Results of the European Group for the Study of Resistant Depression (GSRD) — basis for further research and clinical practice. (3rd July 2019)

• Record Type: Journal Article
• Authors: Bartova, Lucie; Dold, Markus; Kautzky, Alexander; Fabbri, Chiara; Spies, Marie; Serretti, Alessandro; Souery, Daniel; Mendlewicz, Julien; Zohar, Joseph; Montgomery, Stuart; Schosser, Alexandra; Kasper, Siegfried
• Is Part Of: World journal of biological psychiatry. Volume 20:Number 6(2019)
• Page Start: 427
• Page End: 448
• Publication Date: 2019-07-03
• Subjects: Depression -- psychopharmacotherapy -- response -- treatment resistance -- predictor; Biological psychiatry -- Periodicals; Biological Psychiatry -- Periodicals; 616.89
• DOI: 10.1080/15622975.2019.1635270
• Languages: English
• ISSNs: 1562-2975

Abstract

Abstract: Objectives: The overview outlines two decades of research from the European Group for the Study of Resistant Depression (GSRD) that fundamentally impacted evidence-based algorithms for diagnostics and psychopharmacotherapy of treatment-resistant depression (TRD). Methods: The GSRD staging model characterising response, non-response and resistance to antidepressant (AD) treatment was applied to 2762 patients in eight European countries. Results: In case of non-response, dose escalation and switching between different AD classes did not show superiority over continuation of original AD treatment. Predictors for TRD were symptom severity, duration of the current major depressive episode (MDE), suicidality, psychotic and melancholic features, comorbid anxiety and personality disorders, add-on treatment, non-response to the first AD, adverse effects, high occupational level, recurrent disease course, previous hospitalisations, positive family history of MDD, early age of onset and novel associations of single nucleoid polymorphisms (SNPs) within the PPP3CC, ST8SIA2, CHL1, GAP43 and ITGB3 genes and gene pathways associated with neuroplasticity, intracellular signalling and chromatin silencing. A prediction model reaching accuracy of above 0.7 highlighted symptom severity, suicidality, comorbid anxiety and lifetime MDEs as the most informative predictors for TRD. Applying machine-learning algorithms, a signature of three SNPs of the BDNF, PPP3CC and HTR2A genes and lacking melancholia predicted treatment response. Conclusions: The GSRD findings offer a unique and balanced perspective on TRD representing foundation for further research elaborating on specific clinical and genetic hypotheses and treatment strategies within appropriate study-designs, especially interaction-based models and randomized controlled trials.