To describe the clinical characteristics, laboratory results, imaging findings, and in-hospital outcomes of COVID-19 patients admitted to Brazilian hospitals. Methods: A cohort study of laboratory-confirmed COVID-19 patients who were hospitalized from March 2020 to September 2020 in 25 hospitals. Data were collected from medical records using Research Electronic Data Capture (REDCap) tools. A multivariate Poisson regression model was used to assess the risk factors for in-hospital mortality. Results: For a total of 2,054 patients (52.6% male; median age of 58 years), the in-hospital mortality was 22.0%; this rose to 47.6% for those treated in the intensive care unit (ICU). Hypertension (52.9%), diabetes (29.2%), and obesity (17.2%) were the most prevalent comorbidities. Overall, 32.5% required invasive mechanical ventilation, and 12.1% required kidney replacement therapy. Septic shock was observed in 15.0%, nosocomial infection in 13.1%, thromboembolism in 4.1%, and acute heart failure in 3.6%. Age >= 65 years, chronic kidney disease, hypertension, C-reactive protein ! 100 mg/dL, platelet count < 100 Â 10 9 /L, oxygen saturation < 90%, the need for supplemental oxygen, and invasive mechanical ventilation at admission were independently associated with a higher risk of in-hospital mortality. The overall use of antimicrobials was 87.9%. Conclusions: This study reveals the characteristics and in-hospital outcomes of hospitalized patients with confirmed COVID-19 in Brazil. Certain easily assessed parameters at hospital admission were independently associated with a higher risk of death. The high frequency of antibiotic use points to an over-use of antimicrobials in COVID-19 patients.

[1] This article discusses seasonal and interannual variations of the evapotranspiration (ET) rates in Bananal Island floodplain, Brazil. Measurements included ET and sensible heat flux using the eddy covariance method, atmospheric forcings (net radiation, Rn, vapor pressure deficit, VPD, wind speed and air temperature), soil moisture profiles, groundwater level and flood height, taken from November 2003 to December 2006. For the hydrological years (October-September) of 2003/2004, 2004/2005 and 2005/2006, the accumulated precipitation was 1692, 1471, 1914 mm and the accumulated ET was 1361, 1318 and 1317 mm, respectively. Seasonal analyses indicated that ET decreased in the dry season (average 3.7 mm day À1 ), despite the simultaneous increase in Rn, air temperature and VPD. The increase of ET in the wet season and particularly in the flood period (average 4.1 mm day À1 ) showed that the free water surface evaporation strongly influenced the energy exchange. Soil moisture, which was substantially depleted during the dry season, and adaptative vegetation mechanisms such as leaf senescence contributed to limit the dry season ET. Strong drainage within permeable sandy soils helped to explain the soil moisture depletion. These results suggest that the Bananal flooding area shows a different pattern in relation to the upland Amazon forests, being more similar to the savanna strictu senso areas in central Brazil. For example, seasonal ET variation was not in phase with Rn; the wet season ET was higher than the dry season ET; and the system stored only a tiny memory of the flooding period, being sensitive to extended drought periods.

Objectives The majority of available scores to assess mortality risk of coronavirus disease 19 (COVID-19) patients in the emergency department have high risk of bias. Therefore, our aim was to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients, and to compare this score with other existing ones. Methods Consecutive patients (≥18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Results Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO 2 /FiO 2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833 to 0.885]) and Spanish (0.894 [95% CI 0.870 to 0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Objective: To develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones. Design: Cohort study Setting: The Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Participants: Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay. Main outcome measures: In-hospital mortality Results: Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Abstract. In the Amazonian rain forest, major parts of trees and shrubs are covered by epiphytic cryptogams of great taxonomic variety, but their relevance in biosphere–atmosphere exchange, climate processes, and nutrient cycling is largely unknown. As cryptogams are poikilohydric organisms, they are physiologically active only under moist conditions. Thus, information on their water content (WC) as well as temperature and light conditions experienced by them are essential to analyze their impact on local, regional, and even global biogeochemical processes. In this study, we present data on the microclimatic conditions, including water content, temperature, and light conditions experienced by epiphytic bryophytes along a vertical gradient, and combine these with above-canopy climate data collected at the Amazon Tall Tower Observatory (ATTO) in the Amazonian rain forest between October 2014 and December 2016. While the monthly average of above-canopy light intensities revealed only minor fluctuations over the course of the year, the light intensities experienced by the bryophytes varied depending on the location within the canopy, probably caused by individual shading by vegetation. In the understory (1.5 m), monthly average light intensities were similar throughout the year, and individual values were extremely low, remaining below 3 µmol m−2 s−1 photosynthetic photon flux density more than 84 % of the time. Temperatures showed only minor variations throughout the year, with higher values and larger height-dependent differences during the dry season. The indirectly assessed water content of bryophytes varied depending on precipitation, air humidity, dew condensation, and bryophyte type. Whereas bryophytes in the canopy were affected by diel fluctuations of the relative humidity and condensation, those close to the forest floor mainly responded to rainfall patterns. In general, bryophytes growing close to the forest floor were limited by light availability, while those growing in the canopy had to withstand larger variations in microclimatic conditions, especially during the dry season. For further research in this field, these data may be combined with CO2 gas exchange measurements to investigate the role of bryophytes in various biosphere–atmosphere exchange processes, and could be a tool to understand the functioning of the epiphytic community in greater detail.