ABPM provides more valuable information regarding cardiovascular risk stratification than office BPs and should be performed, if possible, in every high-risk type 2 diabetic patient. Achieved 24-h ambulatory BPs less than 120/75 mmHg are associated with significant cardiovascular protection and, if confirmed by other studies, may be considered as BP treatment targets.

BackgroundPatients with Chagas disease and segmental wall motion abnormality (SWMA) have worse prognosis independent of left ventricular ejection fraction (LVEF). Cardiac magnetic resonance (CMR) is currently the best method to detect SWMA and to assess fibrosis.ObjectiveTo quantify fibrosis by using late gadolinium enhancement CMR in patients with Chagas disease and preserved or minimally impaired ventricular function (> 45%), and to detect patterns of dependence between fibrosis, SWMA and LVEF in the presence of ventricular arrhythmia.MethodsElectrocardiogram, treadmill exercise test, Holter and CMR were carried out in 61 patients, who were divided into three groups as follows: (1) normal electrocardiogram and CMR without SWMA; (2) abnormal electrocardiogram and CMR without SWMA; (3) CMR with SWMA independently of electrocardiogram.ResultsThe number of patients with ventricular arrhythmia in relation to the total of patients, the percentage of fibrosis, and the LVEF were, respectively: Group 1, 4/26, 0.74% and 74.34%; Group 2, 4/16, 3.96% and 68.5%; and Group 3, 11/19, 14.07% and 55.59%. Ventricular arrhythmia was found in 31.1% of the patients. Those with and without ventricular arrhythmia had mean LVEF of 59.87% and 70.18%, respectively, and fibrosis percentage of 11.03% and 3.01%, respectively. Of the variables SWMA, groups, age, LVEF and fibrosis, only the latter was significant for the presence of ventricular arrhythmia, with a cutoff point of 11.78% for fibrosis mass (p < 0.001).ConclusionEven in patients with Chagas disease and preserved or minimally impaired ventricular function, electrical instability can be present. Regarding the presence of ventricular arrhythmia, fibrosis is the most important variable, its amount being proportional to the complexity of the groups.

Although asbestos causes asbestosis, lung cancer, and mesothelioma, it remains widely used in Brazil, mostly in cement-fiber products. We report the Brazilian mesothelioma mortality trend 1980-2003, using records of the national System of Mortality Information of DATASUS, including all deaths with IX International Disease Classification (ICD9) codes 163.n--pleura cancer during the period 1980-1995; and ICD10 codes c45.n--mesotheliomas and c38.4--pleura cancer for the years 1996-2003. Mesothelioma mortality rates increased over the period studied, from 0.56 to 1.01 deaths per 1,000,000 [corrected] habitants. The total number of mesothelioma deaths nationwide in the period studied was 2,414; the majority (1,415) were in the Southeast region. Mortality was highest among males and people over age 65. Given the history of asbestos exposure in Brazil, our findings support the need for policies that limit or ban the use of this product.

OBJECTIVE:This paper aims to describe and discuss a minimization procedure specifically designed for a clinical trial that evaluates treatment efficacy for OCD patients.METHOD:Aitchison’s compositional distance was used to calculate vectors for each possibility of allocation in a covariate adaptive method. Two different procedures were designed to allocate patients in small blocks or sequentially one-by-one.RESULTS:We present partial results of this allocation procedure as well as simulated data. In the clinical trial for which this procedure was developed, successful balancing between treatment arms was achieved. Separately, in an exploratory analysis, we found that if the arrival order of patients was altered, most patients were allocated to a different treatment arm than their original assignment.CONCLUSION:Our results show that the random arrival order of patients determine different assignments and therefore maintains the unpredictability of the allocation method. We conclude that our proposed procedure allows for the use of a large number of prognostic factors in a given allocation decision. Our method seems adequate for the design of the psychiatric trials used as models. Trial registrations are available at clinicaltrials.gov NCT00466609 and NCT00680602.

Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis (PT) cases reported yearly. Rapid and accurate diagnosis of SNPT could provide lower morbidity and mortality, and case detection at a less contagious status. The main objective of this work is to evaluate a prediction model for diagnosis of SNPT, useful for outpatients who are attended in settings with limited resources. The data used for developing the proposed models werecomprised of 136 patients from health care units. They were referred to the University Hospital in Rio de Janeiro, Brazil, from March 2001 to September 2002, with clinical-radiological suspicion of SNPT. Only symptoms and physical signs were used for constructing the neural network (NN) modelling, which was able to correctly classify 77% of patients from a test sample. The achievements of the NN model suggest that mathematical modelling, developed for classifying SNPT cases could be a useful tool for optimizing application of more expensive tests, and to avoid costs of unnecessary anti-PT treatment. In addition, the main features extracted by the neural model are shown to agree with current analysis from experts in the field.

Este trabalho desenvolve um sistema para predição da soroprevalência da Hepatite A. Para isto, são considerados os modelos de regressão de logística e redes neurais artificiais. O desempenho de tais modelos é medido através da taxa de classificação incorreta em uma amostra do município de Duque de Caxias, Rio de Janeiro, que possui elevada prevalência da doença. Resultados mostram que o modelo neural, aplicado sobre a informação relevante extraída do modelo de regressão logística, apresenta um bom desempenho, alcançando uma eficiência de classificação geral acima de 88%.

In order to demonstrate how DEA modeling can be helpful to aid decision making relative to the Brazilian Teaching Hospital Policy by means of hospital performance assessment, we develop a case study with 30 general hospitals linked to Brazilian Federal Universities. We consider data on medical care (Medical Model-MM), teaching and research (Teaching-Research Model-TRM) and use the software IDEAL (Interactive Data Envelopment Analysis Laboratory) as a tool for the units' efficiency evaluation. IDEAL, developed in Brazil, is unique in providing a 3-D frontiers visualization, assisting in exploratory analysis and allowing a better understanding of the DEA modeling (envelopment and multiplier). Both models are input-oriented and each hospital is categorized according to its relative efficiency in the MM and TRM. In this phase, it is very important to discuss with the decision-makers the results and patterns of the DEA models. Finally, the modelling indicates the necessary changes for the inefficient units and generates recommendations for teaching ratios and public financing.

BackgroundMajor Depressive Disorder (MDD) is one of the most common mental illnesses in psychiatry, being considered a risk factor for Acute Coronary Syndrome (ACS).ObjectiveTo assess the prevalence of MDD in ACS patients, as well as to analyze associated factors through the interdependence of sociodemographic, lifestyle and clinical variables.MethodsObservational, descriptive, cross-sectional, case-series study conducted on patients hospitalized consecutively at the coronary units of three public hospitals in the city of Rio de Janeiro over a 24-month period. All participants answered a standardized questionnaire requesting sociodemographic, lifestyle and clinical data, as well as a structured diagnostic interview for the DSM-IV regarding ongoing major depressive episodes. A general log-linear model of multivariate analysis was employed to assess association and interdependence with a significance level of 5%.ResultsAnalysis of 356 patients (229 men), with an average and median age of 60 years (SD ± 11.42, 27-89). We found an MDD point prevalence of 23%, and a significant association between MDD and gender, marital status, sedentary lifestyle, Killip classification, and MDD history. Controlling for gender, we found a statistically significant association between MDD and gender, age ≤ 60 years, sedentary lifestyle and MDD history. The log-linear model identified the variables MDD history, gender, sedentary lifestyle, and age ≤ 60 years as having the greatest association with MDD.ConclusionDistinct approaches are required to diagnose and treat MDD in young women with ACS, history of MDD, sedentary lifestyle, and who are not in stable relationships.