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Background: Treatment-resistant depression (TRD) is an important clinical challenge and may present differently between age groups., Methods: A total of 893 depressed patients recruited within the framework of the European research consortium "Group for the Studies of Resistant Depression" were assessed by generalized linear models regarding age effects (both as numerical and factorial predictors) on treatment outcome, number of lifetime depressive episodes, hospitalization time, and duration of the current episode. Effects of age as numerical predictor on the severity of common depressive symptoms, measured with Montgomery-Åsberg Depression Rating Scale (MADRS) for two-time points, were assessed by linear mixed models, respectively, for patients showing TRD and treatment response. A corrected p threshold of 0.001 was applied., Results: Overall symptom load reflected by MADRS ( p  < 0.0001) and lifetime hospitalization time ( p  < 0.0001) increased with age in TRD patients but not treatment responders. In TRD, higher age was predicting symptom severity of inner tension, reduced appetite, concentrations difficulties, and lassitude (all p  ≤ 0.001). Regarding clinical significance, older TRD patients were more likely to report severe symptoms (item score > 4) for these items both before and after treatment (all p  ≤ 0.001)., Conclusions: In this naturalistic sample of severely ill depressed patients, antidepressant treatment protocols were equally effective in addressing TRD in old age. However, specific symptoms such as sadness, appetite, and concentration showed an age-dependent presentation, impacting residual symptoms in severely affected TRD patients and calling for a precision approach by a better integration of age profiles in treatment recommendations.

About two thirds of the patients with major depressive disorder (MDD) do not sufficiently respond to monotherapy with antidepressants (ADs) which makes them reliant on further treatment approaches. Hereby, combination of different ADs and augmentation with second-generation antipsychotics (SGAs) are widely used and recommended psychopharmacotherapeutic strategies. The present secondary analyses are based on an international, naturalistic, cross-sectional multicenter study conducted by the European Group for the Study of Resistant Depression. Comparing socio-demographic and clinical characteristics of 436 adult MDD patients receiving either SGAs (N = 191, 43.8%) or ADs (N = 245, 56.2%), that were additionally administered to their first-line AD psychopharmacotherapy, we aimed to identify possible trajectories of decision-making for clinicians regarding which treatment option to prefer in individual patients. Our most robust findings represent an association of SGA augmentation with the presence of psychotic symptoms, longer mean duration of lifetime psychiatric hospitalizations, employment of further augmentation strategies with mood-stabilizers and benzodiazepines, and a trend towards higher mean daily dosages of their first-line ADs and current suicidal risk. Treatment outcome was not significantly different between patients receiving either SGA augmentation or AD combination. Being aware of limitations inherent to the cross-sectional study design and the lack of randomization, more severe and rather chronic conditions in MDD seemed to encourage clinicians to choose SGA augmentation over AD combination. The fact that mood-stabilizers and/or benzodiazepines were more frequently co-administered with SGAs may represent a requirement of an overall refined psychopharmacotherapy including additional fast-acting agents with potent AD, tranquilizing and anti-suicidal effects in MDD patients experiencing challenging clinical manifestations. New glutamatergic substances seem to be promising in this regard., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Sex-related effects on the evolution and phenotype of major depressive disorder (MDD) were reported previously., Methods: This European multicenter cross-sectional study compared sociodemographic, clinical, and treatment patterns between males and females in a real-world sample of 1410 in- and outpatients with current MDD., Results: Male MDD patients (33.1%) were rather inpatients, suffered from moderate to high suicidality levels, received noradrenergic and specific serotonergic antidepressants (ADs) as first-line AD treatment, generally higher mean AD daily doses, and showed a trend towards a more frequent administration of add-on treatments. Female MDD patients (66.9%) were rather outpatients, experienced lower suicidality levels, comorbid thyroid dysfunction, migraine, asthma, and a trend towards earlier disease onset., Conclusions: The identified divergencies may contribute to the concept of male and female depressive syndromes and serve as predictors of disease severity and course, as they reflect phenomena that were repeatedly related to treatment-resistant depression (TRD). Especially the greater necessity of inpatient treatment and more complex psychopharmacotherapy in men may reflect increased therapeutic efforts undertaken to treat suicidality and to avoid TRD. Hence, considering sex may guide the diagnostic and treatment processes towards targeting challenging clinical manifestations including comorbidities and suicidality, and prevention of TRD and chronicity., (© 2021 The Authors. Depression and Anxiety Published by Wiley Periodicals LLC.)

Background: The present multicenter study aimed at defining the clinical profile of patients with major depressive disorder (MDD) and comorbid migraine., Methods: Demographic and clinical information for 1410 MDD patients with vs without concurrent migraine were compared by descriptive statistics, analyses of covariance, and binary logistic regression analyses., Results: The point prevalence rate for comorbid migraine was 13.5% for female and 6.2% for male patients. MDD + migraine patients were significantly younger, heavier, more likely female, of non-Caucasian origin, outpatient, and suffering from asthma. The presence of MDD + migraine resulted in a significantly higher functional disability. First-line antidepressant treatment strategy revealed a trend towards agomelatine. Second-generation antipsychotics were significantly less often administered for augmentation treatment in migraineurs. Overall, MDD + migraine patients tended to respond worse to their pharmacotherapy., Conclusion: Treatment guidelines for comorbid depression and migraine are warranted to ensure optimal efficacy and avoid possible pitfalls in psychopharmacotherapy, including serotonin syndrome., (© The Author(s) 2020. Published by Oxford University Press on behalf of CINP.)

In this meta-analysis, we aimed to estimate and compare the efficacy of add-on treatment of antidepressants with esketamine nasal spray and second-generation antipsychotics in patients with nonpsychotic major depressive disorder and inadequate response to antidepressants. Searching for acute-phase, double-blind, placebo-controlled, randomized trials, we found 22 second-generation antipsychotic (n = 8363) and 3 intranasal esketamine (n = 641) studies. Mean change in the Montgomery Åsberg Depression Rating Scale total score served as outcome. We determined a higher mean difference (vs placebo) for the pooled esketamine nasal spray trials (mean difference = 4.09, 95% confidence interval: 2.01 to 6.17) than for the pooled second-generation antipsychotic augmentation trials (mean difference  = 2.05, 95% confidence interval: 1.51 to 2.59). Thus, the effect size for intranasal esketamine was nearly twice as high as those for the second-generation antipsychotics. This indicates high efficacy of add-on esketamine nasal spray in treatment-resistant major depressive disorder compared with other well-established, evidence-based pharmacological options such as augmentation with second-generation antipsychotics., (© The Author(s) 2020. Published by Oxford University Press on behalf of CINP.)

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.

Objective: To elucidate the impact of the presence of psychotic features in patients diagnosed with major depressive disorder (MDD) on sociodemographic, psychosocial, clinical, and response characteristics., Methods: A total of 1,410 DSM-IV-TR MDD patients were included in the present European multicenter study, which was conducted between 2011 and 2016. Analyses of covariance, χ² tests, and binary logistic regression analyses were performed to explore differences in sociodemographic and clinical variables between MDD patients with and without psychotic symptoms., Results: A prevalence rate of 10.92% for psychotic features was found in MDD. Compared to nonpsychotic MDD patients, those with psychotic features were characterized by a higher likelihood for melancholic characteristics (73.38% vs 59.16%, P = .0006), a higher rate of current suicide risk (60.39% vs 44.27%, P = .0002), greater likelihood of receiving inpatient treatment (55.84% vs 32.01%, P < .0001), greater depressive symptom severity (measured by various rating scales), and more often receiving augmentation/combination treatment strategies in general (81.17% vs 58.12%, P < .0001) and add-on therapy with antipsychotics (50.00% vs 22.69%, P < .0001) and benzodiazepines (47.40% vs 31.29%, P = .0001) in particular. Moreover, psychotic symptoms in MDD were highly predictive of treatment resistance, expressed by a more than 2.2-fold higher likelihood for resistance compared to nonpsychotic MDD patients (79.87% vs 35.75%, P < .0001). Only 3.25% of the patients with psychotic MDD achieved treatment response (vs 27.15% of those with nonpsychotic MDD, P < .0001)., Conclusions: These findings suggest that adequate diagnosis of psychotic features in MDD should be ensured in routine clinical care. As a combination of antipsychotics and antidepressants represents the first-line treatment option in psychotic MDD, the finding of a 2-fold higher prescription rate for antipsychotic drugs in psychotic versus nonpsychotic MDD patients reflects the current evidence., (© Copyright 2019 Physicians Postgraduate Press, Inc.)

Objectives: Clinical variables were investigated in the 'treatment resistant depression (TRD)- III' sample to replicate earlier findings by the European research consortium 'Group for the Study of Resistant Depression' (GSRD) and enable cross-sample prediction of treatment outcome in TRD., Experimental Procedures: TRD was defined by a Montgomery and Åsberg Depression Rating Scale (MADRS) score ≥22 after at least two antidepressive trials. Response was defined by a decline in MADRS score by ≥50% and below a threshold of 22. Logistic regression was applied to replicate predictors for TRD among 16 clinical variables in 916 patients. Elastic net regression was applied for prediction of treatment outcome., Results: Symptom severity (odds ratio (OR) = 3.31), psychotic symptoms (OR = 2.52), suicidal risk (OR = 1.74), generalized anxiety disorder (OR = 1.68), inpatient status (OR = 1.65), higher number of antidepressants administered previously (OR = 1.23), and lifetime depressive episodes (OR = 1.15) as well as longer duration of the current episode (OR = 1.022) increased the risk of TRD. Prediction of TRD reached an accuracy of 0.86 in the independent validation set, TRD-I., Conclusion: Symptom severity, suicidal risk, higher number of lifetime depressive episodes, and comorbid anxiety disorder were replicated as the most prominent risk factors for TRD. Significant predictors in TRD-III enabled robust prediction of treatment outcome in TRD-I., (© 2018 The Authors Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.)

Background: Treatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.AimsTo investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine., Method: A sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set (i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761) participants., Results: No individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027)., Conclusions: The identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant treatments and targets for new antidepressants.Declaration of interestD.S. has received grant/research support from GlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. S.M. has been a consultant or served on advisory boards for: AstraZeneca, Bristol-Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, Richter. S.K. has received grant/research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, Janssen and Neuraxpharm. J.Z. has received grant/research support from Lundbeck, Servier, Brainsway and Pfizer, has served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca and Roche and has served on speakers' bureaus for Lundbeck, Roch, Lilly, Servier, Pfizer and Abbott. J.M. is a member of the Board of the Lundbeck International Neuroscience Foundation and of Advisory Board of Servier. A.S. is or has been consultant/speaker for: Abbott, AbbVie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi and Servier. C.M.L. receives research support from RGA UK Services Limited.

Major depressive disorder is one of the most prevalent and life-threatening forms of mental illnesses and a major cause of morbidity worldwide. Currently available antidepressants are effective for most patients, although around 30% are considered treatment resistant (TRD), a condition that is associated with a significant impairment of cognitive function and poor quality of life. In this respect, the identification of the molecular mechanisms contributing to TRD represents an essential step for the design of novel and more efficacious drugs able to modify the clinical course of this disorder and increase remission rates in clinical practice. New insights into the neurobiology of TRD have shed light on the role of a number of different mechanisms, including the glutamatergic system, immune/inflammatory systems, neurotrophin function, and epigenetics. Advances in drug discovery processes in TRD have also influenced the classification of antidepressant drugs and novel classifications are available, such as the neuroscience-based nomenclature that can incorporate such advances in drug development for TRD. This review aims to provide an up-to-date description of key mechanisms in TRD and describe current therapeutic strategies for TRD before examining novel approaches that may ultimately address important neurobiological mechanisms not targeted by currently available antidepressants. All in all, we suggest that drug targeting different neurobiological systems should be able to restore normal function but must also promote resilience to reduce the long-term vulnerability to recurrent depressive episodes., (Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.)

Background: This European multicenter study aimed to elucidate suicidality in major depressive disorder. Previous surveys suggest a prevalence of suicidality in major depressive disorder of ≥50%, but little is known about the association of different degrees of suicidality with socio-demographic, psychosocial, and clinical characteristics., Methods: We stratified 1410 major depressive disorder patients into 3 categories of suicidality based on the Hamilton Rating Scale for Depression item 3 (suicidality) ratings (0=no suicidality; 1-2=mild/moderate suicidality; 3-4=severe suicidality). Chi-squared tests, analyses of covariance, and Spearman correlation analyses were applied for the data analyses., Results: The prevalence rate of suicidality in major depressive disorder amounted to 46.67% (Hamilton Rating Scale for Depression item 3 score ≥1). 53.33% were allocated into the no, 38.44% into the mild/moderate, and 8.23% into the severe suicidality patient group. Due to the stratification of our major depressive disorder patient sample according to different levels of suicidality, we identified some socio-demographic, psychosocial, and clinical variables differentiating from the patient group without suicidality already in presence of mild/moderate suicidality (depressive symptom severity, treatment resistance, psychotic features, add-on medications in general), whereas others separated only when severe suicidality was manifest (inpatient treatment, augmentation with antipsychotics and benzodiazepines, melancholic features, somatic comorbidities)., Conclusions: As even mild/moderate suicidality is associated with a failure of achieving treatment response, adequate recognition of this condition should be ensured in the clinical practice.

Objective: This multicenter, multinational, cross-sectional study aimed to investigate clinical characteristics and treatment outcomes associated with augmentation/combination treatment strategies in major depressive disorder (MDD)., Method: Sociodemographic, clinical, and treatment features of 1410 adult MDD patients were compared between MDD patients treated with monotherapy and augmentation/combination medication using descriptive statistics, analyses of covariance (ancova), and Spearman's correlation analyses., Results: 60.64% of all participants received augmentation and/or combination strategies with a mean number of 2.18 ± 1.22 simultaneously prescribed psychiatric drugs. We found male gender, older age, Caucasian descent, higher weight, low educational status, absence of occupation, psychotic symptoms, melancholic and atypical features, suicide risk, in-patient treatment, longer duration of hospitalization, some psychiatric comorbidities (panic disorder, agoraphobia, obsessive-compulsive disorder, and bulimia nervosa), comorbid somatic comorbidity in general and concurrent hypertension, thyroid dysfunction, diabetes, and heart disease in particular, higher current and retrospective Montgomery and Åsberg Depression Rating Scale total scores, treatment resistance, and higher antidepressant dosing to be significantly associated with augmentation/combination treatment. These findings were corroborated when examining the number of concurrently administered psychiatric drugs in the statistical analyses., Conclusion: Our findings suggest a clear association between augmentation/combination strategies and treatment-resistant/difficult-to-treat MDD conditions characterized by severe symptomatology and high amount of psychiatric and somatic comorbidities., (© 2018 The Authors Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd.)

Background: Initial improvement in the first weeks of antidepressant (AD) treatment is a useful early predictor of complete AD response. We performed a meta-analysis of AD studies to investigate whether a partial decrease in depressive symptoms by week 4 was associated with response and remission by weeks 6-14 in major depressive disorder (MDD). Finally, we focused on treatment-resistant depression (TRD: lack of response to prior AD) to test the impact of early improvement on a second AD treatment outcome and to compare different switching strategies., Methods: Meta-analysis was conducted on AD naturalistic studies published between 01.01.2000 and 06.30.2017. TRD was an exclusion criterion. TRD was analyzed in 407 MDD patients treated with venlafaxine for 6 weeks. The MADRS was used to define very early improvement (VEI: > 20% decrease at week 2), early improvement (EI: > 30% decrease at week 4) and remission (week 6 MADRS < 10). A theoretical model was used to simulate AD switch in TRD patients who failed to achieve remission (Algorithm A), VEI (Algorithm B) or EI (Algorithm C)., Results: Our meta-analysis (9 studies; N = 6185) showed significant associations between early improvement, response (OR: 3.28 95% C.I: 2.06-5.20) and remission (OR: 2.10 95% C.I: 1.53-2.87). 24.6% of TRD sample remitted. VEI was a poor outcome predictor: sensitivity = 0.52 (0.40-0.63); specificity = 0.82 (0.76-0.86); AUC = 0.67 (0.62-0.71). EI had a moderate predictive power: sensitivity = 0.87 (0.77-0.93); specificity = 0.71 (0.66-0.77); AUC = 0.76 (0.71-0.80). The best treatment scenario was Algorithm C (switch after 4 weeks) in which remission rate was marginally increased (35.1% vs 33.7% of Algorithm A). Algorithm B (switch after 2 weeks) led to a 4.3% decrease in remission compared to Algorithm A., Limitations: Inclusion of a naturalistic sample without a control arm; simulation of treatments., Conclusion: Although literature data suggest a correlation between an initial improvement of depressive symptoms and later response and remission during AD treatment, our analysis shows that such an early improvement is not a reliable outcome predictor in TRD. The nature of TRD is complex and different biological mechanisms and treatments might be necessary for TRD patients., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome., Method: 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation., Results: The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization., Conclusions: With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD., (© Copyright 2017 Physicians Postgraduate Press, Inc.)

Treatment resistance to the antidepressive pharmacotherapy represents one of the most important clinical challenges in the pharmacological management of unipolar depression. In this review, we aimed to summarise the evidence for various pharmacological treatment options in therapy-resistant unipolar depression derived from clinical trials, systematic reviews, meta-analyses and treatment guidelines. The first measure in case of insufficient response to the initial antidepressant monotherapy contains the debarment of 'pseudo-resistance', potentially caused by inadequate dose and treatment duration of the antidepressant, insufficient plasma levels, non-compliance of the patient regarding medication intake or relevant psychiatric and/or somatic comorbidities. Applying a dose escalation of the current antidepressant cannot be generally recommended as evidence-based treatment option and the efficacy depends on the class of antidepressants. There is no compelling evidence for a switch to another, new antidepressant compound after insufficient response to a previous antidepressant. The combination of two antidepressants should be preferentially established with antidepressants characterised by different mechanisms of action (e.g. reuptake inhibitors together with presynaptic autoreceptor inhibitors). At present, the most convincing body of evidence exists for the augmentation of antidepressants with second-generation antipsychotic drugs and lithium. Hence, both strategies are consistently advised by treatment guidelines as pharmacological first-line strategy in treatment-resistant depression.