BackgroundThe performance of severity-of-illness scores varies in different scenarios and must be validated prior of being used in a specific settings and geographic regions. Moreover, models’ calibration may deteriorate overtime and performance of such instruments should be reassessed regularly. Therefore, we aimed at to validate the SAPS 3 in a large contemporary cohort of patients admitted to Brazilian ICUs. In addition, we also compared the performance of the SAPS 3 with the MPM0-III.MethodsThis is a retrospective cohort study in which 48,816 (medical admissions = 67.9%) adult patients are admitted to 72 Brazilian ICUs during 2013. We evaluated models’ discrimination using the area under the receiver operating characteristic curve (AUROC). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration).ResultsMean SAPS 3 score was 44.3 ± 15.4 points. ICU and hospital mortality rates were 11.0 and 16.5%. We estimated predicted mortality using both standard (SE) and Central and South American (CSA) customized equations. Predicted mortality rates were 16.4 ± 19.3% (SAPS 3-SE), 21.7 ± 23.2% (SAPS 3-CSA) and 14.3 ± 14.0% (MPM0-III). Standardized mortality ratios (SMR) obtained for each model were: 1.00 (95% CI, 0.98–0.102) for the SAPS 3-SE, 0.75 (0.74–0.77) for the SAPS 3-CSA and 1.15 (1.13–1.18) for the MPM0-III. Discrimination was better for SAPS 3 models (AUROC = 0.85) than for MPM0-III (AUROC = 0.80) (p < 0.001). We applied the calibration belt to evaluate the agreement between observed and expected mortality rates (calibration): the SAPS 3-CSA overestimated mortality throughout all risk classes while the MPM0-III underestimated it uniformly. The SAPS 3-SE did not show relevant deviations from ideal calibration.ConclusionsIn a large contemporary database, the SAPS 3-SE was accurate in predicting outcomes, supporting its use for performance evaluation and benchmarking in Brazilian ICUs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13613-017-0276-3) contains supplementary material, which is available to authorized users.

RESUMO Objetivo: identificar a adesão ao checklist de cirurgia segura, a partir do seu preenchimento, em um hospital geral de referência do interior do Estado de Minas Gerais, bem como, verificar os fatores associados à sua utilização. Métodos: trata-se de estudo transversal, documental, retrospectivo de abordagem quantitativa. A coleta de dados foi realizada por meio da revisão retrospectiva de prontuários de uma amostra de pacientes operados no período de um ano. Foram incluídos os atendimentos de pacientes cirúrgicos de todas as especialidades, com idade de 18 anos ou mais, e período de internação igual ou maior do que 24 horas. A amostra probabilística foi de 423 casos. Resultados: o checklist estava presente em 95% dos prontuários. Porém, apenas 67,4% deles estavam com preenchimento completo. A presença do checklist no prontuário apresentou associação significativa com o risco anestésico do paciente. Não houve diferença no percentual de preenchimento entre os três momentos do checklist: antes da indução anestésica (sign in), antes da incisão cirúrgica (time out ou parada cirúrgica) e antes do paciente deixar a sala de cirurgia (sign out). Também não foram encontradas diferenças significativas em relação ao percentual de preenchimento dos itens de responsabilidade do cirurgião. Considerando o procedimento cirúrgico realizado, foram encontradas incoerências no item lateralidade. Conclusão: apesar do elevado percentual de prontuários com checklist, a presença de incompletude e incoerência pode comprometer os resultados esperados na segurança do paciente cirúrgico.

BackgroundAlthough a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever.MethodsThis work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies.ResultsIn order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine.ConclusionsThis work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.

This study demonstrates the high and growing prevalence of PIMs in the hospital environment, according to Beers and STOPP criteria. Educational measures and specific pharmaceutical interventions for each specialty are needed to change this situation.

One of the main aspects related to non-adherence to combined antiretroviral therapy (cART) for patients infected with the Human Immunodeficiency Virus (HIV) refers to the abandonment of outpatient care. This study was aimed to estimate the loss to follow-up in outpatient HIV care at a Regional Referral Clinic (SAE) for HIV/AIDS in the city of Juiz de Fora, Brazil, and to identify associated factors and predictors. This is a prospective cohort of patients older than 18 years, under cART and regular outpatient care. The study included patients who attended medical visits during July-August 2011. Those who did not return to the clinic for new medical appointments within 90 days after the sixth month of follow up were considered lost to follow-up in outpatient care. Variables with P value ≤0.25 in the univariate analysis were included in a logistic regression model, adopting a significance level of 0.05. Among the 250 patients included in the study, 44 (17.6 %) were lost to follow up in outpatient care. Among these, 38 (86.4 %) were located in the cART delivery database system (SICLOM). Younger patients (≤43 versus >43 years) (OR 2.30 CI 1.06-5.00, P = 0.04), and patients attended by physician "E", when compared with physicians "A", "B", "C" or "D" (OR 5.90 CI 2.64-13.18, P = 0.00) were more likely to be lost to follow-up. Patients admitted in the service for 7 years or more were also more likely to be to lost to follow-up (OR 2.27 CI 1.2-4.4, P = 0.01), although this association did not remain statistically significant in the multivariate analysis. Although the purpose of the study, to identify individual factors associated to loss to follow-up, positives associations with a specific physician and with patients admitted in the service for 7 years or more suggest organizational factors. Although the majority of patients lost to follow-up in outpatient care were detected by SICLOM, a detectable viral load in most of these patients suggest a quality of outpatient HIV care proved ineffective, despite the availability of cART. We conclude on the need for further studies to investigate structural factors associated to loss to follow-up when enhanced retention strategies should be implemented in order to maintain an effective outpatient HIV care.

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The identification of adverse events following immunization (AEFI) and their prompt investigation are important to allow a timely and scientifically based response to the users of immunization services. This article presents an analysis of notified AEFI cases between 1999 and 2005 and their temporal association with 2001 yellow fever vaccination campaign, AEFI notification attributed to yellow fever vaccination rose from 0.06 to 1.32 per 100,000 vaccinees in Brazil, between 1998 and 2000. During the 2001 yellow fever mass vaccination campaign held in Juiz de Fora, Brazil, 12 cases of aseptic meningitis were temporally associated to yellow fever vaccination, but clinical and laboratory data were not available to confirm nor deny causality. Epidemiological studies associated to enhanced surveillance and standardized protocols should take advantage of public health interventions like mass vaccination campaigns and implementation of new vaccination strategies in order to assess and investigate vaccine safety.

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