Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis

A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder

Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.

When to hold and when to fold: Early prediction of nonresponse to transcranial magnetic stimulation in major depressive disorder

BACKGROUND

Determining when to recommend a change in treatment regimen due to insufficient improvement is a common challenge in therapeutics.

METHODS

In a sample of 7215 patients with major depressive disorder treated with transcranial magnetic stimulation (TMS) and with PHQ-9 scores before, during and after the course, 3 groups were identified based on number of acute course sessions: exactly 36 sessions (N = 3591), more than 36 sessions (N = 975), and less than 36 sessions (N = 2649). Two techniques were used to determine thresholds for percentage change in PHQ-9 scores at assessments after 10, 20, and 30 sessions that optimized prediction of endpoint response status: the Youden index and fixing the false positive rate at 10%. Positive and negative predictive values were calculated to assess the accuracy of identifying final nonresponders and responders, respectively.

RESULTS

There was greater accuracy in predicting final response than nonresponse, especially in the groups that had at least 36 sessions. Substantial proportions of patients with low levels of early improvement were classified as responders at the end of treatment.

LIMITATIONS

The findings should be validated with clinician ratings using a more comprehensive depression severity scale.

CONCLUSIONS

Manifesting clinical improvement early in the TMS course is strongly predictive of final status as a responder, while lack of early improvement is a relatively poor indicator of final nonresponse status. The predictive value of lack of early symptomatic improvement is too low to make reliable recommendations regarding changes in treatment regimen.

The Relationship of Early Sleep Improvement With Response to Pharmacotherapy in Unipolar Psychotic Depression

BACKGROUND

Since insomnia and depression are interrelated, improved sleep early in antidepressant pharmacotherapy may predict a positive treatment outcome. We investigated whether early insomnia improvement (EII) predicted treatment outcome in psychotic depression (PD) and examined if there was an interaction effect between EII and treatment type to assess if findings were treatment-specific.

METHODS

This study is a secondary analysis of a randomized trial comparing 7 weeks treatment with the antidepressants venlafaxine, imipramine and venlafaxine plus the antipsychotic quetiapine in PD ( n = 114). Early insomnia improvement, defined as ≥20% reduced insomnia after 2 weeks, was assessed by the Hamilton Rating Scale for Depression (HAM-D-17). Associations between EII and treatment outcome were examined using logistic regressions. Subsequently, we added interaction terms between EII and treatment type to assess interaction effects. The predictive value of EII was compared with early response on overall depression (≥20% reduced HAM-D-17 score after 2 weeks).

RESULTS

EII was associated with response (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.7-23.4; P = <0.001), remission of depression (OR, 6.1; 95% CI, 1.6-22.3; P = 0.009), and remission of psychosis (OR, 4.1; 95% CI, 1.6-10.9; P = 0.004). We found no interaction effects between EII and treatment type on depression outcome. Early insomnia improvement and early response on overall depression had a comparable predictive ability for treatment outcome.

CONCLUSIONS

Early insomnia improvement was associated with a positive outcome in pharmacotherapy of PD, regardless of the medication type. Future studies are needed to confirm our findings and to examine the generalizability of EII as predictor in treatment of depression.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

BACKGROUND

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS

Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS

After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS

Relatively small sample size and not a double-blind design.

CONCLUSIONS

The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Electroacupuncture alleviated depression‐like behaviors in ventromedial prefrontal cortex of chronic unpredictable mild stress‐induced rats: Increasing synaptic transmission and phosphorylating dopamine transporter

AIMS

Electroacupuncture (EA) shows advantages in both clinical practice and depression animal models. Dopaminergic-related dysfunction in the prefrontal cortex (PFC) may be a hidden antidepressant mechanism of EA, where dopamine transporter (DAT) plays an essential role. This study aimed to investigate the synaptic transmission and DAT-related changes of EA in depression.

METHODS

Male Sprague-Dawley rats were subjected to 3-week chronic unpredictable mild stress (CUMS). The successfully modeled rats were then randomly and equally assigned to CUMS, selective serotonin reuptake inhibitor (SSRI), and EA or SSRI + EA groups, followed by a 2-week treatment respectively. After monitoring body weight and behavioral tests of all rats, the ventromedial PFC (vmPFC) tissue was collected for electrophysiology and the expression detection of DAT, phosphorylated DAT (p-DAT), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), and trace amine-associated receptor 1 (TAAR1).

RESULTS

Depressive-like behaviors induced by CUMS were alleviated by EA, SSRI, and SSRI + EA treatments through behavioral tests. Compared with CUMS group, EA improved synaptic transmission in vmPFC by upregulating spontaneous excitatory postsynaptic currents amplitude. Molecularly, EA reversed the increased total DAT and p-DAT expression as well as the decreased ratio of p-DAT/total DAT along with the activation of TAAR1, cAMP, and PKA in vmPFC.

CONCLUSION

We speculated that the antidepressant effect of EA was associated with enhanced synaptic transmission in vmPFC, and the upregulated phosphorylation of DAT relevant to TAAR1, cAMP, and PKA may be the potential mechanism.

Venlafaxine XR treatment for older patients with major depressive disorder: decision trees for when to change treatment

BACKGROUND

Predictors of antidepressant response in older patients with major depressive disorder (MDD) need to be confirmed before they can guide treatment.

OBJECTIVE

To create decision trees for early identification of older patients with MDD who are unlikely to respond to 12 weeks of antidepressant treatment, we analysed data from 454 older participants treated with venlafaxine XR (150-300 mg/day) for up to 12 weeks in the Incomplete Response in Late-Life Depression: Getting to Remission study.

METHODS

We selected the earliest decision point when we could detect participants who had not yet responded (defined as >50% symptom improvement) but would do so after 12 weeks of treatment. Using receiver operating characteristic models, we created two decision trees to minimise either false identification of future responders (false positives) or false identification of future non-responders (false negatives). These decision trees integrated baseline characteristics and treatment response at the early decision point as predictors.

FINDING

We selected week 4 as the optimal early decision point. Both decision trees shared minimal symptom reduction at week 4, longer episode duration and not having responded to an antidepressant previously as predictors of non-response. Test negative predictive values of the leftmost terminal node of the two trees were 77.4% and 76.6%, respectively.

CONCLUSION

Our decision trees have the potential to guide treatment in older patients with MDD but they require to be validated in other larger samples.

CLINICAL IMPLICATIONS

Once confirmed, our findings may be used to guide changes in antidepressant treatment in older patients with poor early response.

Perceived helpfulness of treatment for specific phobia: Findings from the World Mental Health Surveys

BACKGROUND

Although randomized trials show that specific phobia treatments can be effective, it is unclear whether patients experience treatment as helpful in clinical practice. We investigated this issue by assessing perceived treatment helpfulness for specific phobia in a cross-national epidemiological survey.

METHODS

Cross-sectional population-based WHO World Mental Health (WMH) surveys in 24 countries (n=112,507) assessed lifetime specific phobia. Respondents who met lifetime criteria were asked whether they ever received treatment they considered helpful and the number of professionals seen up to the time of receiving helpful treatment. Discrete-event survival analysis was used to calculate conditional-cumulative probabilities of obtaining helpful treatment across number of professionals seen and of persisting in help-seeking after prior unhelpful treatment.

RESULTS

23.0% of respondents reported receiving helpful treatment from the first professional seen, whereas cumulative probability of receiving helpful treatment was 85.7% after seeing up to 9 professionals. However, only 14.7% of patients persisted in seeing up to 9 professionals, resulting in the proportion of patients ever receiving helpful treatment (47.5%) being much lower than it could have been with persistence in help-seeking. Few predictors were found either of perceived helpfulness or of persistence in help-seeking after earlier unhelpful treatments.

LIMITATIONS

Retrospective recall and lack of information about either types of treatments received or objective symptomatic improvements limit results.

CONCLUSIONS

Despite these limitations, results suggest that helpfulness of specific phobia treatment could be increased, perhaps substantially, by increasing patient persistence in help-seeking after earlier unhelpful treatments. Improved understanding is needed of barriers to help-seeking persistence.

The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial

Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.

Effectiveness of mirtazapine as add-on to paroxetine v. paroxetine or mirtazapine monotherapy in patients with major depressive disorder with early non-response to paroxetine: a two-phase, multicentre, randomized, double-blind clinical trial

BACKGROUND

This study aimed to examine the efficacy of combining paroxetine and mirtazapine v. switching to mirtazapine, for patients with major depressive disorder (MDD) who have had an insufficient response to SSRI monotherapy (paroxetine) after the first 2 weeks of treatment.

METHODS

This double-blind, randomized, placebo-controlled, three-arm study recruited participants from five hospitals in China. Eligible participants were aged 18-60 years with MDD of at least moderate severity. Participants received paroxetine during a 2-week open-label phase and patients who had not achieved early improvement were randomized to paroxetine, mirtazapine or paroxetine combined with mirtazapine for 6 weeks. The primary outcome was improvement on the Hamilton Rating Scale for Depression 17-item (HAMD-17) scores 6 weeks after randomization.

RESULTS

A total of 204 patients who showed early non-response to paroxetine monotherapy were randomly assigned to receive either mirtazapine and placebo (n = 68), paroxetine and placebo (n = 68) or mirtazapine and paroxetine (n = 68), with 164 patients completing the outcome assessment. At week 8, the least squares (LS) mean change of HAMD-17 scores did not significantly differ among the three groups, (12.98 points) in the mirtazapine group, (12.50 points) in the paroxetine group and (13.27 points) in the mirtazapine plus paroxetine combination group. Participants in the paroxetine monotherapy group were least likely to experience adverse effects.

CONCLUSIONS

After 8 weeks follow-up, paroxetine monotherapy, mirtazapine monotherapy and paroxetine/mirtazapine combination therapy were equally effective in non-improvers at 2 weeks. The results of this trial do not support a recommendation to routinely offer additional treatment or a switch in treatment strategies for MDD patients who do not show early improvement after 2 weeks of antidepressant treatment.

The use of sequential pharmacotherapy for the treatment of acute major depression: a scoping review

INTRODUCTION

Major Depressive Disorder (MDD) is a chronic, relapsing, and remitting disorder affecting over 250 million persons each year worldwide. More than 50% of the patients do not respond to their initial antidepressant treatment and may benefit from sequential pharmacotherapy for the acute treatment of their MDD. Although guidelines outline options for next-step treatments, there is a paucity of evidence to select specific second- or third-step treatments.

AREAS COVERED

This scoping review synthesizes and discusses available evidence for sequential pharmacotherapy for MDD. MEDLINE was searched from inception to 7 July 2020; 4490 studies were identified. We selected meta-analyses and reports on clinical trials that were judged to inform the sequential selection of pharmacotherapy for MDD.

EXPERT OPINION

Most relevant published trials are focused on, and support, the use of augmentation pharmacotherapy. There is also some support for other strategies such as combining or switching antidepressants. In the future, more studies need to directly compare these sequential options. To provide more personalized treatment within the framework of precision psychiatry, these studies should include an assessment of moderators and mediators ('mechanism') of antidepressant response.

The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

OBJECTIVES

To provide advice and guidance regarding the management of mood disorders, derived from scientific evidence and supplemented by expert clinical consensus to formulate s that maximise clinical utility.

METHODS

Articles and information sourced from search engines including PubMed, EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (e.g. books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Relevant information was appraised and discussed in detail by members of the mood disorders committee, with a view to formulating and developing consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous consultation and external review involving: expert and clinical advisors, key stakeholders, professional bodies and specialist groups with interest in mood disorders.

RESULTS

The Royal Australian and New Zealand College of Psychiatrists mood disorders clinical practice guidelines 2020 (MDcpg²⁰²⁰) provide up-to-date guidance regarding the management of mood disorders that is informed by evidence and clinical experience. The guideline is intended for clinical use by psychiatrists, psychologists, primary care physicians and others with an interest in mental health care.

CONCLUSION

The MDcpg²⁰²⁰ builds on the previous 2015 guidelines and maintains its joint focus on both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus.

MOOD DISORDERS COMMITTEE

Gin S Malhi (Chair), Erica Bell, Darryl Bassett, Philip Boyce, Richard Bryant, Philip Hazell, Malcolm Hopwood, Bill Lyndon, Roger Mulder, Richard Porter, Ajeet B Singh and Greg Murray.

Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues

Introduction: Individual participant data meta-analyses (IPD-MAs) include the raw data from relevant randomised clinical trials (RCTs) and involve secondary analyses of the data. Performed since the late 1990s, ~50 such meta-analyses have been carried out in psychiatry, mostly in the field of treatment. IPD-MAs are particularly relevant for three objectives: (1) evaluation of the average effect of an intervention by combining effects from all included trials, (2) evaluation of the heterogeneity of an intervention effect and sub-group analyses to approach personalised psychiatry, (3) mediation analysis or surrogacy evaluation to replace a clinical (final) endpoint for the evaluation of new treatments with intermediate or surrogate endpoints. The objective is to describe the interest and the steps of an IPD-MA method applied to the field of psychiatric therapeutic research. Method: The method is described in three steps. First, the identification of the relevant trials with an explicit description of the inclusion/exclusion criteria for the RCT to be incorporated in the IPD-MA and a definition of the intervention, the population, the context and the relevant points (outcomes or moderators). Second, the data management with the standardisation of collected variables and the evaluation and the assessment of the risk-of-bias for each included trial and of the global risk. Third, the statistical analyses and their interpretations, depending on the objective of the meta-analysis. All steps are illustrated with examples in psychiatry for treatment issues, excluding study protocols. Conclusion: The meta-analysis of individual patient data is challenging. Only strong collaborations between all stakeholders can make such a process efficient. An "ecosystem" that includes all stakeholders (questions of interest prioritised by the community, funders, trialists, journal editors, institutions, …) is required. International medical societies can play a central role in favouring the emergence of such communities.

Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants

Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting.

Esketamine Nasal Spray for Treatment-Resistant Depression

Major depression affects millions of adults in the United States, and women are affected at twice the rate of men. Approximately 30% of individuals may continue to experience depression symptoms despite treatment with oral antidepressants. In March 2019, the U.S. Food and Drug Administration approved esketamine (Spravato), which is specifically indicated for treatment-resistant depression. Available as a nasal spray in a health care office or facility, esketamine has the potential to fill an unmet need for individuals who continue to experience depression with conventional treatment. Symptom improvement may be seen as rapidly as 1 week after treatment initiation. This article provides an overview of esketamine, including administration, adverse effects, and practice implications for women's health nurses.

CA, Kasper S. Prognosis and improved outcomes in major depression: a review

Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha₂ being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha₂ and frontopolar alpha₁. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.