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...

ObjectivesTo analyse the effectiveness of dalbavancin (DBV) in clinical practice as consolidation therapy in patients with bloodstream infection (BSI) and/or infective endocarditis (IE) produced by gram-positive cocci (GPC), as well as its safety and pharmacoeconomic impact.MethodsA multicentre, observational and retrospective study was conducted of hospitalised patients with IE and/or BSI produced by GPC who received at least one dose of DBV. Clinical response was assessed during hospitalization, at 3 months and at 1 year.ResultsEighty-three patients with median age of 73 years were enrolled; 73.5% were male; 59.04% had BSI and 49.04% IE (44.04% prosthetic valve IE, 32.4% native IE, 23.5% pacemaker lead). The most frequently isolated microorganism was Staphylococcus aureus in BSI (49%) and coagulase-negative staphylococci in IE (44.1%). All patients with IE were clinically cured in hospital; at 12 months, there was 2.9% loss to follow-up, 8.8% mortality unrelated to IE, and 2.9% therapeutic failure rate. The percentage effectiveness of DBV to treat IE was 96.7%. The clinical cure rate for BSI was 100% during hospital stay and at 3 months; there were no recurrences or deaths during the follow-up. No patient discontinued treatment for adverse events. The saving in hospital stay was 636 days for BSI (315,424.20€) and 557 days for IE (283,187.45€).ConclusionsDBV is an effective consolidation antibiotic therapy in clinically stabilized patients with IE and/or BSI. It proved to be a cost-effective treatment, reducing the hospital stay, thanks to the pharmacokinetic/pharmacodynamic profile of this drug.

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The serum metabolomic profile is sensitive and specific to identify ARDS in patients with H1N1 influenza A pneumonia. Future studies are needed to determine the role of NMR spectroscopy as a biomarker of ARDS.