Building and revolutionising public healthcare: A living ecosystem to link and improve patient health data and outcomes in a Brazilian hospital

Objectives To develop a Brazilian public hospital, Sao Paulo University Medical School Clinics Hospital, HCFMUSP, informational model to link and improve multiple patients' health data, care pathways and outcomes, to build a living real world ecosystem aiming to subsidize policy decision-making, support research and promote patients' engagement and involvement. Methods Policy-relevant linkage data including demography, diagnostics, outpatient and emergency room visits, hospitalizations, intensive care evolution, assisted mechanical ventilation or special equipment’s uses, electronic prescriptions, imaging and clinical laboratory tests results, surgery records, blood components use, and medical and multidisciplinary teams’ evolutions. Telemedicine-based hub developed for patient’s access to his own visits or procedures schedule, comprehensive data and results temporal series, and specific communications channels. Anonymized data sharing for Sao Paulo State Health Secretariat policy decision-making and SP Research Agency multicenter Data Lake for Covid-19 pandemic research. Stratified impact and economic analysis regarding clinical and co-morbid conditions research were published. Results Since March 2020, this informational model example comprises over 10,000 Covid-19 patient’s related data with more than 100,000 events registered. During the first pandemic trimester, upon SP Health Secretariat policy, the HCFMUSP Central Institute’s 900 ward and 300 ICU beds were the SP central reference for severe and critical admissions. In this first evaluation 88.4% had co-morbidities (e.g. 48.1% hypertension, 30.5% diabetes), 51.7% required ICU admission and 28.9% died. Average hospital length of stay was 10.7 days, mean cost per admission was US$12,637.42, and the overall daily cost was US$919.24. Age strata >69 years confirmed COVID-19, ICU, elevated C-reactive protein (inflammation) adjusted by D-dimer levels (thrombosis biomarker), higher mSOFA, mechanical ventilation, dialysis, surgery and comorbidities, remained significantly associated with higher (24%-200%) costs and poorer outcomes. Conclusion The informational model is proving to be beneficial for all stakeholders. Technology-based organized systems increased management accuracy and efficiency, emergency preparedness, facilitates patient’s involvement and participation, promote medical and multi-professionals teams’ knowledge development, and permits to subsidize policy decisions and to improve public health

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Updated cardiovascular prevention guideline of the Brazilian Society of Cardiology: 2019

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Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Prediction of body fat in adolescents: comparison of two electric bioimpedance devices with dual-energy X-ray absorptiometry

An accurate estimate of body composition is important in assessing and monitoring the nutritional status of adolescents. To compare the accuracy of 2 electrical bioimpedance devices with that of dual-energy X-ray absorptiometry (DXA) to predict body fat in Brazilian adolescents. We evaluated 500 adolescents aged between 10 and 19 years, stratified by sex and divided into overweight and non-overweight groups. The percentage of body fat (%BF) was estimated using 2 types of electrical bioimpedance devices: BIA1 (horizontal tetrapolar bioimpedance equipment) and BIA2 (vertical 8-electrode bioimpedance equipment), as well as by DXA. A Bland–Altman plot was used to calculate the total errors and standard errors of estimate. Considering BMI for age, 19.4% were overweight and 47.4% as assessed by %BF of DXA were overweight. The %BF estimated by BIA2 correlated well (p < 0.05) with the %BF predicted by DXA, and only the total errors for BIA2 in the overweight group were acceptable (≤2.5%). The standard errors of estimate was <3.5%, with the lowest values observed for BIA2. Both BIA1 and BIA2 underestimated the %BF in overweight adolescents, while overestimating the %BF in male adolescents of normal weight. The BIA2 was found to be more effective in the evaluation of body fat. Regardless of the method used, the results should be carefully interpreted when assessing the body composition of adolescents.Una estimación precisa de la composición corporal es importante para evaluar y monitorear el estado nutricional de los adolescentes. Comparar la exactitud de 2 dispositivos de bioimpedancia eléctrica con la absortometria de rayos X de doble energía (AXD) para predecir la grasa corporal en adolescentes brasileños. Se evaluaron 500 adolescentes entre 10 y 19 años, estratificados por sexo y divididos en grupos com sobrepeso y sin sobrepeso. El porcentaje de grasa corporal (%GC) se estimó utilizando 2 tipos de bioimpedancia eléctrica: BIA1 (equipo de bioimpedancia tetrapolar horizontal) y BIA2 (vertical equipo de bioimpedancia 8 electrodos), así como por AXD. Un gráfico de Bland-Altman se utilizó para calcular los errores totales y errores estándar de estimación. Teniendo en cuenta el IMC para la edad, el 19,4% tenían sobrepeso y el 47,4% según la evaluación de %GC de DXA tenían sobrepeso. El %GC estimado por BIA2 buena correlación (p<0,05) con el %GC pronosticado por AXD, y sólo los errores totales para BIA2 en el grupo de sobrepeso eran aceptables (≤2,5%). Los errores estándar de estimación fue <3,5%, con los valores más bajos observados para BIA2. Tanto BIA1 y BIA2 subestimaron el %GC en los adolescentes con sobrepeso, mientras que sobreestimar el %GC de los adolescentes varones de peso normal. El BIA2 se encontró que era más eficaz en la evaluación de la grasa corporal. Independientemente del método utilizado, los resultados deben interpretarse con cautela al evaluar la composición corporal de los adolescentes