Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive-compulsive disorder

About 30% of obsessive-compulsive disorder (OCD) patients exhibit an inadequate response to pharmacotherapy. The detection of clinical variables associated with treatment response may result in achievement of remission in shorter period, preventing illness development and reducing socioeconomic costs.

In total, 330 subjects with OCD diagnosis underwent 12-week pharmacotherapy with fluvoxamine (150-300 mg). Treatment response was ≥25% reduction in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score. In total, 36 clinical attributes of 151 subjects who had completed their treatment course were analyzed. Data mining algorithms included missing value handling, feature selection, and new analytical method based on ensemble classification. The results were compared with those of other traditional classification algorithms such as decision tree, support vector machines, k-nearest neighbor, and random forest.

Sexual and contamination obsessions are high-ranked predictors of resistance to fluvoxamine pharmacotherapy as well as high Y-BOCS obsessive score. Our results showed that the proposed analysis strategy has good ability to distinguish responder and nonresponder patients according to their clinical features with 86% accuracy, 79% sensitivity, and 89% specificity.

This study proposed an analytical approach which is an accurate and a sensitive method for the analysis of high-dimensional medical data sets containing more number of missing values. The treatment of OCD could be improved by better understanding of the predictors of pharmacotherapy, which may lead to more effective treatment of patients with OCD.