New Algorithm Can Predict Response to Antidepressants
Researchers have developed a statistical algorithm that identifies patients who may best respond to antidepressants — before they begin treatment.
Investigators from McLean Hospital, an affiliate of Harvard Medical School, used data from a recently completed multi-site clinical trial called Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC).
The paper by Christian A. Webb, Ph.D., and Diego A. Pizzagalli, Ph.D., appears in the journal Psychological Medicine.
Webb said that demographic and clinical characteristics of individuals who took part in the EMBARC study were collected prior to the start of treatment by the study team across four sites (Columbia University, Massachusetts General Hospital, the University of Michigan, and UT Southwestern Medical Center). Participants were also administered computer-based tasks.
Using this information, Webb and his colleagues developed an algorithm predicting that approximately one-third of individuals would derive a meaningful therapeutic benefit from antidepressant medications relative to placebo. In the study, participants were randomly assigned to a common antidepressant medication or a placebo pill.
The results, Webb said, were like many previous clinical trials in that “we found relatively little difference in average symptom improvement between those individuals randomly assigned to the medication vs. placebo.”
However, 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 latter group of patients were characterized by higher depression severity and negative emotionality, were older, more likely to be employed, and exhibited better cognitive control on a computerized task.
“These results bring us closer to identifying groups of patients very likely to benefit preferentially from selective serotonin reuptake inhibitors (SSRIs) and could realize the goal of personalizing antidepressant treatment selection,” added UT Southwestern Medical Center’s Madhukar Trivedi, M.D., coordinating principal investigator for the EMBARC study.
SSRIs are a class of drugs that are typically used as antidepressants in the treatment of major depressive disorder and anxiety disorders.
Building on these findings, Webb said, his team is now looking to adapt the algorithm for use in “real-world” clinics. Specifically, he said, the researchers are looking to collaborate with the University of Pennsylvania on a study that would test the algorithm in psychiatric clinics treating individuals suffering from depression by comparing two or more viable treatments; for example, two different classes of antidepressants, or antidepressants vs. psychotherapy.
“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.
He explained that research like this may further the goal of creating “personalized medicine” in health care. “Rather than using a one-size-fits-all approach, we’d like to optimize our treatment recommendations for individual patients,” he said.
Source: McClean Hospital
Nauert PhD, R. (2018). New Algorithm Can Predict Response to Antidepressants. Psych Central. Retrieved on December 3, 2020, from https://psychcentral.com/news/2018/07/19/new-algorithm-can-predict-response-to-antidepressants/137117.html