General Predictors and Moderators of Depression Remission: A VAST-D Report.

OBJECTIVE: Almost two-thirds of patients with major depressive disorder do not achieve remission with initial treatments. Thus, identifying and providing effective, feasible, and safe "next-step" treatments are clinical imperatives. This study explores patient baseline features that might help clinicians select between commonly used next-step treatments.

METHODS: The authors used data from the U.S. Department of Veterans Affairs (VA) Augmentation and Switching Treatments for Improving Depression Outcomes (VAST-D) study, a multisite, randomized, single-blind trial of 1,522 Veterans Health Administration patients who did not have an adequate response to at least one course of antidepressant treatment meeting minimal standards for dosage and duration. For 12 weeks, participants received one of three possible next-step treatments: switch to another antidepressant-sustained-release bupropion; combination with another antidepressant-sustained-release bupropion; or augmentation with an antipsychotic-aripiprazole. Life table regression models were used to identify baseline characteristics associated with remission overall (general predictors) and their interaction with remission among the three treatment groups (moderators).

RESULTS: Remission was more likely for individuals who were employed, less severely and chronically depressed, less anxious, not experiencing complicated grief symptoms, did not experience childhood adversity, and had better quality of life and positive mental health. Two features suggested specific next-step treatment selections: age ≥65 years (for whom augmentation with aripiprazole was more effective than switch to bupropion) and severe mixed hypomanic symptoms (for which augmentation with aripiprazole and combination with bupropion were more effective than switch to bupropion).

CONCLUSIONS: If replicated, these preliminary findings could help clinicians determine which patients with depression requiring next-step treatment will benefit most from a specific augmentation, combination, or switching strategy.