Combination Therapies Superior to Monotherapy Antidepressants in Treating Acute Depression

February 16, 2022
Kenny Walter

Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.

The number of dropouts and dropouts because of adverse events did not differ between treatments.

Investigators found both positives in efficacy and negatives in dropouts in comparing combination and monotherapies for the treatment of acute depression.

A team, led by Jonathan Henssler, MD, Department of Psychiatry and Psychotherapy, University of Cologne Medical School, assessed the efficacy and tolerability of combination therapy for treating acute depression.

In recent years there has been conflicting results over whether combining antidepressants is effective in treating acute depression.

Monotherapy Vs. Combination Therapy

In the systematic review and meta-analysis, the investigators investigated combinations using presynaptic a2-autoreceptor antagonists or bupropion separately, identifying randomized clinical trials that compared combinations of antidepressants with antidepressant monotherapy in adult patients with acute depression.

The investigators sought primary outcomes of efficacy, measured as standardized mean difference (SMD). They also sought various secondary outcomes including, response, remission, change from baseline in rating scale scores, number of dropouts, and the number of dropouts due to adverse events.

The team identified 39 randomized clinical trials involving 6751 patients.

Mixed Results

The results show combination therapy was statistically significantly linked to superior treatment outcomes compared with monotherapy (SMD = 0.31; 95% CI, 0.19-0.44) and combining a reuptake inhibitor with an antagonist of presynaptic a2-autoreceptors was superior to all other combinations studied (SMD = 0.37; 95% CI, 0.19-0.55).

However, bupropion combinations were not superior to monotherapy (SMD = 0.10; 95% CI, −0.07 to 0.27).

The number of dropouts and dropouts because of adverse events did not differ between treatments.

In addition, the studies included were heterogeneous, with indications of publication bias (Egger test result was positive; P = .007; df = 36). However, these results remained robust across prespecified secondary outcomes and sensitivity and subgroup analyses, including analyses restricted to studies with a low risk of bias.

“In this meta-analysis of RCTs comparing combinations of antidepressants with antidepressant monotherapy, combining antidepressants was associated with superior treatment outcomes but not with more patients dropping out of treatment,”the authors wrote. “Combinations using an antagonist of presynaptic α2-autoreceptors may be preferable and may be applied as a first-line treatment in severe cases of depression and for patients considered nonresponders.”

Machine Learning for Depression

Investigators have long sought ways to improve symptoms for patients with depression.

In 2020, investigators found machine learning may be used to identify independent associations of symptoms and electroencephalographic (EEG) features to predict antidepressant-associated improvements in specific symptoms of depression.

Pranav Rajpurkar, MS, and a team of investigators identified the extent to which a machine-learning approach could predict acute improvement for individual depressive symptoms with antidepressants based on pre-treatment symptom scores and EEG measures.

The team found their machine-learning algorithm called ElecTreeScore could reliably distinguish patients who responded to treatment from those who did not, based on various symptoms using pre-treatment symptom scores.

The importance of any EEG feature was higher than 5% for the prediction of 7 symptoms. The most important EEG feature was the absolute delta band power at occipital electrode sites for loss of insight.

The use of the EEG and baseline symptom features was linked with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, .012; 95% CI, .001-.02), energy loss (C index increase, .035; 95% CI, .011=.059), appetite change (C index increase, .017; 95% CI, .003-.03), and psychomotor agitation (C index increase, .02; 95% CI, .008-.032).

The study, “Combining Antidepressants vs Antidepressant Monotherapy for Treatment of Patients With Acute Depression,” was published online in JAMA Psychiatry.


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