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...

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Bacteraemia due to carbapenemase-producing Enterobacteriaceae is an emerging medical problem. Management of this entity is complicated by the difficulty in identifying resistance patterns and the limited therapeutic options. A cohort study was performed including all episodes of bloodstream infection due to OXA-48-producing Enterobacteriaceae (O48PE), occurring between July 2010 and April 2012. Data on predisposing factors, clinical presentation, therapy and outcome were collected from medical records. There were 40 cases of bacteraemia caused by O48PE, 35 Klebsiella pneumoniae and five Escherichia coli. Patients were elderly with significant comorbidities (57.5% underlying malignancy). Thirty-five cases (87.5%) were nosocomial, and five (12.5%) were healthcare-associated. Patients had frequently been exposed to antibiotics and to invasive procedures during hospitalization. The most common source of bacteraemia was the urinary tract followed by deep intra-abdominal surgical site infection. Clinical presentation was severe sepsis or shock in 18 cases (45%). Extended-spectrum β-lactamase production was detected in 92.5% of isolates. MIC(90) for ertapenem, imipenem and meropenem were 32, 16 and 16 mg/L, respectively. Most frequently preserved antibiotics were amikacin, colistin, tigecycline and fosfomycin. These antibiotics combined are the basis of targeted therapies, including carbapenem in selected cases. Median delay in starting clinically adequate and microbiologically appropriate treatment was 3 days. Crude mortality during admission and within 30 days from bacteraemia was 65% and 50%, respectively. Bloodstream infections caused by O48PE have a poor prognosis. Delay in diagnosis and in initiation of optimal antimicrobial therapy is frequent. Suspicion and rapid identification could contribute to improving outcomes.

To our knowledge, this is the largest reported series of infections caused by O48KP in the setting of a single-centre outbreak and provides further input on the clinical relevance of infections caused by O48KP and the difficulties associated with its detection and control.