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...

Santiago de Chile is one of the most polluted South American cities, concentrating its pollution episodes during winter. Daily PM 2.5 (particulate matter [PM] Յ 2.5 m in aerodynamic diameter) concentrations over 80 g/m 3 have been reached frequently since 1998. Despite several regulations introduced over the past 20 yr to improve the air quality, PM concentration levels remain high. In this work, sampling in downtown Santiago was conducted from

No abstract

No abstract

No abstract

Improving air quality in Santiago has been a high priority for the Chilean government. In this paper, we examine trends of fine particulate matter (PM 2.5 ) mass and species concentrations during the period 1998 to 2010 and explore the impact of fuel-related interventions and fuel sales on concentration changes. Smoothing spline functions were utilized to characterize and account for nonlinear relationships between pollutant concentrations and different parameters. Meteorology-adjusted PM 2.5 concentrations were lower by 21.8 µg/m 3 in 2010 compared to 1998. In this model, wind speed was the most important determinant of PM 2.5 levels. A decrease in 24-hr average wind speed below 1.0 m/s was associated with a significant increase in daily PM 2.5 levels, indicating a high sensitivity of PM 2.5 concentrations to the accumulation of local emissions. The same regression model framework was applied to examine the trends of lead, bromine, and sulfur concentrations. Removal of lead and bromine from gasoline achieved dramatic decreases in their atmospheric concentrations. Nonetheless, both elements continue to persist, likely in the form of PbBrCl. The reduction of diesel sulfur content from 1,500 to 50 ppm corresponded to a 32% decrease in particulate sulfur levels. Lastly, a surge in PM 2.5 was observed in [2005][2006][2007][2008]. Further regression analyses suggested this was prompted by a rise in monthly petroleum-based fuel sales.Implications: In this paper, we elucidate meteorology-adjusted trends of PM 2.5 mass and species concentrations in Santiago and assess the efficacy of fuel-related interventions, such as the removal of lead from gasoline and reduction of sulfur content in diesel. In addition, we explore the impact of fuel sales on PM 2.5 trends. Given that fuel consumption is likely to increase further in this rapidly growing city, understanding its impact on PM 2.5 trends can inform future air quality control efforts in Santiago.

No abstract

No abstract