Multivariate strategies for screening evaluation of harmful drinking

Abstract

Background: A chemometric class modeling strategy (unequal dispersed classes [UNEQ]) is applied for the first time to evaluate harmful alcohol drinking within large population screening programs, in comparison with traditional strategies of data interpretation. Five inexpensive indirect biomarkers (aspartate aminotransferase, alanine aminotransferase, γ-glutamyltransferase, mean corpuscular volume and carbohydrate-deficient transferrin) were determined in blood samples from 423 patients, classified as low-risk or harmful drinkers, according to their ethanol consumption. Results: The multivariate UNEQ approach remarkably improves the diagnostic performances of indirect biomarkers in harmful drinking evaluation, leading to reliable decision rules, with few doubtful classifications to be reviewed through complex confirmation procedures. Conclusion: This UNEQ model represents an innovative general approach for clinical evaluation that efficiently extracts the information content present in each biomarker to provide a new synthetic multivariate parameter, to be directly used in diagnostic protocols.