Introduction: SARS-CoV-2 infection is characterized by hyperproduction of pro-inflammatory cytokines that impact the cardiovascular system. In addition, several cardiovascular risk factors, like arterial hypertension, were identified as risk factors for greater disease severity and mortality in these patients. The objective of this study is to evaluate the prevalence of hypertension in patients with COVID-19 and its association with complications, comorbidities and mortality. Methods: Retrospective study of the patients with COVID-19 admitted to Internal Medicine ward between March 2020 and February 2021. Data was collected from the digital clinical file. Logistic regression was applied to clarify the effect of independent variables on mortality. Results: The sample included 1291 patients with COVID-19, a median age of 73 years (IQR: 22) and male gender predominance (n = 701, 54.3%). There were 65.5% (n = 845) of patients with hypertension, 54.9% (n = 709) dyslipidemia and about a third had diabetes mellitus and obesity (31 and 38%, respectively). Comparatively to the non-hypertensive patients, hypertensive group presented with higher prevalence of other cardiovascular risk factors (diabetes mellitus, obesity and dyslipidemia (p <0.001)) and lower functionality and more comorbidities (as calculated by AVDezIS and Charlson modified scores, p<0.001). There was also higher proportion of severe COVID-19 disease (p = 0.003), greater number of patients admitted to intensive care units (p <0.001) and higher in-hospital mortality (p <0.001). Hypertension was not a mortality predictive factor in these patients. Conclusion: In this study, patients with hypertension presented with higher severity of COVID-19 disease, higher prevalence of other cardiovascular risk factors, complications during hospitalization and in-hospital mortality, however, its presence was not a mortality predictive factor.

Introduction: Early readmission rate (≤ 2 after discharge) tothe Intensive Care Units (ICU) is used as a quality measure.Since readmitted patients have poor outcomes, tools toidentify them before readmission happens are needed. TheModified Early Warning Score (MEWS) identifies patientsat high risk of clinical deterioration, admission to ICU andintra-hospital mortality when done at the Emergency Roomor at the ward. We tested MEWS as a predictor of early readmissions to the ICU when done at the moment of dischargefrom these Units.Material and Methods: We conducted a case-control study(1:1) comparing patients with early readmissions with controls without readmissions to a polyvalent ICU, during a period of 9 years. Logistic regression was used to determine thediscriminative power of MEWS to predict early readmissionsto the ICU. The predictive precision of the score was calculated by the area under the receiver operating characteristiccurve.Results: We paired 114 patients with early readmission (rateof 1.5%) with 114 controls. Any value of MEWS > 0 was associated with a significant increase in the risk of early readmission. A value of MEWS = 0 showed a negative predictivevalue of 99.7%. The area under the receiver operating characteristic curve of the MEWS to the prediction of early readmissions was 0.69 (IC95%: 0.62-0.76; p < 0.001).Discussion: The predictive precision of MEWS to this purpose was higher than other scores reported in the literature.Conclusion: MEWS is an easy clinical score with the potentialto increase patient safety at the moment of discharge fromthe ICU.

OBJETIVO: Descrever o perfil clínico-epidemiológico dos casos confirmados de febre amarela internados em hospital geral de referência para doenças infecciosas no estado do Rio de Janeiro, Brasil, de 11 de março de 2017 a 15 de junho de 2018, durante recente surto e fatores associados ao óbito. MÉTODOS: Estudo observacional retrospectivo, com análise de bases de dados secundários da vigilância epidemiológica local e coleta complementar de dados nas fichas de investigação epidemiológica e prontuários clínicos. As variáveis analisadas incluíram dados demográficos, epidemiológicos, clínicos e laboratoriais. Foi conduzida análise estatística descritiva bivariada e múltipla por regressão logística para estudo de fatores associados ao óbito. RESULTADOS: Foram internados 52 casos confirmados, 86,5% deles homens, com mediana de idade de 49,5 anos e 40,4% trabalhadores rurais. Os sinais e sintomas mais frequentes foram: febre (90,4%), icterícia (86,5%), náuseas e/ou vômitos (69,2%), alterações de excreção renal (53,8%), hemorragias (50%) e dor abdominal (48,1%), com comorbidade em 38,5% dos casos. A letalidade foi de 40,4%. Os fatores associados significativamente à maior chance de óbito na análise bivariada foram: hemorragia, alterações de excreção renal e valores máximos de bilirrubina direta, aspartato aminotransferase (AST), alanina aminotransferase (ALT), ureia e creatinina. Na análise múltipla por regressão logística, apenas alterações de excreção renal e ALT permaneceram como preditores significativos de maior chance de óbito. Observou-se ainda efeito limítrofe para AST. Os pontos de corte identificados como de alto risco para óbito foram ALT > 4.000 U/L e AST > 6.000 U/L. CONCLUSÕES: O estudo contribuiu para o conhecimento do perfil de casos confirmados de febre amarela com gravidade alta. Os principais fatores associados ao óbito foram a alteração da excreção renal e a elevação sérica de transaminases, sobretudo a ALT. A letalidade elevada reforça a necessidade de diagnóstico e tratamento precoces, e a importância do incremento da cobertura vacinal.

OBJETIVO: Avaliar o desempenho do serviço de atendimento móvel de urgência (Samu) na região do Grande ABC, utilizando como condição traçadora o infarto agudo do miocárdio. MÉTODOS: A análise de séries temporais interrompidas foi a abordagem de escolha para testar efeitos imediatos e graduais da intervenção na população de estudo. A pesquisa compreendeu séries temporais mensais ajustadas da taxa de mortalidade hospitalar por infarto agudo do miocárdio no período entre 2000 e 2011. Os dados foram extraídos do Sistema de Informações sobre Mortalidade, usando a análise de regressão segmentada para avaliar o nível e tendência da intervenção antes e após sua implementação. Para fortalecer a validade interna do estudo, foi incluída uma região controle. RESULTADOS: A análise de séries temporais interrompidas mostrou redução de 0,04 mortes por 100.000 habitantes na taxa de mortalidade em relação à tendência subjacente desde a implantação do serviço de atendimento médico de urgência (p = 0,0040; IC95% -0,0816 – -0,0162) e uma redução no nível de 2,89 mortes por 100.000 habitantes (p = 0,0001; IC95% -4,3293 – -1,4623), ambos com significância estatística. Em relação à região controle, a Baixada Santista, a diferença da tendência do resultado entre desfecho de intervenção e controle pós-intervenção de -0,0639 mortes por 100.000 habitantes mostrou-se estatisticamente significativa (p = 0,0031; IC95% -0,1060 – -0,0219). Não podemos excluir confundimentos, mas limitamos sua presença no estudo incluindo séries de região controle. CONCLUSÕES: Embora a análise de séries temporais interrompidas tenha limitações, essa modelagem pode ser útil para a análise de desempenho de políticas e programas. Apesar de a intervenção estudada não ser uma condição que por si só implica na efetividade, a efetividade não estaria presente sem essa intervenção, que, integrada a outras condições, gera um resultado positivo. O Samu é uma estratégia cuja expansão precisa ser levada em consideração ao formular e consolidar políticas com foco nas urgências e emergências.

OBJECTIVE To identify demographic and clinical characteristics of adult patients hospitalized in the Brazilian Unified Health System (SUS) due to viral pneumonia and investigate the association between some comorbidities and death during hospitalization. METHODS This retrospective cohort study was conducted with secondary data of adults admitted to SUS due to viral pneumonia between 2002 and 2015. Patient profile was characterized based on demographic and clinical variables. The association between the ten Elixhauser comorbidities and in-hospital death was investigated using Poisson regression models with robust standard errors. Results were quantified as incidence rate ratio (IRR) with 95% confidence intervals (CI), and we built five models using successive inclusion of variables blocks. RESULTS Hospital admissions for viral pneumonias decreased throughout the study period, and it was observed that 5.8% of hospitalized patients had an in-hospital death. We observed significant differences in demographic and clinical characteristics by comparing individuals who died during hospitalization with those who did not, with the occurrence of one or more comorbidities being more expressive among patients who died. Although not considered risk factors for in-hospital death, chronic pulmonary disease and congestive heart failure were the most common comorbidities. Conversely, IRR for in-hospital death increased with other neurological disorders, diabetes, cancer, obesity, and especially with HIV/AIDS. CONCLUSIONS Individuals presenting with pulmonary and cardiovascular diseases require proper attention during hospitalization, as well as those with other neurological diseases, diabetes, cancer, obesity, and especially HIV/AIDS. Understanding the influence of chronic diseases on viral infections may support the healthcare system in achieving better outcomes.

This study aimed to verify socio-demographic and baseline clinical factors associated with death in a hospital cohort of patients with COVID-19. A retrospective cohort study was conducted between February and December 2020 in a university hospital in the city of São Paulo, using Hospital Epidemiology Center data. RT-PCR-positive patients were selected to compose the sample (n = 1,034). At the end of the study, 362 (32%) patients died. In this cohort, age equal to or greater than sixty years (HR = 1.49) and liver disease (HR = 1.81) were independent risk factors for death from COVID-19 associated with higher in-hospital mortality.

OBJECTIVE: To describe cases, deaths, and hospital mortality from covid-19 in children andadolescents in Brazil, according to age group, during the evolving phases of the pandemic in2020 and 2021.METHODS: Census of patients aged up to 19 committed with severe acute respiratory syndrome, due to covid-19 or unspecified, notified to the Brazilian Influenza Epidemiological Surveillance Information System, from January 1, 2020, to December 31, 2021. The two years were divided into six phases, covering the spread of the disease—first, second and third wave—as well as the impact of vaccination. The pediatric population was categorized into infants, preschoolers, schoolchildren, and adolescents. Hospital mortality was assessed by pandemic phase and age group.RESULTS: A total of 144,041 patients were recorded in the two years, 18.2% of whom had confirmed cases of covid-19. Children under 5 years old (infants and preschoolers) accounted for 62.8% of those hospitalized. A total of 4,471 patients died, representing about 6.1 deaths per day. Infants were the ones who most progressed to the intensive care unit (24.7%) and had the highest gross number of deaths (n = 2,012), but mortality was higher among adolescents (5.7%), reaching 9.8% in phase 1. The first peak of deaths occurred in phase 1 (May/2020), and two other peaks occurred in phase 4 (March/2021 and May/2021). There was an increase in cases and deaths for younger ages since phase 4. Hospital mortality in the pediatric population washigher in phases 1, 4, and 6, following the phenomena of dissemination/interiorization of thevirus in the country, beginning of the second wave and beginning of the third wave, respectively.CONCLUSION: The absolute number of cases o f covid-19 in children and adolescents is significant. Although complete vaccination in descending order of age provided a natural deviation in age range, there was a greater gap between the curve of new hospitalized cases and the curve of deaths, indicating the positive impact of immunization.

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >;1, and their mortality rate was 4.5% (66.0% of them had scores >;1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

BACKGROUND: The prevalence of use of the World Health Organization surgical checklist is unknown. The clinical effectiveness of this intervention in improving postoperative outcomes is debated. METHODS: We undertook a retrospective analysis of data describing surgical checklist use from a 7 day cohort study of surgical outcomes in 28 European nations (European Surgical Outcomes Study, EuSOS). The analysis included hospitals recruiting >10 patients and excluding outlier hospitals above the 95th centile for mortality. Multivariate logistic regression and three-level hierarchical generalized mixed models were constructed to explore the relationship between surgical checklist use and hospital mortality. Findings are presented as crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: A total of 45 591 patients from 426 hospitals were included in the analysis. A surgical checklist was used in 67.5% patients, with marked variation across countries (0-99.6% of patients). Surgical checklist exposure was associated with lower crude hospital mortality (OR 0.84, CI 0.75-0.94; P=0.002). This effect remained after adjustment for baseline risk factors in a multivariate model (adjusted OR 0.81, CI 0.70-0.94; P<0.005) and strengthened after adjusting for variations within countries and hospitals in a three-level generalized mixed model (adjusted OR 0.71, CI 0.58-0.85; P<0.001). CONCLUSIONS: The use of surgical checklists varies across European nations. Reported use of a checklist was associated with lower mortality. This observation may represent a protective effect of the surgical checklist itself, or alternatively, may be an indirect indicator of the quality of perioperative care.

PURPOSE: Very old critical ill patients are a rapid expanding group in the ICU. Indications for admission, triage criteria and level of care are frequently discussed for such patients. However, most relevant outcome studies in this group frequently find an increased mortality and a reduced quality of life in survivors. The main objective was to study the impact of frailty compared with other variables with regards to short-term outcome in the very old ICU population. METHODS: A transnational prospective cohort study from October 2016 to May 2017 with 30 days follow-up was set up by the European Society of Intensive Care Medicine. In total 311 ICUs from 21 European countries participated. The ICUs included the first consecutive 20 very old (≥ 80 years) patients admitted to the ICU within a 3-month inclusion period. Frailty, SOFA score and therapeutic procedures were registered, in addition to limitations of care. For measurement of frailty the Clinical Frailty Scale was used at ICU admission. The main outcomes were ICU and 30-day mortality and survival at 30 days. RESULTS: A total of 5021 patients with a median age of 84 years (IQR 81-86 years) were included in the final analysis, 2404 (47.9%) were women. Admission was classified as acute in 4215 (83.9%) of the patients. Overall ICU and 30-day mortality rates were 22.1% and 32.6%. During ICU stay 23.8% of the patients did not receive specific ICU procedures: ventilation, vasoactive drugs or renal replacement therapy. Frailty (values ≥ 5) was found in 43.1% and was independently related to 30-day survival (HR 1.54; 95% CI 1.38-1.73) for frail versus non-frail. CONCLUSIONS: Among very old patients (≥ 80 years) admitted to the ICU, the consecutive classes in Clinical Frailty Scale were inversely associated with short-term survival. The scale had a very low number of missing data. These findings provide support to add frailty to the clinical assessment in this patient group.