Network analysis of the depressive symptom profiles in Asian patients with depressive disorders: Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD)

Satisfaction with web-based courses on clinical practice guidelines for psychiatrists: Findings from the "Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE)" project

To disseminate, educate, and validate psychiatric clinical practice guidelines, the Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project was launched in 2016. In this study, we investigated whether the web-based courses offered by this project would be as effective as the face-to-face courses. We analyzed and compared survey answers about overall participant satisfaction with the course and answers regarding clinical knowledge of schizophrenia and major depressive disorder between 170 participants who took the web-based courses in 2020 and 689 participants who took the face-to-face courses from 2016 to 2019. The web-based course participants completed the survey questions about satisfaction with the web-based courses. The web-based courses were conducted using a combination of web services to make it as similar as possible to the face-to-face courses. The degree of satisfaction assessed by the general evaluation of the web-based courses was higher than what was expected from the face-to-face courses. The degree of satisfaction was similar for the courses on schizophrenia and major depressive disorder. In addition, there were no significant differences in overall satisfaction and clinical knowledge between web-based and face-to-face courses. In conclusion, the web-based courses on clinical practice guidelines provided by the EGUIDE project were rated as more satisfying than the face-to-face course that the participants expected to take and no differences in the effectiveness of either course. The results suggest that, after the COVID-19 pandemic, it would be possible to disseminate this educational material more widely by adopting web-based courses additionally face-to-face courses.

Network analysis of psychiatric symptoms in schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP)

AIMS

Schizophrenia is a major mental disorder with a wide range of psychiatric symptoms. This study explored the structure of psychiatric symptoms of schizophrenia using network analysis in a large representative Asian sample based on a survey of clinical features and treatment used in schizophrenia patients across 15 countries/territories in Asia.

METHODS

Data on the demographic characteristics and psychiatric symptoms in schizophrenia patients were extracted from the dataset of the fourth Research on Asia Psychotropic Prescription for Antipsychotics (REAP-AP) project. The presence of the following psychiatric symptoms including delusions, hallucinations, disorganized speech, grossly disorganized or catatonic behavior, negative symptoms, social/occupational dysfunction, verbal aggression, physical aggression, and affective symptoms were analyzed.

RESULTS

A total of 3681 patients were included. The network analysis revealed that verbal aggression, hallucinations, and social/occupational dysfunction were the most central symptoms, while the connections between social/occupational dysfunction and verbal aggression, and between hallucinations and disorganized speech were the two strongest edges. There were significant gender differences in the network structure based on the network structure invariance test (M=0.74, P = 0.03) and invariant edge strength test. The positive correlation between verbal aggression and hallucinations was significantly stronger in the female network than that in the male network (P = 0.03), while a negative correlation between affective symptoms and negative symptoms was found in the female, but not the male network (P < 0.01).

CONCLUSION

Central symptoms including verbal aggression, hallucinations, and socio-occupational dysfunction should be addressed in developing targeted treatment strategy for schizophrenia patients.

A Machine-Learning-Algorithm-Based Prediction Model for Psychotic Symptoms in Patients with Depressive Disorder

Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897–0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the “severity psychosis” hypothesis.

A dissemination and education programme to improve the clinical behaviours of psychiatrists in accordance with treatment guidelines for schizophrenia and major depressive disorders: the Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project

BACKGROUND

Clinical practice guidelines for schizophrenia and major depressive disorder have been published. However, these have not had sufficient penetration in clinical settings. We developed the Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project as a dissemination and education programme for psychiatrists.

AIMS

The aim of this study is to assess the effectiveness of the EGUIDE project on the subjective clinical behaviour of psychiatrists in accordance with clinical practice guidelines before and 1 and 2 years after participation in the programmes.

METHOD

A total of 607 psychiatrists participated in this study during October 2016 and March 2019. They attended both 1-day educational programmes based on the clinical practice guidelines for schizophrenia and major depressive disorder, and answered web questionnaires about their clinical behaviours before and 1 and 2 years after attending the programmes. We evaluated the changes in clinical behaviours in accordance with the clinical practice guidelines between before and 2 years after the programme.

RESULTS

All of the scores for clinical behaviours in accordance with clinical practice guidelines were significantly improved after 1 and 2 years compared with before attending the programmes. There were no significant changes in any of the scores between 1 and 2 years after attending.

CONCLUSIONS

All clinical behaviours in accordance with clinical practice guidelines improved after attending the EGUIDE programme, and were maintained for at least 2 years. The EGUIDE project could contribute to improved guideline-based clinical behaviour among psychiatrists.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Quercetin and/or Ascorbic Acid Modulatory Effect on Phenobarbital-Induced Sleeping Mice Possibly through GABAA and GABAB Receptor Interaction Pathway

Depressive disorder is a recurrent illness that affects large numbers of the general population worldwide. In recent years, the goal of depression treatment has moved from symptomatic response to that of full remission. However, treatment-resistant depression is a major challenge in the treatment of depression or depression-related disorders. Consensus opinion, therefore, suggests that effective combined aggressive initial treatment is the most appropriate strategy. This study aimed to evaluate the effects of quercetin (QUR) and/or ascorbic acid (AA) on Phenobarbital-induced sleeping mice. QUR (50 mg/kg) and/or AA (25 mg/kg) with or without intraperitoneally pre-treated with GABA receptor agonist (diazepam: 2 mg/kg, i.p.) or antagonist (Flumazenil: 2.5 mg/kg, i.p.) to underscore the effects, as well as the possible involvement of the GABA receptor in the modulatory action of QUR and AA in sleeping mice. Additionally, an in silico study was undertaken to predict the involvement of GABA receptors in the sleep mechanism. Findings suggest that the pretreatment of QUR and AA modulated the onset and duration of action of the standard drugs in experimental animals. The acute administration of QUR and/or AA significantly (p < 0.05) reversed the DZP-mediated onset of action and slightly reversed the duration of sleep time in comparison to the vehicle (control) group. A further combination of QUR or AA with the FLU resulted in an enhancement of the onset of action while reducing the duration of action, suggesting a FLU-like effect on the test animals. In in silico studies, AA and QUR showed good to moderate binding affinities with GABA_A and GABA_B receptors. Both QUR and AA produced a stimulatory-like effect on mice, possibly through the GABA_A and GABA_B receptor interaction pathways. Further studies are necessary to verify this activity and clarify the exact mechanism of action(s) involved.

An alternative approach to future diagnostic standards for major depressive disorder

During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.

Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures

OBJECTIVE

Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.

METHODS

We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.

RESULTS

In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.

CONCLUSION

Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.