Literature DB >> 31437703

Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report.

N van der Vinne1, M A Vollebregt2, N N Boutros3, K Fallahpour4, M J A M van Putten5, M Arns6.   

Abstract

BACKGROUND: MDD patients with abnormal EEG patterns seem more likely to be non-responsive to the antidepressants escitalopram and venlafaxine, but not sertraline, than patients without EEG abnormalities. This finding suggests that patients with both MDD and abnormal EEGs may differentially respond to antidepressant treatment. In the current study, we investigated whether depressed patients with an abnormal EEG show a normalization of the EEG related to antidepressant treatment and response and whether such effect is drug specific, and whether having had early life stress (ELS) increases the chance of abnormal activity.
METHODS: Baseline and week 8 EEGs and depression symptoms were extracted from a large multicenter study (iSPOT-D, n = 1008) where depressed patients were randomized to escitalopram, sertraline, or venlafaxine-XR treatment. We calculated Odds Ratios of EEG normalization and depression response in patients with an abnormal EEG at baseline, comparing sertraline versus other antidepressants.
RESULTS: Fifty seven patients with abnormal EEGs were included. EEGs did not normalize significantly more with sertraline compared to other antidepressants (OR = 1.9, p = .280). However, patients with a normalized EEG taking sertraline were 5.2 times more likely to respond than subjects taking other antidepressants (p = .019). ELS was not significantly related to abnormal activity. LIMITATIONS: Neurophysiological recordings were limited in time (two times 2-minute EEGs) and statistical power (n = 57 abnormal EEGs).
CONCLUSIONS: Response rates in patients with normalized EEG taking sertraline were significantly larger than in subjects treated with escitalopram/venlafaxine. This adds to personalized medicine and suggests a possible drug repurposing for sertraline.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Antidepressant; Depression; EEG; Paroxysmal; Personalized medicine; Prognosis

Mesh:

Substances:

Year:  2019        PMID: 31437703     DOI: 10.1016/j.jad.2019.08.016

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  3 in total

Review 1.  Isolated epileptiform activity in children and adolescents: prevalence, relevance, and implications for treatment.

Authors:  Ronald J Swatzyna; Martijn Arns; Jay D Tarnow; Robert P Turner; Emma Barr; Erin K MacInerney; Anne M Hoffman; Nash N Boutros
Journal:  Eur Child Adolesc Psychiatry       Date:  2020-07-14       Impact factor: 4.785

2.  Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

Authors:  Pranav Rajpurkar; Jingbo Yang; Nathan Dass; Vinjai Vale; Arielle S Keller; Jeremy Irvin; Zachary Taylor; Sanjay Basu; Andrew Ng; Leanne M Williams
Journal:  JAMA Netw Open       Date:  2020-06-01

3.  Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review.

Authors:  Rebecca Strafella; Robert Chen; Tarek K Rajji; Daniel M Blumberger; Daphne Voineskos
Journal:  Front Hum Neurosci       Date:  2022-08-04       Impact factor: 3.473

  3 in total

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