Belmont, MA—Which depression patients are most likely to be helped by antidepressant medications? A new statistical algorithm helps determine that.
A report in Psychological Medicine discusses the development of the tool to identify before treatment those patients who might best respond to antidepressants.
McLean Hospital–led researchers explain how their paper, entitled “Personalized Prediction of Antidepressant v. Placebo Response: Evidence from the EMBARC Study,” was developed from data in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) effort, a large and recently completed multisite clinical trial of antidepressant medications.
Prior to the start of treatment, the study team collected demographic and clinical characteristics of participants in EMBARC from four sites—Columbia University, Massachusetts General Hospital, the University of Michigan, and University of Texas.
Southwestern Medical Center—and also administered computer-based evaluations to the patients. During the study, participants with major depression disorder (MDD) were randomly assigned to either a common antidepressant medication or a placebo pill.
The new algorithm is based on those results and predicts that about one-third of individuals would derive a meaningful therapeutic benefit from antidepressant medications compared to placebo.
“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,” study authors conclude.
Coauthor Christian A. Webb, PhD, director of the Treatment and Etiology of Depression in Youth Laboratory at McLean Hospital, noted that the study “found relatively little difference in average symptom improvement between those individuals randomly assigned to the medication vs. placebo.” At the same time, he explained, "for the one-third of individuals predicted to be better suited to antidepressants, they had significantly better outcomes if they happened to be assigned to the medication rather than the placebo.”
The patients predicted to benefit more from antidepressants tended to have higher depression severity and negative emotionality, were older, more likely to be employed, and exhibited better cognitive control on a computerized task, the researchers determined.
“These results bring us closer to identifying groups of patients very likely to benefit preferentially from an SSRI and could realize the goal of personalizing antidepressant treatment selection,” added Madhukar Trivedi, MD, of UT Southwestern Medical Center, who was the coordinating principal investigator for the EMBARC study.
“Our mission is to use these data-driven algorithms to provide clinicians and patients with useful information about which treatment is expected to yield the best outcome for this specific individual,” Webb said. “Rather than using a one-size-fits-all approach, we’d like to optimize our treatment recommendations for individual patients.”
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A report in Psychological Medicine discusses the development of the tool to identify before treatment those patients who might best respond to antidepressants.
McLean Hospital–led researchers explain how their paper, entitled “Personalized Prediction of Antidepressant v. Placebo Response: Evidence from the EMBARC Study,” was developed from data in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) effort, a large and recently completed multisite clinical trial of antidepressant medications.
Prior to the start of treatment, the study team collected demographic and clinical characteristics of participants in EMBARC from four sites—Columbia University, Massachusetts General Hospital, the University of Michigan, and University of Texas.
Southwestern Medical Center—and also administered computer-based evaluations to the patients. During the study, participants with major depression disorder (MDD) were randomly assigned to either a common antidepressant medication or a placebo pill.
The new algorithm is based on those results and predicts that about one-third of individuals would derive a meaningful therapeutic benefit from antidepressant medications compared to placebo.
“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,” study authors conclude.
Coauthor Christian A. Webb, PhD, director of the Treatment and Etiology of Depression in Youth Laboratory at McLean Hospital, noted that the study “found relatively little difference in average symptom improvement between those individuals randomly assigned to the medication vs. placebo.” At the same time, he explained, "for the one-third of individuals predicted to be better suited to antidepressants, they had significantly better outcomes if they happened to be assigned to the medication rather than the placebo.”
The patients predicted to benefit more from antidepressants tended to have higher depression severity and negative emotionality, were older, more likely to be employed, and exhibited better cognitive control on a computerized task, the researchers determined.
“These results bring us closer to identifying groups of patients very likely to benefit preferentially from an SSRI and could realize the goal of personalizing antidepressant treatment selection,” added Madhukar Trivedi, MD, of UT Southwestern Medical Center, who was the coordinating principal investigator for the EMBARC study.
“Our mission is to use these data-driven algorithms to provide clinicians and patients with useful information about which treatment is expected to yield the best outcome for this specific individual,” Webb said. “Rather than using a one-size-fits-all approach, we’d like to optimize our treatment recommendations for individual patients.”
« Click here to return to Weekly News Update.
