Factors Affecting Antidepressant Response Trajectories: A Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes Trial Report

Author:

Hicks Paul B.12ORCID,Sevilimedu Varadan345,Johnson Gary R.5,Tal Ilanit R.6,Chen Peijun78,Davis Lori L.910,Vertrees Julia E.11,Zisook Sidney612,Mohamed Somaia1314

Affiliation:

1. Department of Psychiatry Baylor Scott & White Health Temple Texas

2. Texas A&M College of Medicine Temple Texas

3. Biostatistics Service Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York New York

4. Yale University School of Public Health New Haven Connecticut

5. Cooperative Studies Program Coordinating Center VA Connecticut Healthcare System West Haven Connecticut

6. VA San Diego Healthcare System San Diego California

7. Department of Psychiatry VISN10 Geriatric Research, Education and Clinical Center VA Northeast Ohio Healthcare System Cleveland Ohio

8. Case Western Reserve University Cleveland Ohio

9. Tuscaloosa VA Medical Center Tuscaloosa Alabama

10. University of Alabama School of Medicine Birmingham Alabama

11. Cooperative Studies Program Clinical Research Pharmacy Coordinating Center Albuquerque New Mexico

12. University of California San Diego California

13. Veterans Affairs (VA) New England Mental Illness Research, Education and Clinical Center VA Connecticut Healthcare System West Haven Connecticut

14. Yale University School of Medicine New Haven Connecticut

Abstract

BackgroundIn this secondary analysis of the VA Augmentation and Switching Treatments for Improving Depression Outcomes (VAST‐D) study we used antidepressant response trajectories to assess the association of treatment and multiple clinical/demographic factors with the probability of response.MethodsUsing data from VAST‐D, a multi‐site, randomized, single‐blind trial with parallel‐assignment to one of three treatment interventions in 1522 Veterans whose major depressive disorder was unresponsive to at least one antidepressant trial, we evaluated response patterns using group‐based trajectory modeling (GBTM). A weighted multinomial logistic regression analysis with backward elimination and additional exploratory analyses were performed to evaluate the association of multiple clinical/demographic factors with the probability of inclusion into specific trajectories. Additional exploratory analyses were used to identify factors associated with trajectory group membership that could have been missed in the primary analysis.ResultsGBTM showed the best fit for depression symptom change was comprised of six trajectories, with some trajectories demonstrating minimal improvement and others showing a high probability of remission. High baseline depression and anxiety severity scores decreased, and early improvement increased, the likelihood of inclusion into the most responsive trajectory in both the GBTM and exploratory analyses.ConclusionWhile multiple factors influence responsiveness, the probability of inclusion into a specific depression symptom trajectory is most strongly influenced by three factors: baseline depression, baseline anxiety, and the presence of early improvement.

Publisher

American Psychiatric Association Publishing

Subject

General Engineering

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