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...

Sex differences in adult cellulitis, a frequent cause of hospitalization, have not been analyzed. These differences were investigated in a large cellulitis series. Methods: This was a prospective observational study of 606 Spanish hospitalized cellulitis patients. Different comorbidities, clinical, diagnostic, and treatment data were compared between the sexes. Multiple logistic regression modeling was performed to determine the variables independently associated with sex. Results: Overall 606 adult cellulitis patients were enrolled; 314 (51.8%) were male and 292 (48.2%) were female. Females were older (mean age 68.8 vs 58.9 years, p < 0.0001), less likely to have prior wounds (p = 0.02), and more likely to have venous insufficiency (p = 0.0002) and edema/lymphedema (p = 0.0003) than males. The location of the infection differed between the sexes (p = 0.02). Males were more likely to have positive pus cultures (p = 0.0008), the causing agent identified (p = 0.04), and higher rates of Staphylococcus aureus infection (p = 0.04) and received longer antibiotic treatment (p = 0.03). Factors independently associated with female sex in the multivariate analysis were older age (p < 0.0001), prior cellulitis (p = 0.01), presence of edema/lymphedema as the predisposing factor (p = 0.004), negative versus positive pus culture (p = 0.0002), and location of cellulitis other than in the lower extremities (p = 0.035). Conclusions: Differences between male and female patients with cellulitis were age, recurrence, presence of edema/lymphedema, positivity of pus culture, and topography of the infection.

Long term liver fibrosis (LF) changes and their best -monitoring non-invasive markers (NILFM) after effective anti-HCV DAA therapy are little- known. Matrix-metalloproteases (MMPs) and their tissue-inhibitors (TIMPs) are pivotal in liver inflammation repair. Their plasma levels might assess long-term LF changes after therapy. Overall 374 HCV-infected adult patients, 214 HCV-HIV coinfected, were followed-up for 24 months after starting DAA. LF was assessed by transient elastometry (TE), biochemical indexes (APRI, Forns, FIB-4) and, in 61 individuals, by MMPs and TIMP-1 plasma levels. Several MMPs and TIMP-1 SNPs were genotyped in 319 patients. TE was better than biochemical indexes for early and long-term LF monitoring. MMPs-2,-8,-9 and-TIMP-1 levels and TE displayed parallel declining curves although only TIMP-1 correlated with TE (P = 0.006) and biochemical indexes (P < 0.02). HCV monoinfected had significantly higher baseline NILFM and TIMP-1 plasma values, but lower MMPs levels than coinfected patients. No differences in NILFM course were observed between mono-and coinfected or between different DAA regimens. Only the MMP-2 (-1306 C/T) variant TT genotype associated with higher values of NILFM NILFM decline extends 24 months after therapy. TE and TIMP1 are reliable LF-monitoring tools. NILFM courses were similar in mono-and coinfected patients, DAA regimens type did not influence NILFM course.