Prevalence And Treatment Of Hypertension In Urban And Riverside Areas In Porto Velho, The Brazilian Amazon.

Abstract Introduction: Hypertension (HTN) is a preventable cause of cardiovascular morbidity and mortality. To compare the prevalence, awareness, treatment, and control of HTN among urban and riverside populations in Porto Velho, Amazon region. We conducted a cross-sectional study between July and December 2013 based on a household survey of individuals aged 35-80 years. Interviews by using a standardized questionnaire, and blood pressure (BP), weight, height, and waist circumference measurements were performed. HTN was defined when individuals reported having the disease, received antihypertensive medications, or had a systolic BP ≥ 140 mm Hg or diastolic BP ≥ 90 mm Hg. Awareness was based on self-reports and the use of antihypertensive medications. Control was defined as a BP ≤ 140/90 mm Hg. Among the 1410 participants, 750 (53.19%) had HTN and 473 (63.06%) had diagnosis awareness, of whom 404 (85.41%) received pharmacological treatment but with low control rate. The prevalence and treatment rates were higher in the urban areas (55.48% vs. 48.87% [p = 0.02] and 61.25% vs. 52.30% [p < 0.01], respectively). HTN awareness was higher in the riverside area (61.05% vs. 67.36% ; p < 0.01), but the control rates showed no statistically significant difference (22.11% vs. 23.43% ; p = 0.69). HTN prevalence was higher in the urban population than in the riverside population. Of the hypertensive individuals in both areas, <25% had controlled HTN. Comprehensive public health measures are needed to improve the prevention and treatment of systemic arterial HTN and prevent other cardiovascular diseases.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Possibilidades e limites da avaliação da qualidade de vida : análise dos instrumentos WHOQOL e modelos clássicos de qualidade de vida no trabalho = Possibilities and limits of quality of life assessment : analysis of WHOQOL instruments and quality of work life classical models

Universidade Estadual de Campinas . Faculdade de Educação Física