A heuristic index for selecting similar categories in multiple correspondence analysis applied to living donor kidney transplantation

Characterization of the variation of health care taking into account the costs of hospital admissions for acute myocardial infarction in Brazilian Unified Health System

INTRODUCTION

The global epidemiologic scenario indicates an increase in cardiovascular disease rates, with special emphasis on acute myocardial infarction (AMI), owing to its large magnitude and severity. In Brazil, coronary diseases now account for about 5% of hospital admission expenditures.

OBJECTIVE

To characterize the admissions in the Brazilian Unified Health System of patients with AMI, by identifying clusters suggested by a traditional cluster analysis and by a multiple correspondence analysis (MCA).

METHODS

The records of the Hospital Information System/Brazilian Unified Health System with a primary diagnosis of AMI in the State of Rio de Janeiro, Brazil, 2002, were selected and subsequently related to the records of the Mortality Information System. Next, an MCA and a metric called the tolerance distance were used for cluster identification. The variable of interest was "hospital expenditures", classified into two categories (above and below BRL 905).

RESULTS

"Higher costs" were associated with "use of the Intensive Care Unit (ICU)" and "moderate severity of the case" and "lower costs" with "low severity" and "nonuse of the ICU". On the other hand, high severity cases, with no apparent association with "use of ICU" or other categories. Other associations identified were "lower costs" and "no displacement of the patient," "female," "age between 56 and 75 years," "death within 30 days," and "death within 1 year".

CONCLUSIONS

The nonclustered characteristic of the most serious cases and the association between "deaths" and "lower costs" suggests that the technological resources available during hospitalization for AMI are not being properly used.

Análise de Correspondência: bases teóricas na interpretação de dados categóricos em Ciências da Saúde

Na área biomédica, a ocorrência de dados categóricos é comum, e métodos de análise específicos para este tipo de dado são usados para revelar padrões existentes. A Análise de Correspondência é uma dessas técnicas, utilizada na análise de tabelas de contingência de grande porte. A maioria dos trabalhos publicados em periódicos brasileiros foca apenas na sua interpretação gráfica, não abordando outras potencialidades da técnica. O objetivo do trabalho é mostrar a técnica não limitada à análise gráfica, mas também utilizar estatísticas que permitem sua análise quantitativa. Exemplo mostra que a análise gráfica é enriquecida com a utilização dessas estatísticas, e que a inclusão de uma categoria com baixa ocorrência pode ser considerada como categoria suplementar devido à sua baixa contribuição à inércia. Assim, diminui-se a subjetividade na análise, sendo possível revelar a relação entre as categorias com a análise de resíduos, aspecto este não facilmente observado graficamente. Comparação com a Análise de Componentes Principais mostrou a vantagem da técnica.

Application of artificial neural networks in renal transplantation: classification of nephrotoxicity and acute cellular rejection episodes

Complications associated with kidney transplantation and immunosuppression can be prevented or treated effectively if diagnosed in the early stages by posttransplant monitoring. One of the major problems is diseases that occur during the first year after kidney transplantation. For this purpose, we used different classifiers to predict events of nephrotoxicity versus acute cellular rejection episodes. The classifiers were evaluated according to values of sensitivity, specificity and area under ROC curves (RCA). The classifier with better accuracy rate for nephrotoxicity achieved the value of 75.68% and RCA classifier reached the accuracy of 80.89%. These results are encouraging, with rates of accuracy and error consistent with work purpose.

Multiple correspondence analysis in predictive logistic modelling: application to a living-donor kidney transplantation data

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors. These models were compared to two other models, ordinarily built by backward and forward stepwise selection, which yielded, respectively, five and two predictors. After internal validation, the models performance statistics showed similar results. Likelihood ratio tests suggested that backward approach achieved a better fit than the forward modelling in both methods and the Vuong's non-nested test between backward-built models suggested that these were undistinguishable. We conclude that the tolerance distance, in combination with MCA, could be a feasible method for variable selection in logistic modelling, when there are several categorical predictors.