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

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.

Keywords: Antidepressant; depression; endophenotype; machine learning; placebo; precision medicine; prediction.

Conflict of interest statement

Conflict of Interest

In the last three years, the authors report the following financial disclosures, for activities unrelated to the current research:

Dr. Trivedi: reports the following lifetime disclosures: research support from the Agency for Healthcare Research and Quality, Cyberonics Inc., National Alliance for Research in Schizophrenia and Depression, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Diabetes and Digestive and Kidney Diseases, Johnson & Johnson, and consulting and speaker fees from Abbott Laboratories Inc., Akzo (Organon Pharmaceuticals Inc.), Allergan Sales LLC, Alkermes, AstraZeneca, Axon Advisors, Brintellix, Bristol-Myers Squibb Company, Cephalon Inc., Cerecor, Eli Lilly & Company, Evotec, Fabre Kramer Pharmaceuticals Inc., Forest Pharmaceuticals, GlaxoSmithKline, Health Research Associates, Johnson & Johnson, Lundbeck, MedAvante Medscape, Medtronic, Merck, Mitsubishi Tanabe Pharma Development America Inc., MSI Methylation Sciences Inc., Nestle Health Science-PamLab Inc., Naurex, Neuronetics, One Carbon Therapeutics Ltd., Otsuka Pharmaceuticals, Pamlab, Parke-Davis Pharmaceuticals Inc., Pfizer Inc., PgxHealth, Phoenix Marketing Solutions, Rexahn Pharmaceuticals, Ridge Diagnostics, Roche Products Ltd., Sepracor, SHIRE Development, Sierra, SK Life and Science, Sunovion, Takeda, Tal Medical/Puretech Venture, Targacept, Transcept, VantagePoint, Vivus, and Wyeth-Ayerst Laboratories.

Dr. Dillon: funding from NIMH, consulting fees from Pfizer Inc.

Dr. Fava: Dr. Fava reports the following lifetime disclosures: http://mghcme.org/faculty/faculty-detail/maurizio_fava

Dr. Weissman: funding from NIMH, the National Alliance for Research on Schizophrenia and Depression (NARSAD), the Sackler Foundation, and the Templeton Foundation; royalties from the Oxford University Press, Perseus Press, the American Psychiatric Association Press, and MultiHealth Systems.

Dr. Oquendo: funding from NIMH; royalties for the commercial use of the Columbia-Suicide Severity Rating Scale. Her family owns stock in Bristol Myers Squibb.

Dr. McInnis: funding from NIMH; consulting fees from Janssen and Otsuka Pharmaceuticals.

Dr. McGrath has received research grant support from Naurex Pharmaceuticals (now Allergan), Sunovion, and the State of New York.

Dr. Pizzagalli: funding from NIMH and the Dana Foundation; consulting fees from Akili Interactive Labs, BlackThorn Therapeutics, Boehringer Ingelheim, Pfizer Inc. and Posit Science.

Dr. Trombello currently owns stock in Merck and Gilead Sciences and within the past 36 months previously owned stock in Johnson & Johnson.

Drs. Adams, Cohen, Bruder, Cooper, Deldin, DeRubeis, Fournier, Huys, Jha, Kurian, McGrath, Parsey, Webb, Ms. Goer: report no financial conflicts.

Figures

Figure 1.

Frequency histogram displaying distribution of Personalized Advantage Index (PAI) scores, computed as the predicted difference in week 8 HRSD scores for SSRI minus placebo. Accordingly, a PAI score less than 0 signifies that SSRI was indicated, whereas a PAI score greater than 0 indicates that placebo was expected to yield a better outcome. The kernel density estimate illustrates the expected distribution of PAI scores in the population.

Figure 2.

Plots of baseline predictor by treatment group interactions from the final model.

Figure 3.

Comparison of mean week 8 HRSD for patients randomized to SSRI or placebo (left panel) (n=216). Comparison of mean week 8 HRSD scores for patients randomly assigned to their PAI-indicated treatment vs. those assigned to their PAI-contraindicated treatment for the full sample (n = 216) vs. including only patients for whom the algorithm predicted a clinically significant advantage in one treatment condition (PAI ≥ |3|); n = 105) (right panel). Error bars represent standard error.