Clinical prediction of antidepressant response in mood disorders: linear multivariate vs. neural network models

Predicting the outcome of antidepressant treatment by pre-treatment features would be of great usefulness for clinicians as up to 50% of major depressives may not have a satisfactory response in spite of adequate trials of antidepressant drugs. In the present article we compared a linear multivariate model of predictors with a few artificial neural network (ANN) models differing from one another by outcome definition and validation procedure. The sample consisted of a reanalysis of 116 inpatients with a major depressive episode included in a 6-week open-label trial with fluvoxamine. With the original outcome definition (responders/non-responders), ANN performed better than logistic regression (90% of correct classifications in the training sample vs. 77%). However only 62% of new patients were correctly predicted by ANN for their outcome class. Length of the index episode, psychotic features and suicidal behavior emerged as outcome predictors in both models, while demographic characteristics, personality disorders and concomitant somatic morbidity were pointed to only by ANN analysis. Increase of classes in the outcome field resulted in a more elevated error: 46.4% for three classes, 60.4% for four classes and 70.3% for five classes. Overall, our findings suggest that antidepressant outcome prediction based on clinical variables is poor. The ANN approach is as valid as traditional multivariate techniques for the analysis of psychopharmacology studies. The complex interactions modelled through ANN may eventually be applied at the clinical level for individualized therapy. However, the accuracy of prediction is still far from satisfactory from a clinical point of view.