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EEG May Predict Antidepressant Response, Tailoring Treatment for Patients
Electroencephalography (EEG) may help match patients with the optimal depression treatment for their unique needs by predicting response to specific antidepressant medications, according to recent research published in JAMA Network Open.
The multivariate predictive model developed in the study “showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment,” Benjamin Schwartzmann, MSc, Simon Fraser University, Surrey, British Colombia, Canada, and co-authors wrote in the study.
With a model that used EEG data collected between 2011 and 2017 from 2 independent cohorts of participants aged 18 to 65 years with major depressive disorder (MDD), this study aimed to establish whether a patient with depression will benefit from specific medication treatments.
The study’s first cohort was from the Canadian Biomarker Integration Network in Depression (CAN-BIND) group, and the other was from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. CAN-BIND participants received an 8-week escitalopram treatment (10-20 mg) regimen in an open-label trial. EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) or placebo treatment.
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At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity—either the Montgomery-Åsberg Depression Rating Scale (MADRS) or Hamilton Depression Rating Scale (HDRS-17). The model achieved 64.2% balanced accuracy using internal validation with CAN-BIND, and 63.7% accuracy using external validation with EMBARC.
“This finding is of clinical importance because it provides a substantial improvement over trial-and-error methods,” researchers concluded. “This observation is notably accentuated by the initial low response rates in the CAN-BIND and EMBARC samples. Accurately predicting treatment response in almost two-thirds of cases could lead to improved patient outcomes, reducing ineffective treatments and health care burden.”
“EEG-based models are not necessarily intended for stand-alone use in clinical practice, and future studies may enhance accuracy by incorporating other data sources,” researchers noted in the study limitations. “Future studies could use more diverse data sets including patients who are already taking an antidepressant or those for whom their first treatment failed to enhance the model’s generalizability and ensure its applicability to a wider range of patients.”
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