Results of the European Group for the Study of Resistant Depression (GSRD) — basis for further research and clinical practice

Bartova, Lucie; Dold, Markus; Kautzky, Alexander; Fabbri, Chiara; Spies, Marie; Serretti, Alessandro; Souery, Daniel; Mendlewicz, Julien; Zohar, Joseph; Montgomery, Stuart; Schosser, Alexandra; Kasper, Siegfried

doi:10.1080/15622975.2019.1635270

Cited by 95 publications

(108 citation statements)

References 135 publications

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“…51,67 In line with these findings, recent results from the European GSRD indicated that great depressive symptom severity, as well as early age of onset (≤18 years), are predictors of resistance to AD treatment. 68 In conclusion, the greater the pretreatment symptom severity of patients in an MDE, the greater the expected treatment response to the whole class of antidepressants, even if, in patients with severe depressive symptoms, full remission seemed to be more difficult to achieve than response and poorer treatment outcomes are more likely. Patients with very low depressive symptom severity may not require antidepressants, while non-pharmacological interventions, such as cognitive behavioral therapy (CBT), may be sufficient for symptom remission.…”

Section: Severity Of Depressive Symptomsmentioning

confidence: 93%

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Perna

Alciati²,

Daccò

et al. 2020

Psychiatry Investig

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Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

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Section: Severity Of Depressive Symptomsmentioning

confidence: 93%

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Perna

Alciati²,

Daccò

et al. 2020

Psychiatry Investig

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“…39 An important effort to define algorithms for treatment-resistant depression is currently being made by the European Group for the Study of Resistant Depression (GSRD). 40 In a recent work, using random forest and k-means clustering, the authors identified a signature of three genetic variants related to treatment outcome [rs6265 in the gene encoding for the brainderived neurotrophic factor (BDNF), rs7430 in the Protein Phosphatase 3 Catalytic Subunit Gamma (PPP3CC) gene, and rs6313 in the gene encoding for the serotonin transporter receptor 2A (HTR2A)] that, combined with the absence of melancholy, were associated with a decline in the HAMD score <17 (62% of the patients with this combination compared to 34% in the whole study population). 41 A more comprehensive list of genetic variants was investigated by Maciukiewicz et al (2018).…”

Section: Biological Predictive Models In Precision Psychiatrymentioning

confidence: 99%

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Manchia¹,

Pisanu²,

Squassina³

et al. 2020

PGPM

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Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

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“…More specifically, the short allele was shown to reduce transcription of the serotonin transporter gene (Lesch et al 1996), the target for SSRIs. Additionally, several single nucleotide polymorphisms in genes implicated in the serotonergic system have been identified in TRD (Bartova et al 2019), for example: CREB1, BDNF and 5HTR2A (Schosser et al 2012). There are few studies reporting the genetic complexity of TRD and its impact on overall antidepressant efficacy (Fabbri et al 2019); further studies are needed.…”

Section: Understanding the Different Facets Of Treatmentresistant Depmentioning

confidence: 99%

“…These include the severity and duration of the current depressive episode, early age of onset (Bennabi et al 2019), melancholic features (Jaffe et al 2019) or suicidal risk (Souery et al 2007), the latter of which is thought to be associated with Val66Met and rs10501087 polymorphisms (Schosser et al 2017). The patient's disease and treatment history may also impact the risk of TRD, for example, childhood adversity (Tunnard et al 2014), number of prior depressive episodes (Bartova et al 2019), traumatic or stressful life events, or previous non-remission or partial remission (Murphy et al 2017). These clinical factors are thought to contribute to TRD, adding to its complexity.…”

Section: Understanding the Different Facets Of Treatmentresistant Depmentioning

confidence: 99%

Practical recommendations for the management of treatment-resistant depression with esketamine nasal spray therapy: Basic science, evidence-based knowledge and expert guidance

Kasper

Cubała

Fagiolini

et al. 2020

The World Journal of Biological Psychiatry

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Objectives: Despite the available therapies for treatment-resistant depression (TRD), there are a limited number that are evidence-based and effective in this hard-to-treat population. Esketamine nasal spray, an intranasal N-methyl-D-aspartate (NMDA) glutamate receptor antagonist, is a novel, fast-acting option in this patient population. This manuscript provides expert guidance on the practicalities of using esketamine nasal spray. Methods: A group of six European experts in major depressive disorder (MDD) and TRD, with clinical experience of treating patients with esketamine nasal spray, first generated practical recommendations, before editing and voting on these to develop consensus statements during an online meeting. Results: The final consensus statements encompass not only pre-treatment considerations for patients with TRD, but also specific guidelines for clinicians to consider during and post-administration of esketamine nasal spray. Conclusions: Esketamine nasal spray is a novel, fast-acting agent that provides an additional treatment option for patients with TRD who have previously failed several therapies. The guidance here is based on the authors' experience and the available literature; however, further realworld use of esketamine nasal spray will add to existing knowledge. The recommendations offer practical guidance to clinicians who are unfamiliar with esketamine nasal spray.

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Results of the European Group for the Study of Resistant Depression (GSRD) — basis for further research and clinical practice

Cited by 95 publications

References 135 publications

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Practical recommendations for the management of treatment-resistant depression with esketamine nasal spray therapy: Basic science, evidence-based knowledge and expert guidance

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