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Optimizing patient expectancy in the pharmacologic treatment of major depressive disorder

Published online by Cambridge University Press:  13 November 2018

Sigal Zilcha-Mano ,
Patrick J. Brown ,
Steven P. Roose ,
Kiley Cappetta  and
Bret R. Rutherford
Sigal Zilcha-Mano*
Affiliation:
Department of Psychology, University of Haifa, Mount Carmel, Haifa 31905, Israel
Patrick J. Brown
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Steven P. Roose
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Kiley Cappetta
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Bret R. Rutherford
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
*
Author for correspondence: Sigal Zilcha-Mano, E-mail: [email protected]
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Abstract

Background

Patient expectancy is an important source of placebo effects in antidepressant clinical trials, but all prior studies measured expectancy prior to the initiation of medication treatment. Little is known about how expectancy changes during the course of treatment and how such changes influence clinical outcome. Consequently, we undertook the first analysis to date of in-treatment expectancy during antidepressant treatment to identify its clinical and demographic correlates, typical trajectories, and associations with treatment outcome.

Methods

Data were combined from two randomized controlled trials of antidepressant medication for major depressive disorder in which baseline and in-treatment expectancy assessments were available. Machine learning methods were used to identify pre-treatment clinical and demographic predictors of expectancy. Multilevel models were implemented to test the effects of expectancy on subsequent treatment outcome, disentangling within- and between-patient effects.

Results

Random forest analyses demonstrated that whereas more severe depressive symptoms predicted lower pre-treatment expectancy, in-treatment expectancy was unrelated to symptom severity. At each measurement point, increased in-treatment patient expectancy significantly predicted decreased depressive symptoms at the following measurement (B = −0.45, t = −3.04, p = 0.003). The greater the gap between expected treatment outcomes and actual depressive severity, the greater the subsequent symptom reductions were (B = 0.49, t = 2.33, p = 0.02).

Conclusions

Greater in-treatment patient expectancy is associated with greater subsequent depressive symptom reduction. These findings suggest that clinicians may benefit from monitoring and optimizing patient expectancy during antidepressant treatment. Expectancy may represent another treatment parameter, similar to medication compliance and side effects, to be regularly monitored during antidepressant clinical management.

Keywords

expectancymajor depressive disorderplacebo effects
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 14 , October 2019 , pp. 2414 - 2420
Copyright
Copyright © Cambridge University Press 2018 

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