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Published on: January 7, 2019
Characteristics of U.S. Veteran Patients with Major Depressive Disorder who require "next-step" treatments: A VAST-D
Sidney Zisook1, Ilanit Tal1, Kimberly Weingart1
1VA San Diego Healthcare System and University of California, San Diego, USA.
Objective:
Finding effective and lasting treatments for patients with Major Depressive Disorder (MDD) that fail to respond optimally to initial standard treatment is a critical public health imperative. Understanding the nature and characteristics of patients prior to initiating "next-step" treatment is an important component of identifying which specific treatments are best suited for individual patients. We describe clinical features and demographic characteristics of a sample of Veterans who enrolled in a "next-step" clinical trial after failing to achieve an optimal outcome from at least one well-delivered antidepressant trial.
Methods:
1522 Veteran outpatients with nonpsychotic MDD completed assessments prior to being randomized to study treatment. Data is summarized and presented in terms of demographic, social, historical and clinical features and compared to a similar, non-Veteran sample.
Results:
Participants were largely male and white, with about half unmarried and half unemployed. They were moderately severely depressed, with about one-third reporting recent suicidal ideation. More than half had chronic and/or recurrent depression. General medical and psychiatric comorbidities were highly prevalent, particularly PTSD. Many had histories of childhood adversity and bereavement. Participants were impaired in multiple domains of their lives and had negative self-worth.
Limitations:
These results may not be generalizable to females, and some characteristics may be specific to Veterans of US military service. There was insufficient data on age of clinical onset and depression subtypes, and three novel measures were not psychometrically validated.
Conclusions:
Characterizing VAST-D participants provides important information to help clinicians understand features that may optimize "next-step" MDD treatments.
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