A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Geographic Information Systems (GIS) in Assessing Dental Health

The present study investigated the distribution profile of dental caries and its association with areas of social deprivation at the individual and contextual level. The cluster sample consisted of 1,002 12-year-old schoolchildren from Piracicaba, SP, Brazil. The DMFT Index was used for dental caries and the Care Index was used to determine access to dental services. On the individual level, variables were associated with a better oral status. On the contextual level, areas were not associated with oral status. However, maps enabled determining that the central districts have better social and oral conditions than the deprived outlying districts.

PERFORMANCE OF THE MODEL CALLED ACQUANETXL IN THE ALLOCATION OF WATER IN COMPLEX WATER RESOURCE SYSTEMS

This article presents a tool for the allocation analysis of complex systems of water resources, called AcquaNetXL, developed in the form of spreadsheet in which a model of linear optimization and another nonlinear were incorporated. The AcquaNetXL keeps the concepts and attributes of a decision support system. In other words, it straightens out the communication between the user and the computer, facilitates the understanding and the formulation of the problem, the interpretation of the results and it also gives a support in the process of decision making, turning it into a clear and organized process. The performance of the algorithms used for solving the problems of water allocation was satisfactory especially for the linear model.

Smart Alerts: Development of Software to Optimize Data Monitoring

Dherte PM, Negrao MPG, Mori Neto S, Holzhacker R, Shimada V, Taberner P, Carmona MJC - Smart Alerts: Development of a Software to Optimize Data Monitoring. Background and objectives: Monitoring is useful for vital follow-ups and prevention, diagnosis, and treatment of several events in anesthesia. Although alarms can be useful in monitoring they can cause dangerous user`s desensitization. The objective of this study was to describe the development of specific software to integrate intraoperative monitoring parameters generating ""smart alerts"" that can help decision making, besides indicating possible diagnosis and treatment. Methods: A system that allowed flexibility in the definition of alerts, combining individual alarms of the parameters monitored to generate a more elaborated alert system was designed. After investigating a set of smart alerts, considered relevant in the surgical environment, a prototype was designed and evaluated, and additional suggestions were implemented in the final product. To verify the occurrence of smart alerts, the system underwent testing with data previously obtained during intraoperative monitoring of 64 patients. The system allows continuous analysis of monitored parameters, verifying the occurrence of smart alerts defined in the user interface. Results: With this system a potential 92% reduction in alarms was observed. We observed that in most situations that did not generate alerts individual alarms did not represent risk to the patient. Conclusions: Implementation of software can allow integration of the data monitored and generate information, such as possible diagnosis or interventions. An expressive potential reduction in the amount of alarms during surgery was observed. Information displayed by the system can be oftentimes more useful than analysis of isolated parameters.

Model for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic

Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.

Prática e duração do aleitamento materno de crianças matriculadas em escolas particulares do município de São Paulo, SP

presente estudo descreve a prática de aleitamento materno e verifica possíveis fatores de associação com a duração do aleitamento materno exclusivo e aleitamento materno em crianças de escolas particulares do município de São Paulo. Fizeram parte do estudo 566 crianças, com 2 a 6 anos completos de idade. Foi considerada como variável dependente a duração do aleitamento materno (aleitamento materno exclusivo e aleitamento materno), e como independente idade e escolaridade materna, condição de trabalho da mãe e sexo da criança. A caracterização da amostra é apresentada por meio de distribuições de freqüências. A variável aleitamento materno foi descrita em categorias, e para análise foi utilizada como variável contínua. Para a análise da relação entre duração do aleitamento materno e as variáveis independentes utilizou-se a técnica de regressão múltipla de Cox adotando-se critério p < 0,05 para decisão de significância. Não houve associação entre as variáveis estudadas e tempo de duração das duas formas de aleitamento. Cerca de 80% das crianças deixaram de ser amamentadas exclusivamente antes dos seis meses de vida, o que mostra a necessidade de continuar o desenvolvimento de ações para incentivo e apoio à amamentação

GIS applied to geotechnical and environmental risk management in a Brazilian oil pipeline

The paper presents the development of a decision support system for the management of geotechnical and environmental risks in oil pipelines using a geographical information system. The system covers a 48.5 km long section of the So Paulo to Brasilia (OSBRA) oil pipeline, which crosses three municipalities in the northeast region of the So Paulo state (Brazil) and represents an area of 205.8 km(2). The spatial database was created using geo-processing procedures, surface and intrusive investigations and geotechnical reports. The risk assessment was based mainly on qualitative models (relative numeric weights and multicriteria decision analysis) and considered pluvial erosion, slope movements, soil corrosion and third party activities. The maps were produced at a scale of 1:10,000.

Planning and operation of large-scale water distribution systems with preemptive priorities

This paper describes the development of an optimization model for the management and operation of a large-scale, multireservoir water supply distribution system with preemptive priorities. The model considers multiobjectives and hedging rules. During periods of drought, when water supply is insufficient to meet the planned demand, appropriate rationing factors are applied to reduce water supply. In this paper, a water distribution system is formulated as a network and solved by the GAMS modeling system for mathematical programming and optimization. A user-friendly interface is developed to facilitate the manipulation of data and to generate graphs and tables for decision makers. The optimization model and its interface form a decision support system (DSS), which can be used to configure a water distribution system to facilitate capacity expansion and reliability studies. Several examples are presented to demonstrate the utility and versatility of the developed DSS under different supply and demand scenarios, including applications to one of the largest water supply systems in the world, the Sao Paulo Metropolitan Area Water Supply Distribution System in Brazil.

Definition of priority areas for forest conservation through the ordered weighted averaging method

The general objective of this study was to evaluate the ordered weighted averaging (OWA) method, integrated to a geographic information systems (GIS), in the definition of priority areas for forest conservation in a Brazilian river basin, aiming at to increase the regional biodiversity. We demonstrated how one could obtain a range of alternatives by applying OWA, including the one obtained by the weighted linear combination method and, also the use of the analytic hierarchy process (AHP) to structure the decision problem and to assign the importance to each criterion. The criteria considered important to this study were: proximity to forest patches; proximity among forest patches with larger core area; proximity to surface water; distance from roads: distance from urban areas; and vulnerability to erosion. OWA requires two sets of criteria weights: the weights of relative criterion importance and the order weights. Thus, Participatory Technique was used to define the criteria set and the criterion importance (based in AHP). In order to obtain the second set of weights we considered the influence of each criterion, as well as the importance of each one, on this decision-making process. The sensitivity analysis indicated coherence among the criterion importance weights, the order weights, and the solution. According to this analysis, only the proximity to surface water criterion is not important to identify priority areas for forest conservation. Finally, we can highlight that the OWA method is flexible, easy to be implemented and, mainly, it facilitates a better understanding of the alternative land-use suitability patterns. (C) 2008 Elsevier B.V. All rights reserved.

Espacialização da temperatura do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais

The aim of this study was to establish a digital elevation model and its horizontal resolution to interpolate the annual air temperature for the Alagoas State by means of multiple linear regression models. A multiple linear regression model was adjusted to series (11 to 34 years) of annual air temperatures obtained from 28 weather stations in the states of Alagoas, Bahia, Pernambuco and Sergipe, in the Northeast of Brazil, in function of latitude, longitude and altitude. The elevation models SRTM and GTOPO30 were used in the analysis, with original resolutions of 90 and 900 m, respectively. The SRTM was resampled for horizontal resolutions of 125, 250, 500, 750 and 900 m. For spatializing the annual mean air temperature for the state of Alagoas, a multiple linear regression model was used for each elevation and spatial resolution on a grid of the latitude and longitude. In Alagoas, estimates based on SRTM data resulted in a standard error of estimate (0.57 degrees C) and dispersion (r(2) = 0.62) lower than those obtained from GTOPO30 (0.93 degrees C and 0.20). In terms of SRTM resolutions, no significant differences were observed between the standard error (0.55 degrees C; 750 m - 0.58 degrees C; 250m) and dispersion (0.60; 500 m - 0.65; 750 m) estimates. The spatialization of annual air temperature in Alagoas, via multiple regression models applied to SRTM data showed higher concordance than that obtained with the GTOPO30, independent of the spatial resolution.

Peach brown rot incidence related to pathogen infection at different stages of fruit development in an organic peach production system

Brown rot, caused by Monilinia fructicola, is the most widespread disease for organic peach production systems in Brazil. The objective of this study was to determine the favorable periods for latent infection by M. fructicola in organic systems. The field experiment was carried out during 2006, 2007 and 2008 using the cultivar Aurora. After thinning fruits were bagged using white paraffin bags, and the treatments were performed by removing the bags and exposing the fruit for four days to the natural infection during each of seven fruit stages from pit hardening to harvest. Throughout the entire growing season, the conidial density and the weather variables were measured and related to the disease incidence using multiple regression analyses. At the fourth day after harvest in each season, the cumulative disease incidence was assessed, and it ranged from 40 to 98%. The incidence of brown rot on fruit that were exposed during the embryo growing stage was lower than that of unbagged fruit throughout the entire season in 2006 and 2008. The relative humidity and the conidia density were significantly correlated to disease incidence. Based on our results, M. fructicola can infect peaches during any stage of fruit development, and control of the disease must be revised to account for organic peach production systems. (C) 2011 Elsevier Ltd. All rights reserved.

Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Sacchetrum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index (LA!), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300). The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.

Exploratory Approach to the use of Techniques of Regression Analysis and Pooled Analysis in Studies of Consumer Behavior

This paper is part of a large study to assess the adequacy of the use of multivariate statistical techniques in theses and dissertations of some higher education institutions in the area of marketing with theme of consumer behavior from 1997 to 2006. The regression and conjoint analysis are focused on in this paper, two techniques with great potential of use in marketing studies. The objective of this study was to analyze whether the employement of these techniques suits the needs of the research problem presented in as well as to evaluate the level of success in meeting their premisses. Overall, the results suggest the need for more involvement of researchers in the verification of all the theoretical precepts of application of the techniques classified in the category of investigation of dependence among variables.

Depression in women living with HIV: clinical and psychosocial correlates

The number of Brazilian women living with HIV has increased significantly in past years, rendering studies of their particular care demands including psychiatric issues. This study measures the prevalence of major depression, using the Structured Clinical Interview for DSM-IV Axis I Disorders, in a sample of 120 women living with HIV in treatment at a reference centre in So Paulo. Socio-demographic variables, HIV-related clinical and laboratory data, including CD4+ cell counts and HIV plasma viral loads, as well as psychosocial features (intimate relationships, disclosure of HIV serostatus, partner`s serostatus and patient`s emotional and financial support) were investigated as factors potentially associated with depression. The prevalence of major depression at the time of evaluation was 25.8% (95% CI 18.2-33.4%). Clinical status (p = 0.002), lack of emotional support (p = 0.02), use of antidepressants (p = 0.028) and length of time since HIV diagnosis (p = 0.05) were associated with major depression in univariate analysis. In multivariate multiple-regression model, HIV clinical status, lack of emotional support and higher plasma viral loads were associated with depression. Sixty per cent of the women have a major depression diagnosis during lifetime. We conclude that major depression is highly prevalent among women living with HIV, but it is still underdiagnosed and undertreated.

Echocardiographic predictors of functional capacity in endomyocardial fibrosis patients

Endomyocardial fibrosis (EMF) is a restrictive cardiomyopathy manifested mainly by diastolic heart failure. It is recognized that diastole is an important determinant of exercise capacity. The purpose of this study was to determine whether resting echocardiographic parameters might predict oxygen consumption (VO(2p)) by ergoespirometry and the prognostic role of functional capacity in EMF patients. A total of 32 patients with biventricular EMF (29 women, 55.3 +/- 11.4 years) were studied by echocardiography and ergoespirometry. The relationship between the echocardiographic indexes and the percentage of predicted VO(2p) (%VO(2p)) was investigated by the `stepwise` linear regression analysis. The median VO(2p) was 11 +/- 3 mL/kg/min and the %VO(2p) was 53 +/- 9%. There was a correlation of %VO(2p) with an average of A` at four sites of the mitral annulus (A` peak, r = 0.471, P = 0.023), E`/A` of the inferior mitral annulus (r = -0.433, P = 0.044), and myocardial performance index (r = -0.352, P = 0.048). On multiple regression analysis, only A` peak was an independent predictor of %VO(2p) (%VO(2p)= 26.34 + 332.44 x A` peak). EMF patients with %VO(2p)< 53% had an increased mortality rate with a relative risk of 8.47. In EMF patients, diastolic function plays an important role in determining the limitations to exercise and %VO(2p) has a prognostic value.