Algorithm Predicts Which Patients Will Benefit from Antidepressants

The statistical algorithm incorporates data including age, depression severity, neuroticism, and cognitive control to decide which patients with depression are most likely to benefit from the antidepressant sertraline.

Christian A. Webb, PhD

Christian A. Webb, PhD

A study comparing the antidepressant sertraline with placebo in adult patients with depression found that there was no overall difference in outcomes between the 2 study groups. However, for patients predicted to benefit from treatment, those who were randomized to the sertraline arm of the study had better Hamilton Rating Scale for Depression (HRSD) scores (10.7) than those on placebo (14.7) (d = 0.58).

"We found relatively little difference in average symptom improvement between those individuals randomly assigned to the medication vs. placebo," said study coauthor Christian A. Webb, PhD, director of the Treatment and Etiology of Depression in Youth Laboratory at McLean Hospital. 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."

Webb and his colleagues used data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a large multi-center clinical study of antidepressant medications. Demographic and clinical information for participants in the EMBARC study were collected before treatment. Participants were also given computer-based tasks to complete to identify cognitive control.

Using the algorithm, the team found that 31% of patients were predicted to benefit from antidepressant treatment. These patients tended to be older, have higher depression severity and negative emotionality, were more likely to be employed, and showed better cognitive control on the computerized task.

"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 UT Southwestern Medical Center's Madhukar Trivedi, MD, coordinating principal investigator for the EMBARC study.

Using the results, Webb’s research team is working to adapt the algorithm for further use. They hope to collaborate with the University of Pennsylvania on a study to test the algorithm by comparing 2 or more treatment options, such as different types of antidepressant medications or comparing an antidepressant with 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.

The study, “Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study,” was published in Psychological Medicine.

Related Videos
Victor Kim, MD: Addressing Comorbidities and Advancing COPD Care
Cedric Rutland, MD: Exploring Immunology's Role in Molecule Development
Panagis Galiatsatos, MD: Closing the Gap in Lung Cancer Screening
Panagis Galiatsatos, MD
Cedric Rutland, MD: Mechanisms Behind Immunology, Cellular Communication
Experts discuss depression.
Experts discuss depression.
Experts on depression.
Related Content
© 2023 MJH Life Sciences

All rights reserved.