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Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

Published online by Cambridge University Press:  02 July 2018

Christian A. Webb*
Harvard Medical School – McLean Hospital, Boston, MA, USA
Madhukar H. Trivedi
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Zachary D. Cohen
University of Pennsylvania, Philadelphia, PA, USA
Daniel G. Dillon
Harvard Medical School – McLean Hospital, Boston, MA, USA
Jay C. Fournier
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Franziska Goer
Harvard Medical School – McLean Hospital, Boston, MA, USA
Maurizio Fava
Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
Patrick J. McGrath
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Myrna Weissman
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Ramin Parsey
Stony Brook University, Stony Brook, NY, USA
Phil Adams
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Joseph M. Trombello
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Crystal Cooper
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Patricia Deldin
University of Michigan, Ann Arbor, MI, USA
Maria A. Oquendo
University of Pennsylvania, Philadelphia, PA, USA
Melvin G. McInnis
University of Michigan, Ann Arbor, MI, USA
Quentin Huys
University of Zurich, Zurich, Switzerland
Gerard Bruder
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Benji T. Kurian
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Manish Jha
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Robert J. DeRubeis
University of Pennsylvania, Philadelphia, PA, USA
Diego A. Pizzagalli
Harvard Medical School – McLean Hospital, Boston, MA, USA
Author for correspondence: Christian A. Webb, E-mail:



Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.


Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.


Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).


A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Original Articles
Copyright © Cambridge University Press 2018 

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