Influence of “Outliers” on the Association between Laboratory Data and Histopathological Findings in Liver Biopsy

Abstract

Discriminant analysis techniques were used to predict the histopathological findings in liver biopsy specimens in asymptomatic patients with slightly to moderately raised routine liver tests. Moderate to severe fibrosis and/or inflammation were treated as indication for biopsy. Two methods were used to classify patients. One was the dichotomous discrimination between “biopsy necessary” or “biopsy not necessary” groups of patients. The other involved combining two discriminant functions trained separately for recognition of fibrosis or inflammation, and then combined to predict the biopsy necessity. Detection of outliers by standard techniques, directly available in the SPSS-X package, was performed before starting discrimination procedures. Both “sharp” assignment rules and continuous scoring rules were applied to the classification problem. The correct classification rate reached over 85% for the algorithms tested. In the majority of cases the classification was found to be “non-doubtful”. Elimination of outliers (especially by standardized residuals) improved the global correct classification rate, but only slightly improved assignment to the “biopsy necessary” group. Routine and complementary laboratory findings were found to be the most discriminating; answers to questionnaire and ultrasound examination were less important. Selection of the most diagnostic features based on “clean” data without outliers enabled us to find interesting medical associations, which were previously masked by extremely asymptomatic values outlying from the main body of the “biopsy necessary” group.

Key-Words

Discriminant Analysis - Variable Selection - Outliers - Prediction - Liver Diseases

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