Effects of antidepressant medicines on preventing relapse of unipolar depression: a pooled analysis of parametric survival curves

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review

Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.

Later is not necessarily better: limitations of survival analysis in studies of long-term drug treatment of psychiatric conditions

Survival analysis is routinely used to assess differences between groups in relapse prevention and treatment discontinuation studies involving people with long-term psychiatric conditions. The actual outcome in survival analysis is 'time to event', yet, in the mental health field, there has been little consideration of whether a temporary delay to relapse is clinically relevant in a condition that can last for decades. Moreover, in psychiatric drug trials, a pattern of elevated early relapses following randomisation to placebo or no treatment is common. This may be the result of the withdrawal of previous treatment leading to physiological withdrawal effects, which may be mistaken for relapse, or genuine relapse precipitated by the process of withdrawal. Such withdrawal effects typically produce converging survival curves eventually. They inevitably lead to differences in time to relapse, even when there is little or no difference in the cumulative risk of relapse at final follow-up. Therefore, statistical tests based on survival analyses can be misleading because they obscure these withdrawal effects. We illustrate these difficulties in a trial of antipsychotic reduction versus maintenance, and a trial of prophylactic esketamine in people with treatment-resistant depression. Both illustrate withdrawal-related effects that underline the importance of long-term follow-up and question the use of tests based on time to event. Further discussion of the most relevant outcome and appropriate approach to analysis, and research on patient and carer preferences is important to inform the design of future trials and interpretation of existing ones.

Remission of symptoms is not equal to functional recovery: Psychosocial functioning impairment in major depression

The ultimate goal of depression treatment is to achieve functional recovery. Psychosocial functioning is the main component of functional impairment in depressed patients. The concept of psychosocial functioning has an early origin; however, its concept and connotation are still ambiguous, which is the basic and key problem faced by the relevant research and clinical application. In this study, we start from the paradox of symptoms remission and functional recovery, describe the concept, connotation, and characteristics of psychosocial functioning impairment in depressed patients, and re-emphasize its importance in depression treatment to promote research and clinical applications related to psychosocial functioning impairment in depressed patients to achieve functional recovery.

Prevalence and risk factors for mental health symptoms in community epidemic prevention workers during the postpandemic era of COVID-19 in China

Few people have paid attention to community epidemic prevention workers in the postpandemic era of COVID-19. This study aimed to explore the prevalence and risk factors for mental health symptoms in community epidemic prevention workers during the postpandemic era. Mental health status was evaluated by the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Chinese Perceived Stress Scale, Insomnia Severity Index, and Maslach Burnout Inventory-General Survey. The results showed that a considerable proportion of community epidemic prevention workers reported symptoms of depression (39.7%), anxiety (29.5%), high stress (51.1%), insomnia (30.8%), and burnout (53.3%). The prevalence of depression and anxiety in community epidemic prevention workers was higher than in community residents. Among community epidemic prevention workers, short sleep duration was a risk factor for depression, anxiety, high stress and insomnia. Concurrent engagement in work unrelated to epidemic prevention and current use of hypnotics were risk factors for depression, anxiety and insomnia. Our study suggests that during the postpandemic era, the mental health problems of community epidemic prevention workers are more serious than those of community residents. Several variables, such as short sleep duration and concurrent engagement in work unrelated to epidemic prevention, are associated with mental health among community epidemic prevention workers.

Nonpharmacological interventions for relapse prevention in unipolar depression: A network meta-analysis

BACKGROUND

The development of prophylactic interventions for major depressive disorder (MDD) is an important issue in clinical practice. We aimed to compare the relative efficacy of nonpharmacological interventions for relapse prevention in adult patients with MDD.

METHODS

Randomized controlled trials investigating nonpharmachological interventions for relapse prevention were included. A Bayesian network meta-analysis was performed. Hazard ratios are reported as effect sizes with 95% credible intervals. Global inconsistency, local inconsistency, heterogeneity, and transitivity were evaluated. Confidence for the results comparing the active treatment with control conditions or antidepressant medicine (ADM) was assessed.

RESULTS

Thirty-six trials were included. Most nonpharmacological interventions were various forms of psychotherapy; others were noninvasive neurostimulation techniques (3 studies with electroconvulsive therapy and 1 study with transcranial magnetic stimulation). Psychotherapy as a monotherapy following ADM or psychotherapy produced significantly better outcomes than control conditions, and there was no significant difference between psychotherapy and ADM. The combination of psychotherapy and ADM was superior to either treatment alone. The results were similar for patients with at least 3 previous episodes. Neurostimulation techniques were also superior to controls, either as a monotherapy or combined with ADM.

CONCLUSIONS

Our study provided evidence that psychotherapy as a monotherapy following ADM or psychotherapy was effective and performed as well as ADM for relapse prevention. Neurostimulation techniques also showed promising results but more studies are needed to confirm their efficacy. These findings may be informative for clinical practice and inspire future research.