Modeling assembly program with constraints. A contribution to WCET problem

Dissertação para obtenção do Grau de Mestre em Lógica Computacional

Risk-based decision support system for life cycle management of industrials plants

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Frequency of human bocavirus respiratory infections among at-risk patients in São Paulo, Brazil

BACKGROUND AND OBJECTIVES: Human Bocavirus (HBoV) has been described since 2005 as an etiological agent of respiratory virus infections. From 2001 to 2008 we investigated the etiology of HBoV among adults and children in different groups at risk of presenting complications arising from acute respiratory infection, the investigation was carried out in a tertiary hospital health care system in Brazil. METHODS: HBoV DNA was assayed in 598 respiratory samples from community and hospitalized patients by PCR. RESULTS: Of the 598 tested samples, 2.44% (8/328) of children, including five children with heart disease, and 0.4% (1/270) of adult bone-marrow-transplant were HBoV positive. CONCLUSIONS: These data suggested lower HBoV frequency among different at-risk patients and highlights the need to better understand the real role of HBoV among acute respiratory symptomatic patients.

Prevalence and risk factors for intestinal protozoa infection in elderly residents at Long Term Residency Institutions in Southeastern Brazil

This study determined the prevalence of intestinal protozoa in Long Term Residency Institutions for the Elderly (ILPI) in elders, nurses and food handlers, identifying the risk factors associated with the infections. Stool samples taken from the elderly (n = 293), nurses (63) and food handlers (19) were studied. Questionnaires were used with questions related to sociodemographic variables, health, behavior and health characteristics. Stool samples were examined using the techniques of Faust and Ziehl Neelsen, and the prevalence of G. duodenalis, Cryptosporidium spp., E. histolytica/dispar in the elderly was 4.0%, 1.0% and 0.3% respectively. Nurses and food handlers showed 4.8% and 5.2% positivity only for G. duodenalis, respectively. The origin of the individuals and contact with domestic animals has been associated with infection by G. duodenalis in the elderly, and contact with domestic animals was considered a risk factor for infection. The last stool examinations were related to Cryptosporidium spp.. None of the variables were associated with E. histolytica/dispar. The frequency of hand washing was significantly associated with G. duodenalis among nurses. The frequency of positive samples of G. duodenalis, Cryptosporidium spp., E. histolytica/dispar showed that ILPIs environments are conducive to this occurring due to contact between the elderly, nurses and food handlers, which are often poorly trained in hygiene procedures and food handling.

Integration of sediment contamination with multi–biomarker responses in a novel bioindicator candidate (Sepia officinalis) for risk assessment in impacted estuaries

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Characterizing the Risk Profiles of Intensive Care Units

OBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles. METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality. MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models. CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio.