RESUMOObjetivo: Comparar a sensibilidade ao contraste nas faixas etárias de 20 a 25, 40 a 45 e acima de 60 anos de idade. Métodos: Realizouse um estudo transversal com indivíduos de diferentes idades, com acuidade visual superior a 20/25, sem doença ocular e sem cirurgia oftalmológica prévia. A acuidade visual foi medida pelo teste de Snellen e, a sensibilidade ao contraste, pelo aparelho OPTEC 3500P®. A análise estatística foi realizada pelo teste de Wilcoxon, considerando um intervalo de confiança de 95%. Resultados: Em relação aos pacientes de 20 a 25 anos, os de 40 a 45 anos não apresentaram diminuição significativa da sensibilidade ao contraste em nenhuma das frequências espaciais avaliadas. Comparando os pacientes acima de 60 anos aos de 40 a 45 anos, houve diminuição da sensibilidade ao contraste nas frequências de 6,0 a 18,0 cpg no modo diurno e de 3,0 a 18,0 cpg no noturno. Já quando comparados aos de 20 a 25 anos, os pacientes maiores de 60 anos mostraram diminuição nas frequências de 3,0 a 18 cpg no modo diurno e em todas as frequências no modo noturno. Conclusão: A função de sensibilidade ao contraste parece diminuir com a idade, após os 45 anos, principalmente nas médias e altas frequências espaciais. Isso pode impactar na leitura, na direção e na mobilidade, dentre outras atividades diárias.Descritores
RESUMO PALAVRAS-CHAVE IntroduçãoCasos relatados de aspergilose invasiva do sistema nervoso central (SNC) têm apresentado freqüência crescente, sobretudo em função do aumento de pacientes imunossuprimidos 1,4,7 . A aspergilose representa um espectro patológico determinado por espécies do gênero Aspergillus, que são os fungos mais comuns do planeta, presentes no solo, adubo, feno, cereais, vegetais e até em ambiente hospitalar, e que penetram no organismo humano por várias vias, especialmente inalatória 9 .Infecções fúngicas superficiais e sistêmicas são relativamente freqüentes após realização de transplantes de órgãos sólidos. Os patógenos fúngicos mais comuns são, respectivamente, a Candida e o Aspergillus 2,3 . No SNC, o Aspergillus é o segundo fungo mais comum, atrás apenas do Cryptococcus 2 .
Background Bloodstream infection (BSI) - Central and Non-Central Line Associated - and infections of the lower respiratory tract (RESP) - pneumonia and non pneumonia lower respiratory infections - are some of the main causes of unexpected death in Intensive Care Units (ICUs). Although the leading causes of these infections are already known, risk prediction models can be used to identify unexpected cases. This study aims to investigate whether or not it is possible to build multivariate models to predict BSI and RESP events. Methods Univariate and multivariate analysis using multiple logistic regression models were built to predict BSI and RESP events. ROC curve analysis was used to validate each model. Independent variables: 29 quantitative parameters and 131 categorical variables. BSI and RESP were identified using Brazilian Health Regulatory Agency protocols with data collected between January and November 2020 from a medical-surgical ICU in a Brazilian Hospital. Definitions: if an infection is 5% or less likely to occur according to the model used and it eventually occurs, it will be classified as “unexpected”, or else, if an infection is 10% or less likely to occur, it will be classified as “probably unexpected”. Otherwise, infections will be classified as “expected”. Patients with a 30% or more risk for BSI or RESP will be classified as “high risk”. Results A total of 1,171 patients were accessed: 70 patients with BSI (95% confidence interval [CI], 3.1%-5%), 66 patients with RESP (95% CI, 2.9%-4.7%), 235 deaths (95% CI, 11.8%-14.9%). Of the 160 potential risk factors evaluated, logistic models for BSI and RESP identified respectively five and seven predictors (Tables 1 and 2, and Figure 1). Patients admitted to the ICU with Covid-19 had a three fold BSI risk and five times more RESP risk than patients without this diagnosis. Table 1. Independent predictors of Infections of the Lower Respiratory Tract in ICU: results of multivariate analysis performed using a logistic regression model. Table 2. Independent predictors of Bloodstream Infection Events in ICU (Central Line-Associated BSI + Non-central Line Associated BSI): results of multivariate analysis performed using a logistic regression model. Figure 1. Receiver operating characteristic (ROC) curve for the fitted models: area under the ROC Curves were higher than 0.85 for both models. Conclusion The built models make possible the identification of the expected infections and the unexpected ones. Three main course of actions can be taken using these models and associated data: (1) Before the occurrence of BSI and RESP: to place high risk patients under more rigorous infection surveillance. (2) After the occurrence of BSI or RESP: to investigate “unexpected” infections. (3) At discharge: to identify high risk patients with no infections for further studies. Disclosures All Authors: No reported disclosures
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