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

Abstract

Background

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.

Methods

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.

Results

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).

Conclusions

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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2018 

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