Trajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis

Disease-specific health-related quality of life trajectories up to 15 years after curative treatment for esophageal cancer-a prospective cohort study

BACKGROUND

The presence of distinct long-term disease-specific HRQL trajectories after curative treatment for esophageal cancer and factors associated with such trajectories are unclear.

MATERIALS AND METHODS

This population-based and longitudinal cohort study included 425 esophageal cancer patients who underwent curative treatment, including esophagectomy, in Sweden in 2001-2005 and were followed up until 2020, that is, 15-year follow-up. The outcomes were 10 disease-specific HRQL symptoms, measured by the well-validated EORTC QLQ-OES18 questionnaire at 6 months (n = 402 patients), and 3 (n = 178), 5 (n = 141), 10 (n = 92), and 15 years (n = 52) after treatment. HRQL symptoms were examined for distinct trajectories by growth mixture models. Weighted logistic regression models provided odds ratios (OR) with 95% confidence intervals (95% CI) for nine factors in relation to HRQL trajectories: age, sex, education, proxy baseline HRQL, comorbidity, tumor histology, chemo(radio)therapy, pathological tumor stage, and postoperative complications.

RESULTS

Distinct HRQL trajectories were identified for each of the 10 disease-specific symptoms. HRQL trajectories with more symptoms tended to persist or alleviate over time, while trajectories with fewer symptoms were more stable. Eating difficulty had three trajectories: persistently less, persistently moderate, and persistently more symptoms. The OR of having a persistently more eating difficulty trajectory was decreased for adenocarcinoma histology (OR = 0.44, 95% CI 0.21-0.95), and increased for pathological tumor stage III-IV (OR = 2.19, 95% CI 0.99-4.82) and 30-day postoperative complications (OR = 2.54, 95% CI 1.26-5.12).

CONCLUSION

Distinct trajectories with long-term persistent or deteriorating disease-specific HRQL symptoms were identified after esophageal cancer treatment. Tumor histology, tumor stage, and postoperative complications may facilitate detection of high-risk patients for unwanted trajectories.

Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning

BACKGROUND

Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.

METHOD

One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.

RESULTS

Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.

CONCLUSIONS

Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.

Discontinuation of psychotropic medication: a synthesis of evidence across medication classes

Pharmacotherapy is an effective treatment modality across psychiatric disorders. Nevertheless, many patients discontinue their medication at some point. Evidence-based guidance for patients, clinicians, and policymakers on rational discontinuation strategies is vital to enable the best, personalized treatment for any given patient. Nonetheless, there is a scarcity of guidelines on discontinuation strategies. In this perspective, we therefore summarize and critically appraise the evidence on discontinuation of six major psychotropic medication classes: antidepressants, antipsychotics, benzodiazepines, mood stabilizers, opioids, and stimulants. For each medication class, a wide range of topics pertaining to each of the following questions are discussed: (1) Who can discontinue (e.g., what are risk factors for relapse?); (2) When to discontinue (e.g., after 1 year or several years of antidepressant use?); and (3) How to discontinue (e.g., what's the efficacy of dose reduction compared to full cessation and interventions to mitigate relapse risk?). We thus highlight how comparing the evidence across medication classes can identify knowledge gaps, which may pave the way for more integrated research on discontinuation.

Dynamic risk factors of psychiatric readmission for major depressive disorder: A longitudinal study on patients treated with mono-antidepressant

In this comprehensive study, we sought to unravel the risk factors for recurrence in Major Depressive Disorder (MDD), uniquely focusing on patients undergoing mono-antidepressant treatment. By considering psychiatric readmission as a direct indicator of MDD recurrence, we meticulously analyzed the records of 1,456 inpatients from a Chinese mental health center from 2012 to 2020. Our follow-up periods, spanning 90, 180, and 365 days post-discharge, allowed for a nuanced understanding of the recurrence dynamics. We identified four critical risk factors: thyroid function (FT3 and TSH), high-density lipoprotein (HDL) levels, and region of residence. Notably, the study revealed an increasing risk of readmission associated with decreased FT3 and HDL over time, while elevated TSH and residing in another province 's impact diminished. The antidepressant type did not significantly alter readmission risks, providing a unique perspective on MDD management. This research contributes to the field by offering a deeper understanding of how demographic and biochemical factors influence the likelihood of MDD recurrence, guiding more effective treatment approaches.

Health-related quality of life trajectories up to 15 years after curative treatment for esophageal cancer: a prospective cohort study

BACKGROUND

The differentiation of specific, long-term health-related quality of life (HRQL) trajectories among esophageal cancer survivors remains unclear. The authors aimed to identify potentially distinctly different HRQL-trajectories and uncover the underlying factors of such trajectories in patients having undergone surgery (esophagectomy) for esophageal cancer.

MATERIALS AND METHODS

This nationwide, prospective, and longitudinal cohort study included 420 patients who underwent curative treatment for esophageal cancer, including esophageal cancer surgery, in Sweden from 2001to 2005. The main outcome was HRQL summary score trajectories, measured by the well-validated EORTC QLQ-C30 questionnaire at 6 months, 3, 5, 10, and 15 years after esophagectomy, and analyzed using growth mixture models. Potentially underlying factors for these trajectories (age, sex, education, proxy baseline HRQL, comorbidity, tumor histology, chemo(radio)therapy, pathological tumor stage, and postoperative complications) were analyzed using weighted logistic regression providing odds ratios (OR) with 95% CI.

RESULTS

Four distinct HRQL summary score trajectories were identified: Persistently good, improving, deteriorating, and persistently poor. The odds of belonging to a persistently poor trajectory were decreased by longer education (>12 years versus <9 years: OR 0.18, 95% CI: 0.05-0.66) and adenocarcinoma histology (adenocarcinoma versus squamous cell carcinoma: OR 0.37, 95% CI: 0.16-0.85), and increased by more advanced pathological tumor stage (III-IV versus 0-I: OR 2.82, 95% CI: 1.08-7.41) and postoperative complications (OR 2.94, 95% CI: 1.36-6.36).

CONCLUSION

Distinct trajectories with persistently poor or deteriorating HRQL were identified after curative treatment for esophageal cancer. Education, tumor histology, pathological tumor stage, and postoperative complications might influence HRQL trajectories. The results may contribute to a more tailored follow-up with timely and targeted interventions. Future research remains to confirm these findings.

Cancer-related fatigue trajectories up to 5 years after curative treatment for oesophageal cancer

BACKGROUND

Whether cancer-related fatigue develops differently after curative-intended oesophageal cancer treatment and the related modifiable factors are unclear.

METHODS

This population-based and longitudinal cohort included 409 oesophageal cancer patients who underwent curative oesophagectomy in 2013-2020 in Sweden. The main outcome was cancer-related fatigue trajectories with measurements at 1, 1.5, 2, 2.5, 3, 4 and 5 years postoperatively by validated EORTC QLQ-FA12 questionnaire, and analysed using growth mixture models. Weighted logistic regressions provided odds ratios (OR) with 95% confidence intervals (95% CI) for underlying sociodemographic, clinical, and patient-reported outcome factors in relation to the identified trajectories.

RESULTS

Two distinct overall cancer-related fatigue trajectories were identified: low level of persistent fatigue and high level of increasing fatigue, with 64% and 36% of patients, respectively. The odds of having high level of fatigue trajectory were increased by Charlson comorbidity index (≥ 2 versus 0: OR = 2.52, 95% CI 1.07-5.94), pathological tumour Stage (III-IV versus 0-I: OR = 2.52, 95% CI 1.33-4.77), anxiety (OR = 7.58, 95% CI 2.20-26.17), depression (OR = 15.90, 95% CI 4.44-56.93) and pain (continuous score: OR = 1.02, 95% CI 1.01-1.04).

CONCLUSIONS

Long-term trajectories with high level of increasing cancer-related fatigue and the associated modifiable factors were identified after oesophageal cancer treatment. The results may facilitate early identification and targeted intervention for such high-risk patients.

Ketamine and rapid antidepressant action: new treatments and novel synaptic signaling mechanisms

Ketamine is an open channel blocker of ionotropic glutamatergic N-Methyl-D-Aspartate (NMDA) receptors. The discovery of its rapid antidepressant effects in patients with depression and treatment-resistant depression fostered novel effective treatments for mood disorders. This discovery not only provided new insight into the neurobiology of mood disorders but also uncovered fundamental synaptic plasticity mechanisms that underlie its treatment. In this review, we discuss key clinical aspects of ketamine's effect as a rapidly acting antidepressant, synaptic and circuit mechanisms underlying its action, as well as how these novel perspectives in clinical practice and synapse biology form a road map for future studies aimed at more effective treatments for neuropsychiatric disorders.

Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments

Ketamine has emerged as a transformative and mechanistically novel pharmacotherapy for depression. Its rapid onset of action, efficacy for treatment-resistant symptoms, and protection against relapse distinguish it from prior antidepressants. Its discovery emerged from a reconceptualization of the neurobiology of depression and, in turn, insights from the elaboration of its mechanisms of action inform studies of the pathophysiology of depression and related disorders. It has been 25 y since we first presented our ketamine findings in depression. Thus, it is timely for this review to consider what we have learned from studies of ketamine and to suggest future directions for the optimization of rapid-acting antidepressant treatment.

Risk factors for relapse or recurrence in women with bipolar disorder and recurrent major depressive disorder in the perinatal period: a systematic review

It is well known that the perinatal period supposes a considerable risk of relapse for women with bipolar disorder (BD) and recurrent major depressive disorder (rMDD), with the consequences that this entails. Therefore, the authors sought to provide a critical appraisal of the evidence related to specific risk factors for this population with the aim of improving the prevention of relapses during pregnancy and postpartum. The authors conducted a systematic review assessing 18 original studies that provided data on risk factors for relapse or recurrence of BD and/or rMDD in the perinatal period (pregnancy and postpartum). Recurrences of BD and rMDD are more frequent in the postpartum period than in pregnancy, with the first 4-6 weeks postpartum being especially complicated. In addition, women with BD type I are at higher risk than those with BD type II and rMDD, and the most frequent presentation of perinatal episodes of both disorders is a major depressive episode. Other risk factors consistently repeated were early age of onset of illnesses, severity criteria, primiparity, abrupt discontinuation of treatment, and personal or family history of perinatal affective episodes. This review shows that there are common and different risk factors according to the type of disorder and to perinatal timing (pregnancy or postpartum) that should be known for an adequate prevention of relapses.

Depression symptom outcomes and re-engagement among VA patients who discontinue care while symptomatic

OBJECTIVE

Evaluate outcomes of Veterans who discontinued treatment with at least moderate ongoing depressive symptoms.

METHOD

Veterans with elevated depression symptoms from 29 Department of Veterans Affairs facilities completed baseline surveys and follow-up assessments for one year. Analyses examined rates and predictors of treatment discontinuation, treatment re-engagement, and subsequent symptoms among patients who remained out of care.

RESULTS

A total of 242 (17.8%; n = 1359) participants discontinued treatment while symptomatic, with Black participants, participants with less severe depression, and participants receiving only psychotherapy (versus combined psychotherapy and antidepressant medications) discontinuing at higher rates. Among all participants who discontinued treatment (n = 445), 45.8% re-engaged within the following six months with participants receiving combined treatment re-engaging at higher rates. Of participants who discontinued while symptomatic within the first 6 months of the study and did not return to care (n = 112), 68.8% remained symptomatic at 12 months. Lower baseline treatment expectancy and greater depression symptom severity were associated with remaining symptomatic while untreated.

CONCLUSIONS

Black race, lower symptom severity, and treatment modality may help identify patients at higher risk for discontinuing care while symptomatic, whereas patients with lower treatment expectations may be at greater risk for remaining out of care despite continuing symptoms.

Characteristics of persistent depression in the long-term: Randomized controlled trial and two-year observational study

Major depressive disorder is a chronic condition that can recur and relapse. It would be clinically useful to know the patient background to predict the chronicity of depressive symptoms, and the change in diagnosis of bipolar disorder. This study included 197 patients enrolled in a six-week randomized controlled trial with a two-year follow-up. We conducted multiple logistic regression analyses to identify the clinical and sociodemographic characteristics associated with persistent depressive disorder (PDD), relapse, and changes in bipolar disorder diagnosis. The significantly correlated factors were residual symptoms, including insight, work and activity, and general somatic symptoms at week six. We could not identify any factors that contributed to relapse or change in the diagnosis of bipolar disorder. We found that the specific residual symptoms of acute treatment affected long-term treatment outcomes for depression. Attention should be paid to the residual symptoms of depression in the early stages of treatment, and measures should be considered to improve them. There are several limitations to this study, including the fact that PDD may exist among patients who discontinued treatment, treatment was not standardized during the study period, and adherence was confirmed verbally.

An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders

We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an umbrella review (protocol  link ), searching MEDLINE/Embase (19/07/2022), including systematic reviews of studies reporting on MCPs of response, remission, recovery, or relapse, in DSM/ICD-defined mental disorders. From published predictors, we filtered MCPs, validating MCP criteria. AMSTAR2/PROBAST measured quality/risk of bias of systematic reviews/individual studies. We included 117 systematic reviews, 403 studies, 299,888 individuals with mental disorders, testing 796 prediction models. Only 4.3%/1.2% of the systematic reviews/individual studies were at low risk of bias. The most frequently targeted outcome was remission (36.9%), the least frequent was recovery (2.5%). Studies mainly focused on depressive (39.4%), substance-use (17.9%), and schizophrenia-spectrum (11.9%) disorders. We identified numerous MCPs within disorders for response, remission and relapse, but none for recovery. Transdiagnostic MCPs of remission included lower disease-specific symptoms (disorders = 5), female sex/higher education (disorders = 3), and quality of life/functioning (disorders = 2). Transdiagnostic MCPs of relapse included higher disease-specific symptoms (disorders = 5), higher depressive symptoms (disorders = 3), and younger age/higher anxiety symptoms/global illness severity/ number of previous episodes/negative life events (disorders = 2). Finally, positive trans-outcome MCPs for depression included less negative life events/depressive symptoms (response, remission, less relapse), female sex (response, remission) and better functioning (response, less relapse); for schizophrenia, less positive symptoms/higher depressive symptoms (remission, less relapse); for substance use disorder, marital status/higher education (remission, less relapse). Male sex, younger age, more clinical symptoms and comorbid mental/physical symptoms/disorders were poor prognostic factors, while positive factors included social contacts and employment, absent negative life events, higher education, early access/intervention, lower disease-specific and comorbid mental and physical symptoms/conditions, across mental disorders. Current data limitations include high risk of bias of studies and extraction of single predictors from multivariable models. Identified MCPs can inform future development, validation or refinement of prediction models of key outcomes in mental disorders.

Maintenance repetitive transcranial magnetic stimulation (rTMS) therapy for treatment-resistant depression: a study protocol of a multisite, prospective, non-randomized longitudinal study

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is a widely used treatment for major depressive disorder (MDD), and its effectiveness in preventing relapse/recurrence of MDD has been explored. Although few small sample controlled studies exist, the protocols of maintenance rTMS therapy were heterogeneous and evidence of its effectiveness is not sufficient. Thus, this study aims to evaluate whether maintenance rTMS is effective in maintaining the treatment response in patients with MDD with a large sample size and feasible study design.

METHODS

In this multicenter open-labelled parallel-group trial we plan to recruit 300 patients with MDD who have responded or remitted to acute rTMS therapy. Participants would be classified into two groups according to their preference; the maintenance rTMS and pharmacotherapy group, and the pharmacotherapy only group. The protocol of maintenance rTMS therapy is once a week for the first six months and once biweekly for the second six months. The primary outcome is the relapse/recurrence rates during 12 months following enrollment. Other measures of depressive symptoms and recurrence/relapse rates at different time points are the secondary outcomes. The primary analysis is the between-group comparison adjusted for background factors using a logistic regression model. We will perform the group comparison with inverse probability of treatment weighting as the sensitivity analysis to ensure the comparability of the two groups.

DISCUSSION

We hypothesize that maintenance rTMS therapy could be an effective and safe treatment for preventing depressive relapse/recurrence. Considering the limitation of potential bias owing to the study design, we plan to use statistical approaches and external data to avoid overestimation of the efficacy.

TRIAL REGISTRATION

Japan Registry of Clinical Trials, ID: jRCT1032220048 . Registered 1 May 2022.

Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach

BACKGROUND

Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain 'early warning signals' (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD).

METHODS

Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms.

RESULTS

Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = -0.23, p = 0.23) nor in network connectivity (r = -0.12, p = 0.59) were associated with changes in depression symptoms.

CONCLUSIONS

This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

An update on pharmacotherapy for recurrent depression in 2022

INTRODUCTION

Major depressive disorder remains a major challenge due to its biopsychosocial burden with increased morbidity and mortality. Despite successful treatment options for the acute episode, recurrence rates are high, on average four times in a life span.

AREAS COVERED

Both pharmacological as non-pharmacological evidence-based therapeutic options to prevent and treat recurrent depression are discussed.

EXPERT OPINION

Although some risk factors for recurrence are well known, better evidence is needed. Antidepressant medication should be continued after acute treatment at its full therapeutic dose for longer periods, at least 1 year. There are no clear differences between classes of antidepressant medication when treatment is focused on preventing relapse. Bupropion is the only antidepressant with a proven efficacy to prevent recurrence in seasonal affective disorder. Recent findings conclude maintenance subanesthetic ketamine and esketamine treatment can be effective in sustaining antidepressant effect following remission. Furthermore, the pharmacological approach must be integrated with lifestyle interventions, especially aerobic exercise. Finally, combining pharma- and psychotherapy seems to improve outcome. Network and complexity sciences will help to decrease the high recurrence rates of MDD by developing more integrative and personalized approaches.

Association analysis of gut microbiota and efficacy of SSRIs antidepressants in patients with major depressive disorder

BACKGROUND

Relevant studies have shown that gut microbiome plays an important role in the occurrence, development and treatment of major depressive disorder (MDD). Many studies have also shown that, selective serotonin reuptake inhibitors (SSRIs) antidepressants can improve the symptoms of depression by changing the distribution of gut microbiome, Here we investigated whether a distinct gut microbiome was associated with Major depressive disorder (MDD), and how it was modulated by SSRIs antidepressants.

METHOD

In this study, we analyzed the gut microbiome composition of 62 patients with first-episode MDD and 41 matched healthy controls, before SSRIs antidepressants treatment, using 16S rRNA gene sequencing. MDD patients characterized as treatment-resistant (TR) or responders (R) to antidepressants by score reduction rate were ≥50 % after SSRIs antidepressants treatment for eight weeks.

RESULTS

LDA effect size (LEfSe) analysis found that there were 50 different bacterial groups among the three groups, of which 19 genera were mainly at the genus level. The relative abundance of 12 genera increased in the HCs group, 5 genera in the R group increased in relative abundance, and 2 genera in the TR group increased in relative abundance. The correlation analysis of 19 bacterial genera and the score reduction rate showed that Blautia, Bifidobacterium and Coprococcus with higher relative abundance in the treatment effective group were related to the efficacy of SSRIs antidepressants.

CONCLUSIONS

Patients with MDD have a distinct gut microbiome that changes after SSRIs antidepressants treatment. Dysbiosis could be a new therapeutic target and prognostic tool for the treatment of patients with MDD.

Predicting relapse from the time to remission during the acute treatment of depression: A re-analysis of the STAR*D data

BACKGROUND

Predicting relapse during maintenance treatment for depression is challenging. The objective of this analysis was to investigate the association between the time taken to achieve remission in the acute phase, and the subsequent relapse rate or time to relapse using the Sequenced Treatment Alternatives to Relieve Depression dataset.

METHOD

Data of 1296 outpatients with nonpsychotic depression who entered a 12-month naturalistic follow-up period after achieving remission with citalopram for up to 14 weeks were analyzed. One-way analysis of variance and the Jonckheere-Terpstra trend test were performed to compare the relapse rates and days to relapse during the follow-up period among those who achieved remission at weeks 2, 4, 6, 9, 12, and 14. Remission and relapse were defined as scores of ≤5 and ≥11, respectively, on the 16-Item Quick Inventory of Depressive Symptomatology and Self-Report.

RESULTS

The relapse rates were significantly different among those who achieved remission each week (F_{(5, 1087)} = 4.995, p < 0.001). The lowest and highest relapse rates were observed in those who achieved remission at weeks 4 (25.7 %) and 12 (42.4 %), respectively, with a significant difference (p = 0.006). There was also a significant negative trend between the weeks taken to achieve remission and the days to relapse (z = -6.13, p < 0.001).

CONCLUSIONS

Patients with depression who show a faster response to antidepressant treatment are more likely to maintain remission in the long term. This finding suggests that, to prevent relapse, close attention should be paid to patients who require a relatively long time to achieve remission.

Associations between gastrointestinal symptoms, medication use, and spontaneous drug discontinuation in patients with major depressive disorder in China

BACKGROUND

The study was designed to investigate the associations between gastrointestinal (GI) symptoms, medication use, and spontaneous drug discontinuation (SDD) in patients with major depressive disorder (MDD).

METHODS

This cross-sectional study included 3256 MDD patients from the National Survey on Symptomatology of Depression (NSSD). Differences in the sociodemographic factors, clinical characteristics, medication use, and self-reported reasons for SDD were compared in patients with different frequencies of GI symptoms. A multiple logistic regression analysis was employed to assess the contribution of GI symptoms to the risk of spontaneous drug discontinuation.

RESULTS

MDD patients with a higher frequency of GI symptoms were prone to have higher proportions of mood stabilizer and benzodiazepine uses (p_{s for trend} < 0.001) but a lower proportion of SNRI use (p_{for trend} < 0.001). With the increase in GI symptoms, patients were prone to report worries about long-term side effects (p_{for trend} < 0.001), with the patients stating ineffective treatments (p_{for trend} = 0.002) and intolerance of adverse drug reactions (p_{for trend} = 0.022) as the reasons for SDD. Compared with those patients without GI symptoms, all of the MDD patients with GI symptom frequencies of several days (OR = 1.317; 95 % CI: 1.045-1.660), more than half of all days (OR = 1.305; 95 % CI: 1.005-1.695), and nearly every day (OR = 1.820; 95 %: 1.309-2.531) had an increased risk of SDD.

CONCLUSION

GI symptoms are highly associated with drug discontinuation in MDD patients. These findings may have important implications for clinical treatment options, as well as for drug adherence management, in MDD patients.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting

The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.

Body Mass Index-Adjusted Weight Loss Grading System and Cancer-Related Fatigue in Survivors 1 Year After Esophageal Cancer Surgery

BACKGROUND

The association between pre- and postoperative weight loss and cancer-related fatigue after esophageal cancer surgery is unclear. This nationwide, prospective, longitudinal cohort study aimed to assess the influence of weight loss on cancer-related fatigue among esophageal cancer survivors.

METHODS

Patients who underwent esophagectomy for cancer between 2013 and 2019 in Sweden were enrolled in this study. Exposure was measured by the body mass index-adjusted weight loss grading system (WLGS). Cancer-related fatigue was assessed using the fatigue scale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the EORTC QLQ-Fatigue 12 (QLQ-FA12) questionnaire measuring overall fatigue and physical, emotional, and cognitive fatigue. Growth mixture models were used to identify unobserved trajectories of cancer-related fatigue. Multivariable linear and logistic regression models were fitted to assess the associations between WLGS and cancer-related fatigue, adjusting for potential confounders.

RESULTS

Three trajectories were identified-low, moderate, and severe persistent fatigue. Cancer-related fatigue remained stable in each trajectory between 1 and 3 years after esophagectomy. Among the 356 enrolled patients, 4.5-22.6% were categorized into the severe persistent fatigue trajectory in terms of QLQ-C30 (19.9%), FA12 overall (10.5%), physical (22.6%), emotional (15.9%), and cognitive fatigue (4.5%). No association between pre- or postoperative WLGS and cancer-related fatigue was found between 1 and 3 years after esophageal cancer surgery.

CONCLUSIONS

Weight loss did not seem to influence cancer-related fatigue after esophageal cancer surgery.

Approach to Evaluating and Managing Adult Attention-Deficit/Hyperactivity Disorder in Primary Care

Requests for the evaluation of potential adult attention-deficit/hyperactivity disorder (ADHD) is on the rise across primary care clinics. Many health care providers, however, may feel ill equipped to diagnose and manage adults presenting with inattention and impulsivity. The diagnosis of ADHD is often complicated by medical and psychiatric conditions that can contribute to inattention symptoms. In this article, the authors provide a pragmatic clinical approach for evaluating and managing adult ADHD in the primary care setting.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Physiological and self-reported arousal in virtual reality versus face-to-face emotional activation and cognitive restructuring in university students: A crossover experimental study using wearable monitoring

BACKGROUND

Arousal may be important for learning to restructure ones' negative cognitions, a core technique in depression treatment. In virtual reality (VR), situations may be experienced more vividly than, e.g., in an imaginative approach, potentially aiding the emotional activation of negative cognitions. However, it is unclear whether such activation and subsequent cognitive restructuring in VR elicits more physiological, e.g. changes in skin conductance (SC), heart rate (HR), and self-reported arousal.

METHOD

In a cross-over experiment, 41 healthy students experienced two sets, one in VR, one face-to-face (F2F), of three situations aimed at activating negative cognitions. Order of the sets and mode of delivery were randomised. A wristband wearable monitored SC and HR; self-reported arousal was registered verbally.

RESULTS

Repeated measures analyses of variance revealed significantly more SC peaks per minute, F (1, 40) = 13.89, p = .001, higher mean SC, F (1,40) = 7.47, p = .001, and higher mean HR, F (1, 40) = 75.84, p < .001 in VR compared to F2F. No differences emerged on the paired-samples t-test for self-reported arousal, t (40) = -1.35, p = .18.

DISCUSSION

To the best of our knowledge, this is the first study indicating that emotional activation and subsequent cognitive restructuring in VR can lead to significantly more physiological arousal compared to an imaginative approach. These findings need to be replicated before they can be extended to patient populations.

Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis

Objective: Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse. Materials and Methods: Anonymized records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analyzed. To characterize the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders. Results: Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001]. Discussion: The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random. Conclusion: This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.

Discontinuing antidepressant medication after mindfulness-based cognitive therapy: a mixed-methods study exploring predictors and outcomes of different discontinuation trajectories, and its facilitators and barriers

OBJECTIVES

This study aimed to explore predictors and outcomes associated with different trajectories of discontinuing antidepressant medication (ADM), in recurrently depressed individuals after participation in mindfulness-based cognitive therapy (MBCT). Facilitators and barriers of discontinuation were explored qualitatively.

DESIGN

Mixed-methods study combining quantitative and qualitative data, drawn from a randomised controlled trial.

SETTING

Twelve secondary and tertiary psychiatric outpatient clinics in the Netherlands.

PARTICIPANTS

Recurrently depressed individuals (N=226) who had been using ADM for at least 6 months and in partial or full remission. Regardless of trial condition, we made post-hoc classifications of patients' actual discontinuation trajectories: full discontinuation (n=82), partial discontinuation (n=34) and no discontinuation (n=110) of ADM within 6 months after baseline. A subset of patients (n=15) and physicians (n=7) were interviewed to examine facilitators and barriers of discontinuation.

INTERVENTIONS

All participants were offered MBCT, which consisted of eight weekly sessions in a group.

PRIMARY AND SECONDARY OUTCOME MEASURES

Demographic and clinical predictors of successful discontinuation within 6 months, relapse risk within 15 months associated with different discontinuation trajectories, and barriers and facilitators of discontinuation.

RESULTS

Of the 128 patients assigned to MBCT with discontinuation, only 68 (53%) fully discontinued ADM within 6 months, and 17 (13%) discontinued partially. Predictors of full discontinuation were female sex, being employed and lower levels of depression. Relapse risk was lower after no discontinuation (45%) or partial discontinuation (38%), compared with full discontinuation (66%) (p=0.02). Facilitators and barriers of discontinuation were clustered within five themes: (1) pre-existing beliefs about depression, medication and tapering; (2) current experience with ADM; (3) life circumstances; (4) clinical support and (5) mindfulness.

CONCLUSIONS

Discontinuing antidepressants appears to be difficult, stressing the need to support patients and physicians in this process. MBCT may offer one of these forms of support.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT00928980); post-results.

Patient Response Trajectories in Major Depressive Disorder

OBJECTIVE

To investigate whether the efficacy of antidepressants can be understood in terms of patient response-trajectory classes.

EXPERIMENTAL DESIGN

Patient-level data were analysed from 1357 adults with MDD randomised to either escitalopram 20 mg/day (n = 676) or placebo (n = 681) in five 8-week randomised placebo-controlled trials. Growth mixture models (GMMs) were used to identify the response trajectories; longitudinal latent class analysis (LLCA) was used to corroborate the findings.

PRINCIPAL OBSERVATIONS

Three classes of response were identified for escitalopram and placebo based on the trajectory of the patients' Montgomery-Åsberg Depression Rating Scale (MADRS) total scores during treatment. All three classes had similar mean baseline MADRS scores, but the change from baseline after 8 weeks differed: -4.2 MADRS points for non-responders, -18.4 MADRS points for slow responders, and -26.7 points for fast responders. The proportions of non-responders, slow responders and fast responders were 53%, 38% and 9%, respectively, with placebo and 27%, 58% and 14%, respectively, with escitalopram. Receiver operating curve analysis showed that a cut-off of ≥43% improvement from baseline to week 2 predicted fast responders, and a cut-off of ≥28% improvement from baseline to week 4 predicted responders (fast or slow). There were no clinically useful differences at baseline that predicted the trajectory class to which a patient would belong.

CONCLUSIONS

The presence of fast-, slow- and non-responder classes has a clear clinical relevance for guiding treatment decisions; individual patients can be classified by the change in their MADRS score from baseline at 2 or 4 weeks.

Improving Depression Outcome by Patient-Centered Medical Management

(Reprinted with permission from The American Journal of Psychiatry 2018; 175:1187-1198).

Predictors of recurrence of major depressive disorder

A total of 201 patients with major depressive disorder from four hospitals in Malaysia were followed up for 5 years to determine the prognostic factors of recurrent major depressive disorder that could potentially contribute to improving the management of MDD patients. For each individual patient, at the time of recruitment as part of a case-control study, information was collected on recent threatening life events, personality and social and occupational functioning, while blood samples were collected to genotype single nucleotide polymorphisms of vitamin D receptor (VDR), zinc transporter-3 (ZnT3), dopamine transporter-1 (DAT1), brain-derived neurotropic factor (BDNF), serotonin receptor 1A (HT1A) and 2A (HT2A) genes. Kaplan-Meier and Cox-regression were used to estimate hazard functions for recurrence of major depressive disorder. Individuals with severe MDD in previous major depressive episodes had five and a half times higher hazard of developing recurrence compared to mild and moderate MDD (HR = 5.565, 95% CI = 1.631–18.994, p = 0.006). Individuals who scored higher on social avoidance had three and a half times higher hazard of recurrence of MDD (HR = 3.525, 95% CI = 1.349–9.209; p = 0.010). There was significant interaction between ApaI +64978C>A single nucleotide polymorphism and severity. The hazard ratio increased by 6.4 times from mild and moderate to severe MDD for A/A genotype while that for C/A genotype increased by 11.3 times. Social avoidance and severity of depression at first episode were prognostic of recurrence. Screening for personality factors at first encounter with MDD patients needs to be considered as part of the clinical practice. For those at risk of recurrence in relation to social avoidance, the psychological intervention prescribed should be customized to focus on this modifiable factor. Prompt and appropriate management of severe MDD is recommended to reduce risk of recurrence.

Predicting individual clinical trajectories of depression with generative embedding

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Patients with major depressive disorder (MDD) show variable clinical trajectories.

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Generative embedding (GE) is used to predict clinical trajectories in MDD patients.

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GE classifies patients with chronic depression vs. fast remission with 79% accuracy.

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GE provides mechanistic interpretability and outperforms conventional measures.

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Proof-of-concept that illustrates the potential of GE for clinical prediction.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.

Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach

BACKGROUND

Although depression has a high rate of recurrence, no prior studies have established a method that could identify the warning signs of its recurrence.

METHODS

We collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. We assessed depression and its recurrence using both the Kessler Psychological Distress Scale (K6) and the Patient Health Questionnaire-9.

RESULTS

A panel vector autoregressive analysis indicated that long sleep time was a important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks.

CONCLUSIONS

The panel vector autoregressive approach can identify the warning signs of depression recurrence; however, the convenient sampling of the present cohort may limit the scope towards drawing a generalised conclusion.

The Cost-Effectiveness of Cognitive Behavioral Therapy Versus Second-Generation Antidepressants for Initial Treatment of Major Depressive Disorder in the United States: A Decision Analytic Model

Background

Most guidelines for major depressive disorder recommend initial treatment with either a second-generation antidepressant (SGA) or cognitive behavioral therapy (CBT). Although most trials suggest that these treatments have similar efficacy, their health economic implications are uncertain.

Objective

To quantify the cost-effectiveness of CBT versus SGA for initial treatment of depression.

Design

Decision analytic model.

Data Sources

Relative effectiveness data from a meta-analysis of randomized controlled trials; additional clinical and economic data from other publications.

Target Population

Adults with newly diagnosed major depressive disorder in the United States.

Time Horizon

1 to 5 years.

Perspectives

Health care sector and societal.

Intervention

Initial treatment with either an SGA or group and individual CBT.

Outcome Measures

Costs in 2014 U.S. dollars, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios.

Results of Base-Case Analysis

In model projections, CBT produced higher QALYs (3 days more at 1 year and 20 days more at 5 years) with higher costs at 1 year (health care sector, $900; societal, $1500) but lower costs at 5 years (health care sector, -$1800; societal, -$2500).

Results of Sensitivity Analysis

In probabilistic sensitivity analyses, SGA had a 64% to 77% likelihood of having an incremental cost-effectiveness ratio of $100 000 or less per QALY at 1 year; CBT had a 73% to 77% likelihood at 5 years. Uncertainty in the relative risk for relapse of depression contributed the most to overall uncertainty in the optimal treatment.

Limitation

Long-term trials comparing CBT and SGA are lacking.

Conclusion

Neither SGAs nor CBT provides consistently superior cost-effectiveness relative to the other. Given many patients' preference for psychotherapy over pharmacotherapy, increasing patient access to CBT may be warranted.

Primary Funding Source

Department of Veterans Affairs, National Institute of Mental Health.

Long-term outcomes of trials in the National Institute for Health and Care Excellence depression guideline

The forthcoming National Institute for Health and Care Excellence depression guideline reviews short-term outcomes for long-term depression. We present effect sizes for long-term outcomes in trials that report these data. Psychological therapies become more effective, whereas antidepressants become less effective over the long term. We review other forms of longitudinal research that support these findings.

Predictors of recurrence in a sample of 508 outpatients with major depressive disorder

OBJECTIVE

Specific predictors of relapse/recurrence in major depressive disorder (MDD) have been identified but evidence across studies are inconsistent. This study aimed to identify the most relevant socio-demographic/clinical predictors of MDD recurrence in a sample of 508 outpatients.

METHODS

This naturalistic cohort study included 508 currently euthymic MDD patients (mean age = 54.1 ± 16.2) of which 53.9% had a single and 46.1% recurrent depressive episodes. A detailed data collection was performed and illness histories were retraced through clinical files and lifetime computerized medical records.

RESULTS

Compared to patients with single episode, MDD patients with recurrent episodes significantly differ regarding current age, gender, working status, positive history of psychiatric disorders in family, first-lifetime illness episode characteristics, first-episode and current psychotic symptoms, current melancholic features and seasonality, age at first treatment, duration of untreated illness, and comorbid cardiovascular/endocrinological conditions. However, after multivariate analyses controlling for current age, gender, educational level, working status differences, psychiatric conditions in family, and age of illness episode, recurrence was associated with older age (p ≤ .001), younger age at first treatment (p ≤ .005), being treated with previous psychoactive treatments (p .001), and longer duration of untreated illness (p .001).

CONCLUSIONS

The variables associated with MDD recurrence identified in the current study may aid in the stratification of patients who could benefit from more intensive maintenance treatments for MDD. However, clinicians should rapidly identify cases that are not likely to recur in order to avoid unnecessary treatments which are commonly considered as the standard of care.

Prediction of prolonged treatment course for depressive and anxiety disorders in an outpatient setting: The Leiden routine outcome monitoring study

OBJECTIVE

The aim of this study was to improve clinical identification of patients with a prolonged treatment course for depressive and anxiety disorders early in treatment.

METHOD

We conducted a cohort study in 1.225 adult patients with a depressive or anxiety disorders in psychiatric specialty care setting between 2007 and 2011, with at least two Brief Symptom Inventory (BSI) assessments within 6 months. With logistic regression, we modelled baseline age, gender, ethnicity, education, marital status, housing situation, employment status, psychiatric comorbidity and both baseline and 1st follow-up BSI scores to predict prolonged treatment course (>2 years). Based on the regression coefficients, we present an easy to use risk prediction score.

RESULTS

BSI at 1st follow-up proved to be a strong predictor for both depressive and anxiety disorders (OR = 2.17 (CI95% 1.73-2.74); OR = 2.52 (CI95% 1.86-3.23)). The final risk prediction score included BSI 1st follow-up and comorbid axis II disorder for depressive disorder, for anxiety disorders BSI 1st follow-up and age were included. For depressive disorders, for 28% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was60% (sensitivity 0.38, specificity 0.81). For anxiety disorders, for 35% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was 52% (sensitivity 0.55, specificity 0.75).

CONCLUSIONS

A high level of symptoms at 2-6 months of follow-up is a strong predictor for prolonged treatment course. This facilitates early identification of patients at risk of a prolonged course of treatment; in a relatively easy way by a self-assessed symptom severity.

Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression

Background

Major Depressive Disorder (MDD) is a prevalent, disruptive illness. A majority of those with MDD are at high risk for recurrence and increased risk for morbidity and mortality. This study examined whether multimodal baseline (and retest) Cognitive Control performance and neuroimaging markers (task activation and neural connectivity between key brain nodes) could differentiate between those with and without future recurrence of a major depressive (MD) episode within one year. We hypothesized that performance and neuroimaging measures of Cognitive Control would identify markers that differ between these two groups.

Methods

A prospective cohort study of young adults (ages 18–23) with history (h) of early-onset MDD (N = 60), now remitted, and healthy young adults (N = 49). Baseline Cognitive Control measures of performance, task fMRI and resting state connectivity (and reliability retest 4–12 weeks later) were used to compare those with future recurrence of MDD (N = 21) relative to those without future recurrence of MDD (N = 34 with resilience). The measures tested were (1) Parametric Go/No-Go (PGNG) performance, and task activation for (2) PGNG Correct Rejections, (3) PGNG Commission errors, and (4 & 5), resting state connectivity analyses of Cognitive Control Network to and from subgenual anterior cingulate.

Results

Relative to other groups at baseline, the group with MDD Recurrence had less bilateral middle frontal gyrus activation during commission errors. MDD Recurrence exhibited greater connectivity of right middle frontal gyrus to subgenual anterior cingulate (SGAC). SGAC connectivity was also elevated in this group to numerous regions in the Cognitive Control Network. Moderate to strong ICCs were present from test to retest, and highest for rs-fMRI markers. There were modest, significant correlations between task, connectivity and behavioral markers that distinguished between groups.

Conclusion

Markers of Cognitive Control function could identify those with early course MD who are at risk for depression recurrence. Those at high risk for recurrence would benefit from maintenance or preventative treatments. Future studies could test and validate these markers as potential predictors, accounting for sample selection and bias in feature detection.

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Tools are needed to increase identification of MDD recurrence

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Cognitive control behavior and depression symptoms have been predictive of recurrence in prior studies, but with low accuracy

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In remitted Major Depressive Disorder, those who will go on to have future depressive episodes differed in cognitive control activation and connectivity

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Symptoms, performance, task activation, and seed-based connectivity can contribute to identification of risk for recurrence

Risk factors for relapse and recurrence of depression in adults and how they operate: A four-phase systematic review and meta-synthesis

PURPOSE

To review and synthesise prognostic indices that predict subsequent risk, prescriptive indices that moderate treatment response, and mechanisms that underlie each with respect to relapse and recurrence of depression in adults.

RESULTS AND CONCLUSIONS

Childhood maltreatment, post-treatment residual symptoms, and a history of recurrence emerged as strong prognostic indicators of risk and each could be used prescriptively to indicate who benefits most from continued or prophylactic treatment. Targeting prognostic indices or their "down-stream" consequences will be particularly beneficial because each is either a cause or a consequence of the causal mechanisms underlying risk of recurrence. The cognitive and neural mechanisms that underlie the prognostic indices are likely addressed by the effects of treatments that are moderated by the prescriptive factors. For example, psychosocial interventions that target the consequences of childhood maltreatment, extending pharmacotherapy or adapting psychological therapies to deal with residual symptoms, or using cognitive or mindfulness-based therapies for those with prior histories of recurrence. Future research that focuses on understanding causal pathways that link childhood maltreatment, or cognitive diatheses, to dysfunction in the neocortical and limbic pathways that process affective information and facilitate cognitive control, might result in more enduring effects of treatments for depression.

Predictors of recurrence in remitted late-life depression

BACKGROUND

Late-life depression (LLD) is associated with a fragile antidepressant response and high recurrence risk. This study examined what measures predict recurrence in remitted LLD.

METHODS

Individuals of age 60 years or older with a Diagnostic and Statistical Manual - IV (DSM-IV) diagnosis of major depressive disorder were enrolled in the neurocognitive outcomes of depression in the elderly study. Participants received manualized antidepressant treatment and were followed longitudinally for an average of 5 years. Study analyses included participants who remitted. Measures included demographic and clinical measures, medical comorbidity, disability, life stress, social support, and neuropsychological testing. A subset underwent structural magnetic resonance imaging (MRI).

RESULTS

Of 241 remitted elders, approximately over 4 years, 137 (56.8%) experienced recurrence and 104 (43.2%) maintained remission. In the final model, greater recurrence risk was associated with female sex (hazard ratio [HR] = 1.536; confidence interval [CI] = 1.027-2.297), younger age of onset (HR = 0.990; CI = 0.981-0.999), higher perceived stress (HR = 1.121; CI = 1.022-1.229), disability (HR = 1.060; CI = 1.005-1.119), and less support with activities (HR = 0.885; CI = 0.812-0.963). Recurrence risk was also associated with higher Montgomery-Asberg Depression Rating Scale (MADRS) scores prior to censoring (HR = 1.081; CI = 1.033-1.131) and baseline symptoms of suicidal thoughts by MADRS (HR = 1.175; CI = 1.002-1.377) and sadness by Center for Epidemiologic Studies-Depression (HR = 1.302; CI, 1.080-1.569). Sex, age of onset, and suicidal thoughts were no longer associated with recurrence in a model incorporating report of multiple prior episodes (HR = 2.107; CI = 1.252-3.548). Neither neuropsychological test performance nor MRI measures of aging pathology were associated with recurrence.

CONCLUSIONS

Over half of the depressed elders who remitted experienced recurrence, mostly within 2 years. Multiple clinical and environmental measures predict recurrence risk. Work is needed to develop instruments that stratify risk.

Rapid-Acting Antidepressants: Mechanistic Insights and Future Directions

PURPOSE OF REVIEW

Ketamine produces rapid (within hours) antidepressant actions, even in patients considered treatment resistant, and even shows promise for suicidal ideation. Here, we review current research on the molecular and cellular mechanisms of ketamine and other novel rapid-acting antidepressants, and briefly explore gender differences in the pathophysiology and treatment of MDD.

RECENT FINDINGS

Ketamine, an NMDA receptor antagonist, increases BDNF release and synaptic connectivity, opposing the deficits caused by chronic stress and depression. Efforts are focused on the development of novel rapid agents that produce similar synaptic and rapid antidepressant actions, but without the side effects of ketamine. The impact of gender on the response to ketamine and other rapid-acting antidepressants is in early stages of investigation.

SUMMARY

The discovery that ketamine produces rapid therapeutic actions for depression and suicidal ideation represents a major breakthrough and much needed alternative to currently available medications. However, novel fast acting agents with fewer side effects are needed, as well as elucidation of the efficacy of these rapid-acting antidepressants for depression in women.

Psilocybin with psychological support for treatment-resistant depression: six-month follow-up

RATIONALE

Recent clinical trials are reporting marked improvements in mental health outcomes with psychedelic drug-assisted psychotherapy.

OBJECTIVES

Here, we report on safety and efficacy outcomes for up to 6 months in an open-label trial of psilocybin for treatment-resistant depression.

METHODS

Twenty patients (six females) with (mostly) severe, unipolar, treatment-resistant major depression received two oral doses of psilocybin (10 and 25 mg, 7 days apart) in a supportive setting. Depressive symptoms were assessed from 1 week to 6 months post-treatment, with the self-rated QIDS-SR16 as the primary outcome measure.

RESULTS

Treatment was generally well tolerated. Relative to baseline, marked reductions in depressive symptoms were observed for the first 5 weeks post-treatment (Cohen's d = 2.2 at week 1 and 2.3 at week 5, both p < 0.001); nine and four patients met the criteria for response and remission at week 5. Results remained positive at 3 and 6 months (Cohen's d = 1.5 and 1.4, respectively, both p < 0.001). No patients sought conventional antidepressant treatment within 5 weeks of psilocybin. Reductions in depressive symptoms at 5 weeks were predicted by the quality of the acute psychedelic experience.

CONCLUSIONS

Although limited conclusions can be drawn about treatment efficacy from open-label trials, tolerability was good, effect sizes large and symptom improvements appeared rapidly after just two psilocybin treatment sessions and remained significant 6 months post-treatment in a treatment-resistant cohort. Psilocybin represents a promising paradigm for unresponsive depression that warrants further research in double-blind randomised control trials.