Aims: This study describes the derivation and validation of the early detection of alcohol consumption (EDAC) test, which uses linear discriminant function (LDF) analysis for the identification of alcohol misuse. This form of LDF aims to predict a categorical dependent variable (alcohol misuse) on the basis of several independent, predictor variables (routine laboratory tests).
Methods: EDAC was developed to classify individuals as heavy or light drinkers using a database of 1599 subjects recruited from 25 sites in the USA. The predictor variables for the LDF were 36 routine chemistry and haematology analytes.
Results: The EDAC model produced 80.7% sensitivity and 84.4% specificity, with an overall correct classification rate of 82.5%. Using a stepwise method, 20 of the 36 routine tests used in the LDF were selected as the optimal predictor variables. The top three variables with the highest contribution in the stepwise EDAC model were: bilirubin ratio (direct to total), aspartate aminotransferase and albumin.
Conclusions: This study shows that LDF analysis in conjunction with new, user-friendly computer packages is a practical and cost-effective laboratory tool for detecting excessive drinking using blood constituents ordered routinely in a variety of clinical settings. Diagnostic performance can be adjusted to achieve higher specificity rates.