Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study.
- Webb CA Harvard Medical School - McLean Hospital,Boston, MA,USA.
- Trivedi MH University of Texas, Southwestern Medical Center,Dallas, TX,USA.
- Cohen ZD University of Pennsylvania,Philadelphia, PA,USA.
- Dillon DG Harvard Medical School - McLean Hospital,Boston, MA,USA.
- Fournier JC University of Pittsburgh School of Medicine,Pittsburgh, PA,USA.
- Goer F Harvard Medical School - McLean Hospital,Boston, MA,USA.
- Fava M Harvard Medical School, Massachusetts General Hospital,Boston, MA,USA.
- McGrath PJ New York State Psychiatric Institute & Department of Psychiatry,College of Physicians and Surgeons of Columbia University,New York, NY,USA.
- Weissman M New York State Psychiatric Institute & Department of Psychiatry,College of Physicians and Surgeons of Columbia University,New York, NY,USA.
- Parsey R Stony Brook University,Stony Brook, NY,USA.
- Adams P New York State Psychiatric Institute & Department of Psychiatry,College of Physicians and Surgeons of Columbia University,New York, NY,USA.
- Trombello JM University of Texas, Southwestern Medical Center,Dallas, TX,USA.
- Cooper C University of Texas, Southwestern Medical Center,Dallas, TX,USA.
- Deldin P University of Michigan,Ann Arbor, MI,USA.
- Oquendo MA University of Pennsylvania,Philadelphia, PA,USA.
- McInnis MG University of Michigan,Ann Arbor, MI,USA.
- Huys Q University of Zurich,Zurich,Switzerland.
- Bruder G New York State Psychiatric Institute & Department of Psychiatry,College of Physicians and Surgeons of Columbia University,New York, NY,USA.
- Kurian BT University of Texas, Southwestern Medical Center,Dallas, TX,USA.
- Jha M University of Texas, Southwestern Medical Center,Dallas, TX,USA.
- DeRubeis RJ University of Pennsylvania,Philadelphia, PA,USA.
- Pizzagalli DA Harvard Medical School - McLean Hospital,Boston, MA,USA.
- 2018-07-03
Published in:
- Psychological medicine. - 2019
Antidepressant
depression
endophenotype
machine learning
placebo
precision medicine
prediction
Adolescent
Adult
Aged
Antidepressive Agents
Depressive Disorder, Major
Double-Blind Method
Endophenotypes
Female
Humans
Machine Learning
Male
Middle Aged
Patient Outcome Assessment
Precision Medicine
Prospective Studies
Sertraline
Treatment Outcome
Young Adult
English
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.
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.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1017/S0033291718001708
- PMID 29962359
- Persistent URL
- https://sonar.ch/global/documents/30194
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