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Made available in DSpace on 2022-04-29T08:31:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed 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 (25–75th 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, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) 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: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. Department of Internal Medicine Medical School Universidade Federal de Minas Gerais Telehealth Center University Hospital Universidade Federal de Minas Gerais Institute for Health Technology Assessment IATS/ CNPq)., Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507 Department of Statistics Universidade Federal de Minas Gerais Center for Research and Graduate Studies in Business Administration Universidade Federal de Minas Gerais Universidade Federal de São João del-Rei Infectious Diseases Department Vall d'Hebron University Hospita Universitat Autònoma de Barcelona Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor Grupo Hospitalar Conceição Pontifícia Universidade Católica do Rio Grande do Sul RGS) Hospital São Lucas PUCRS Rede Mater Dei de Saúde Hospital Márcio Cunha Universidade do Sul de Santa Catarina UNISUL Dissertare Scientific Advice SOS Cardio Hospital Internal Medicine Department University Hospital Universidade Federal de Minas Gerais Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu Universidade Federal do Rio Grande do Sul Hospital Bruno Born Research Center of Vale do Taquari. Hospital Mãe de Deus Hospital Universitário de Canoas Mucuri Medical School FAMMUC Universidade Federal dos Vales do Jequitinhonha e Mucuri – UFVJM Hospital Santa Rosalia Hospital Metropolitano Doutor Célio de Castro Hospital Moinhos de Vento Hospital Unimed BH Hospital Risoleta Tolentino Neves Post-graduation Center Medical School Universidade Federal de Minas Gerais Hospital Metropolitano Odilon Behrens Hospital Eduardo de Menezes Universidade FUMEC Hospital Julia Kubitschek Hospital Universitário de Santa Maria Departamento de Medicina Interna Universidade Federal de Santa Maria Hospital São João de Deus Hospital Regional Antônio Dias Faculdade Ciências Médicas de Minas Gerais Hospital de Clínicas de Porto Alegre Universidade Federal do Rio Grande do Sul Faculdade de Ciências Humanas de Curvelo Hospital João XXIII Av. Professor Alfredo Balena Hospital Santo Antônio Hospital Universitário Ciências Médicas Vall d'Hebron University Hospital PROSICS Barcelona Instituto René Rachou-FIOCRUZ Minas. Universidade Federal da Fronteira Sul Hospital Regional do Oeste Pontifícia Universidade Católica de Minas Gerais Hospital Tacchini Centro Universitário de Patos de Minas. RPatos de Minas Hospital Semper Hospital Santa Cruz Universidade de Santa Cruz Fundação Hospitalar do Estado de Minas Gerais – FHEMIG. Universidade Federal de Ouro Preto Hospital das Clínicas da Universidade Federal de Pernambuco Universidade Federal de Pernambuco Centro de Ciências Médicas Centro Universitário de Belo Horizonte UniBH) Erasmus MC University Medical Center Rotterdam Department of Cardiology Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu

Introduction Fast and efficient assessment of prognosis of coronavirus disease 19 (COVID-19) is needed to optimize the allocation of health care and human resources, to empower early identification and intervention of patients at higher risk of poor outcome. A proper assessment tool may guide decision making, to develop an appropriate plan of care for each patient. Although different scores have been proposed, the majority of them are limited due to high risk of bias, and there is a lack of reliable prognostic prediction models. Purpose To develop and validate an easy applicable rapid scoring system that employs routinely available clinical and laboratory data at hospital presentation, to predict in-hospital mortality in patients with COVID-19, able to discriminate high vs non-high risk patients. Additionally, we aimed to compare this score with other existing ones. Method Cohort study, conducted in 36 Brazilian hospitals in 17 cities. Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Primary outcome was in-hospital mortality. 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. 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. 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. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Minas Gerais State Agency for Research and Development (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG) [grant number APQ-00208-20], National Institute of Science and Technology for Health Technology Assessment (Instituto de Avaliação de Tecnologias em Saúde – IATS)/ National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnolόgico - CNPq) [grant number 465518/2014-1]

Epidemiología; Microbiología; SARS-CoV-2 Epidemiologia; Microbiologia; SARS-CoV-2 Epidemiology; Microbiology; SARS-CoV-2 Chagas disease (CD) continues to be a major public health burden in Latina America. Information on the interplay between COVID-19 and CD is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Consecutive patients with confirmed COVID-19 were included from March to September 2020. Genetic matching for sex, age, hypertension, diabetes mellitus and hospital was performed in a 4:1 ratio. Of the 7018 patients who had confirmed COVID-19, 31 patients with CD and 124 matched controls were included (median age 72 (64–80) years-old, 44.5% were male). At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p

Around 5% of coronavirus disease 2019 (COVID-19) patients develop critical disease, with severe pneumonia and acute respiratory distress syndrome (ARDS). In these cases, extracorporeal membrane oxygenation (ECMO) may be considered when conventional therapy fails. This study aimed to assess the clinical characteristics and in-hospital outcomes of COVID-19 patients with ARDS refractory to standard lung-protective ventilation and pronation treated with ECMO support and to compare them to patients who did not receive ECMO. Patients were selected from the Brazilian COVID-19 Registry. At the moment of the analysis, 7,646 patients were introduced in the registry, eight of those received ECMO support (0.1%). The convenience sample of patients submitted to ECMO was compared to control patients selected by genetic matching for gender, age, comorbidities, pronation, ARDS and hospital, in a 5:1 ratio. From the 48 patients included in the study, eight received ECMO and 40 were matched controls. There were no significant differences in demographic, clinical and laboratory characteristics. Mortality was higher in the ECMO group (n = 7; 87.5%) when compared with controls (n = 17; 42.5%), (p=0.048). In conclusion, COVID 19 patients with ARDS refractory to conventional therapy who received ECMO support had worse outcomes to patients who did not receive ECMO. Our findings are not different from previous studies including a small number of patients, however there is a huge difference from Extracorporeal Life Support Organization results, which encourages us to keep looking for our best excellence.

ObjectiveChagas disease (CD) continues to be a major public health burden in Latina America, where co-infection with SARS-CoV-2 can occur. However, information on the interplay between COVID-19 and Chagas disease is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients.MethodsPatients with COVID-19 diagnosis were selected from the Brazilian COVID-19 Registry, a prospective multicenter cohort, from March to September, 2020. CD diagnosis was based on hospital record at the time of admission. Study data were collected by trained hospital staff using Research Electronic Data Capture (REDCap) tools. Genetic matching for sex, age, hypertension, DM and hospital was performed in a 4:1 ratio.ResultsOf the 7,018 patients who had confirmed infection with SARS-CoV-2 in the registry, 31 patients with CD and 124 matched controls were included. Overall, the median age was 72 (64.-80) years-old and 44.5% were male. At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (pConclusionsIn this large Brazilian COVID-19 Registry, CD patients had a higher prevalence of atrial fibrillation and chronic heart failure compared with non-CD controls, with no differences in-hospital outcomes. The lower C-reactive protein levels in CD patients require further investigation.Key messagesWhat is already known about this subject?Preexisting cardiovascular disease enhances vulnerability to COVID-19.Co-infection with SARS-CoV-2 and T.cruzi can occur in patients living in areas in which both infections are epidemic.What does this study add?Despite a higher frequency of chronic heart failure and atrial fibrillation, our findings do not suggest that co-infection with T.cruzi and SARS-CoV-2 worsens in-hospital outcomes.Chagas disease patients were observed to have lower C-reactive protein (CRP) levels.How might this impact on clinical practice?Given the current circulation of SARS-CoV-2 at high levels and millions of T cruzi-infected individuals living in Brazil, the risk for co-infections substantially increases.Further studies are needed to investigate why CRP levels were lower in CD patients. We hypothesized that CD patients might have a lower risk of unregulated inflammatory response to COVID-19, as they already have an active chronic inflammatory and immune response response triggered by T.cruzi infection.

ObjectiveTo 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.DesignCohort studySettingThe 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.ParticipantsConsecutive 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 measuresIn-hospital mortalityResultsMedian (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/FiO2ratio, 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/).ConclusionsWe 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.Summary boxesWhat is already known on this topic?Rapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department.The majority of available scores have high risk of bias and lack benefit to clinical decision making.Derivation and validation studies in low- and middle-income countries, including Latin America, are scarce.What this study addsABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate.This easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores.A free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation.

Due to various transformations that have occurred in society since the advent of globalization and Information and Communication Technologies (ICTs), this research has the objective of analyzing the relevance of the electronic participation in the democratic context and to present the availability of web 2.0 tools intended for the participation on the websites of the prefectures of Mucuri Valley Mesoregion with low levels of Human Development Index, since ICTs can foster and broaden the forms of interaction between the State and Society. Electronic Government, based on electronic participation tools, can provide citizens with greater involvement in the decision-making aspects of public organizations, establishing a favorable scenario for democracy. The data collection was based on methods of content analysis, specifically an oriented internet browsing. At the end of the research the tools that are made available by city halls were presented and compared. Most municipalities in the Mucuri Valley Mesoregion use sites as e-government tools, three prefectures do not have electronic tools to disseminate and exchange information with citizens, and eight of them have no web 2.0 tools, demonstrating the slow digital evolution in some Brazilian municipalities. Keywords: Electronic Government, Web 2.0, ICTs, Public transparency, Citizen Participation.