Estudio epidemiológico de las patologías corneales en la especie canina en el Hospital Docente Universitario durante el periodo 2001-2011

Programa de doctorado: Clínica e investigación terapéutica.

A vulnerability analysis of the temperate forests of south central Chile A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122 2005: 9-21.

Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate. spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that: approximately 27000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network. (C) 2004 Elsevier Ltd. All tights reserved.

Sistema de predição de alarmes em processos industriais por classificação não-supervisionada

Um sistema de predição de alarmes com a finalidade de auxiliar a implantação de uma política de manutenção preditiva industrial e de constituir-se em uma ferramenta gerencial de apoio à tomada de decisão é proposto neste trabalho. O sistema adquire leituras de diversos sensores instalados na planta, extrai suas características e avalia a saúde do equipamento. O diagnóstico e prognóstico implica a classificação das condições de operação da planta. Técnicas de árvores de regressão e classificação não-supervisionada são utilizadas neste artigo. Uma amostra das medições de 73 variáveis feitas por sensores instalados em uma usina hidrelétrica foi utilizada para testar e validar a proposta. As medições foram amostradas em um período de 15 meses.

Analysis of the claims data of a life insurance portfolio

Mestrado em Ciências Actuariais

Integrated Analysis of Molecular and Clinical Prognostic Factors in Stage II/III Colon Cancer.

Background The prognostic potential of individual clinical and molecular parameters in stage II/III colon cancer has been investigated, but a thorough multivariable assessment of their relative impact is missing. Methods Tumors from patients (N = 1404) in the PETACC3 adjuvant chemotherapy trial were examined for BRAF and KRAS mutations, microsatellite instability (MSI), chromosome 18q loss of heterozygosity (18qLOH), and SMAD4 expression. Their importance in predicting relapse-free survival (RFS) and overall survival (OS) was assessed by Kaplan-Meier analyses, Cox regression models, and recursive partitioning trees. All statistical tests were two-sided. Results MSI-high status and SMAD4 focal loss of expression were identified as independent prognostic factors with better RFS (hazard ratio [HR] of recurrence = 0.54, 95% CI = 0.37 to 0.81, P = .003) and OS (HR of death = 0.43, 95% CI = 0.27 to 0.70, P = .001) for MSI-high status and worse RFS (HR = 1.47, 95% CI = 1.19 to 1.81, P < .001) and OS (HR = 1.58, 95% CI = 1.23 to 2.01, P < .001) for SMAD4 loss. 18qLOH did not have any prognostic value in RFS or OS. Recursive partitioning identified refinements of TNM into new clinically interesting prognostic subgroups. Notably, T3N1 tumors with MSI-high status and retained SMAD4 expression had outcomes similar to stage II disease. Conclusions Concomitant assessment of molecular and clinical markers in multivariable analysis is essential to confirm or refute their independent prognostic value. Including molecular markers with independent prognostic value might allow more accurate prediction of prognosis than TNM staging alone.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Anger, preoccupied attachment and domain disorganisation in borderline personality disorder

Emotional dysregulation and attachment insecurity have been reported in borderline personality disorder (BPD). Domain disorganization, evidenced in poor regulation of emotions and behaviors in relation to the demands of different social domains, may be a distinguishing feature of BPD. Understanding the interplay between these factors may be critical for identifying interacting processes in BPD and potential subtypes of BPD. Therefore, we examined the joint and interactive effects of anger, preoccupied attachment, and domain disorganization on BPD traits in a clinical sample of 128 psychiatric patients. The results suggest that these factors contribute to BPD both independently and in interaction, even when controlling for other personality disorder traits and Axis I symptoms. In regression analyses, the interaction between anger and domain disorganization predicted BPD traits. In recursive partitioning analyses, two possible paths to BPD were identified: high anger combined with high domain disorganization and low anger combined with preoccupied attachment. These results may suggest possible subtypes of BPD or possible mechanisms by which BPD traits are established and maintained.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Variation in length, fecundity and survival of pink wax scale, Ceroplastes rubens Maskell (Hemiptera: Coccidae), on umbrella trees

We investigated some of the factors that may lead to outbreaks of pink wax scale, Ceroplastes rubens Maskell, on umbrella trees, Schefflera actinophylla (Endl.). Estimates of birth and death rates of pink wax scale were high and variable within and among trees; variation in these rates was not related to scale density. Adult fecundity correlated significantly but weakly with adult test length; mean fecundity was 292 eggs per female with a range of 5-1178. Adult test length and its variance decreased weakly with increasing density. Field experiments showed that mortality of C. rubens is greatest during the first 24 hours after hatching when approximately half disappear. The rate of loss decreases over time with 0.3% of initial motile first-instar nymphs surviving to maturity. Rates of loss varied significantly between trees, indicating that some trees are more suitable for scale colonisation and survival.

Association between LDL-C, Non HDL-C, and Apolipoprotein B Levels with Coronary Plaque Regression

Background:Previous reports have inferred a linear relationship between LDL-C and changes in coronary plaque volume (CPV) measured by intravascular ultrasound. However, these publications included a small number of studies and did not explore other lipid markers.Objective:To assess the association between changes in lipid markers and regression of CPV using published data.Methods:We collected data from the control, placebo and intervention arms in studies that compared the effect of lipidlowering treatments on CPV, and from the placebo and control arms in studies that tested drugs that did not affect lipids. Baseline and final measurements of plaque volume, expressed in mm3, were extracted and the percentage changes after the interventions were calculated. Performing three linear regression analyses, we assessed the relationship between percentage and absolute changes in lipid markers and percentage variations in CPV.Results:Twenty-seven studies were selected. Correlations between percentage changes in LDL-C, non-HDL-C, and apolipoprotein B (ApoB) and percentage changes in CPV were moderate (r = 0.48, r = 0.47, and r = 0.44, respectively). Correlations between absolute differences in LDL-C, non‑HDL-C, and ApoB with percentage differences in CPV were stronger (r = 0.57, r = 0.52, and r = 0.79). The linear regression model showed a statistically significant association between a reduction in lipid markers and regression of plaque volume.Conclusion:A significant association between changes in different atherogenic particles and regression of CPV was observed. The absolute reduction in ApoB showed the strongest correlation with coronary plaque regression.

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Salvage radiosurgery for selected patients with recurrent malignant gliomas.

Purpose. To analyse the survival after salvage radiosurgery and to identify prognostic factors. Methods. We retrospectively reviewed 87 consecutive patients, with recurrent high-grade glioma, that underwent stereotactic radiosurgery between 1997 and 2010. We evaluated the survival after initial diagnosis and after reirradiation. The prognostic factors were analysed by bivariate and multivariate Cox regression model. Results. The median age was 48 years old. The primary histology included anaplastic astrocytoma (47%) and glioblastoma (53%). A margin dose of 18 Gy was administered in the majority of cases (74%). The median survival after initial diagnosis was 21 months (39 months for anaplastic astrocytoma and 18.5 months for glioblastoma) and after reirradiation it was 10 months (17 months for anaplastic astrocytoma and 7.5 months for glioblastoma). In the bivariate analyses, the prognostic factors significantly associated with survival after reirradiation were age, tumour and treatment volume at recurrence, recursive partitioning analyses classification, Karnofsky performance score, histology, and margin to the planning target volume. Only the last four showed significant association in the multivariate analyses. Conclusion. stereotactic radiosurgery is a safe and may be an effective treatment option for selected patients diagnosed with recurrent high-grade glioma. The identified prognostic factors could help individualise the treatment.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Prediction of hemodynamic severity of coarctation by magnetic resonance imaging.

A published formula containing minimal aortic cross-sectional area and the flow deceleration pattern in the descending aorta obtained by cardiovascular magnetic resonance predicts significant coarctation of the aorta (CoA). However, the existing formula is complicated to use in clinical practice and has not been externally validated. Consequently, its clinical utility has been limited. The aim of this study was to derive a simple and clinically practical algorithm to predict severe CoA from data obtained by cardiovascular magnetic resonance. Seventy-nine consecutive patients who underwent cardiovascular magnetic resonance and cardiac catheterization for the evaluation of native or recurrent CoA at Children's Hospital Boston (n = 30) and the University of California, San Francisco (n = 49), were retrospectively reviewed. The published formula derived from data obtained at Children's Hospital Boston was first validated from data obtained at the University of California, San Francisco. Next, pooled data from the 2 institutions were analyzed, and a refined model was created using logistic regression methods. Finally, recursive partitioning was used to develop a clinically practical prediction tree to predict transcatheter systolic pressure gradient ≥ 20 mm Hg. Severe CoA was present in 48 patients (61%). Indexed minimal aortic cross-sectional area and heart rate-corrected flow deceleration time in the descending aorta were independent predictors of CoA gradient ≥ 20 mm Hg (p <0.01 for both). A prediction tree combining these variables reached a sensitivity and specificity of 90% and 76%, respectively. In conclusion, the presented prediction tree on the basis of cutoff values is easy to use and may help guide the management of patients investigated for CoA.