Modeling predictors, moderators and mediators of treatment outcome and resistance in depression

摘要

The article by Perlis (1) addresses a question of major importance for treatment matching of patients with major depressive disorder (MDD): Can we identify patients seeking treatment for depression that are unlikely to respond to currently available treatments? The ability to prospectively identify patient subgroups based on their predicted outcomes especially early in the course of treatment is likely to reduce the inordinate delays in identifying the correct treatment for patients, reduce morbidity and mortality, shorten suffering, reduce rates of treatment dropouts, and likely reduce the cost burden for the patient and society (2,3). The author also suggests that early identification of treatment resistant depression (TRD) could direct patients to more complicated treatments more rapidly. The author used data available from the STAR*D sample and developed a model that is designed to address these issues.