy and echocardiogram were performed in 48% and 50% of patients, respectively. Length of treatment was appropriate in 78% of cases. Early (2–7 days) and overall (2–30 days) mortality were 8% and 27.7%, respectively. Inclusion of less than 50% of the specific recommendations was independently associated with a higher early (HR = 7.02, 95% CI: 2.97–16.57; P < .001) and overall mortality (HR = 3.55, 95% CI: 2.24–5.64; P < .001). In conclusion, ESCMID and IDSA guideline recommendations were not performed on a significant number of patients. Lack of inclusion of these recommendations proved to be an independent risk factor for early and overall mortality.

Objectives: To determine quantitatively the extent of intestinal colonization by OXA-48-producing Klebsiella pneumoniae (KpOXA) in hospitalized patients. Methods:The load of the OXA-48 b-lactamase gene in rectal swabs from 147 colonized patients was measured by quantitative PCR. The load was calculated relative to the total bacterial population (represented by the 16S rRNA gene) using the DDCt method and pure cultures of OXA-48-producing K. pneumoniae as reference samples. The relative loads of the epidemic K. pneumoniae clones ST11 and ST405 were also measured. Results:The relative intestinal loads of the OXA-48 b-lactamase gene, RL OXA-48 , in hospitalized patients were high. The median RL OXA-48 was -0.42 (95% confidence interval (CI): -0.60 to -0.16), close to that of a pure culture of OXA-48-producing K. pneumoniae (RL OXA-48 ¼ 0). In those patients colonized by the KpOXA clones ST11 (51/147, 34.7%) and ST405 (14/147, 9.5%), the relative loads of these clones were similarly high (median RL ST11 ¼ -1.1, 95% CI: -1.64 to -0.92; median RL ST405 ¼ -1.3, 95% CI: -1.76 to -0.96). Patients that had received previous antibiotic treatments and those that developed infections by KpOXA had significantly higher RL OXA-48 values: -0.32 (95%

No abstract