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Aims:
We aimed to estimated the network structures of depressive symptoms using network analysis and evaluated the geographical regional differences in theses network structures among Asian patients with depressive disorders.
Methods:
Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD), the netw...
Contexts in source publication
Context 1
... shown in Table 1, according to the ICD-10, 8 the operational diagnostic criteria for depressive episode consists of (i) the typical symptoms, including depressed symptoms, loss of interest, and reduced energy; and (ii) the other common symptoms, including reduced concentration and attention, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, bleak and pessimistic views of the future, ideas or acts of self-harm or suicide, disturbed sleep, and disturbed appetite. The severity of depressive episodes varies with the number and severity of depressive symptoms. ...Context 2
... to the DSM-5, 9 the operational diagnosis of MDD consists of: (i) the presence of at least one of the core symptoms of depressed mood and loss of interest or pleasure; and (ii) the presence of five or more of the core symptoms and the other depressive symptoms, including weight loss or gain, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue or loss of energy, feelings of worthlessness or excessive guilt, diminished ability to concentrate or indecisiveness, and recurrent thoughts of death or recurrent suicidal ideation. As shown in Table 1, the main differences in the ICD-10 criteria for depressive episodes and DSM-5 criteria for MDD are as follows: First, the DSM-5 criteria for depressive mood cover the two symptoms of depressive mood and bleak and pessimistic views for the future included in the ICD-10 criteria. Hopelessness, which can be partly consistent with bleak and pessimistic views in the ICD-10 criteria, has been newly added as a subjective descriptor to depressive moods in the revision from DSM-IV to DSM-5. 10 Second, in the ICD-10 criteria, low self-esteem, low self-reproach, suicidality, and vegetative symptoms are regarded as better indicators for severity than other symptoms, whereas all symptoms are equally treated in the DSM-5 criteria. ...Citations
... In the area of mental health, schizophrenia (SZ) and major depressive disorder (MDD) are major mental disorders, and international treatment guidelines have been published by the American Psychiatric Association 6 and the National Institute for Health and facilities due to a lack of sufficient practice and because treatment is not standardized. [10][11][12][13] However, no strategy for disseminating and adopting practice guidelines has been established to date. 14 To bridge this evidence-practice gap, it is necessary to disseminate, educate, and validate psychiatric clinical practice guidelines; thus, the Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project was launched in 2016. ...
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 is predominantly based on a bottomup approach, whereas factor analysis using the structural equation model is predominantly based on a top-down approach. 8,9 Herein, with the help of further network analysis for the seven items of the CWS, Uygur et al 6 proposed a new perspective on COVID-19 worry, which is distinct from the results of the structural equation model. ...
... The patients were enrolled using a convenience sampling method from 39 research centers in 10 Asian countries and special administrative regions (SARs): namely, mainland China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, Singapore, Taiwan, and Thailand. This study has been described in detail elsewhere [42][43][44]. In addition, using the United Nations classification, the 10 Asian countries and SARs were geographically divided into eastern Asia (mainland China, Hong Kong, Japan, Korea, and Taiwan), southern Asia (India), and southeast Asia (Indonesia, Malaysia, Singapore, and Thailand). ...
... In addition, using the United Nations classification, the 10 Asian countries and SARs were geographically divided into eastern Asia (mainland China, Hong Kong, Japan, Korea, and Taiwan), southern Asia (India), and southeast Asia (Indonesia, Malaysia, Singapore, and Thailand). Furthermore, using the World Bank list of economies, the countries and SARs were economically categorized as high-income (Hong Kong, Japan, Korea, Singapore, and Taiwan), upper-middle (China, Malaysia, and Thailand), and lower-middle income (India and Indonesia) and SARs [42][43][44]. The REAP-AD was approved by the Institutional Review Board of Taipei City Hospital, Taipei, Taiwan (receipt number: TCHIRB-1020206-E), and other participating centers. ...
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.
... In another study on the network structure of major depressive disorder (MDD) in European older adults [25], central depressive symptoms included depressed moods, sleep difficulties, guilt and loss of interest or pleasure had fundamental roles in the network model. Further, several studies explored the network structure of depressive symptoms among other populations, such as adolescents [26,27], college students [28], patients with other psychiatric disorders [29][30][31], patients with cancer and the general population [32][33][34]. ...
Background: The 2019 novel coronavirus disease (COVID-19) outbreak had a detrimental impact on the mental health of older adults. This study evaluated the central symptoms and their associations in the network of depressive symptoms and compared the network structure differences between male and female older adults in Hong Kong.
Methods: Altogether, 3,946 older adults participated in this study. We evaluated the centrality indicators for network robustness using stability and accuracy tests, and examined the potential differences between the structure and connectivity of depression networks in male and female older adults.
Results: The overall prevalence of depressive symptoms was 43.7% (95% CI=40.6-46.7%) in males, and 54.8% (95% CI=53.1-56.5%) in females (P<0.05). Sad Mood, Guilt, Motor problems and Lack of Energy were influential symptoms in the network model. Gender differences were found in the network global strength, especially in the following edges: Sad Mood--Guilt, Concentration--Guilt, Anhedonia--Motor, Lack of Energy--Suicide, Appetite--Suicide and Concentration--Suicide.
Conclusions: Central symptoms in the depressive symptom network among male and female older adults may be prioritized in the treatment and prevention of depression during the pandemic.
... Previous studies have indicated that there could be a large gap between the development of evidence-based guidelines and their implementation in clinical settings, 18,19,[21][22][23] and that a combination of several guideline dissemination and implementation strategies aimed at healthcare professionals has failed to reduce antipsychotic polypharmacy for schizophrenia out-patients. 24 The pathway from evidence to guidelines is highly developed, but the development of guideline implementation strategies has been insufficient and examined in only a few studies. ...
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.
... Inevitably, the concept that depression is characterized by symptomatic heterogeneity, such as atypical [5], melancholic [6], and anxious [7] subtypes, has gained considerable attention. In addition, it has been reported that the heterogeneity of depressive syndrome can theoretically result from the polythetic and operational criteria of major depression [8][9][10][11][12]. According to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) [13], a confirmed diagnosis of major depressive disorder requires both the presence of five or more symptoms among, nine symptoms, including depressed mood, diminished interest or pleasure, weight loss or gain, insomnia or hypersomnia, psychomotor retardation or agitation, fatigue or loss of energy, feelings of worthlessness or excessive guilt, diminished thinking ability or indecisiveness, recurrent thoughts of death or recurrent suicidal ideation, and the presence of either depressed mood or diminished interest or pleasure. ...
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.
... Generally, there are several situations and conditions that stimulate stress to vary degrees in humans [1]. However, anxiety and depression are principal reaction conditions that induce stress and stress disorders or complications [2]. ...
... ∆E (MM-PBSA) = ∆E MM + ∆G solvation(1) ...
Background: 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. Objectives: This study aimed to evaluate the effects of quercetin (QUR) and/or ascorbic acid (AA) on Phenobarbital-induced sleeping mice. Methods: 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. Results: 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 GABAA and GABAB receptors. Conclusions: Both QUR and AA produced a stimulatory-like effect on mice, possibly through the GABAA and GABAB receptor interaction pathways. Further studies are necessary to verify this activity and clarify the exact mechanism of action(s) involved.
... This result is not entirely consistent with the typical symptoms of depressive episodes as defined by the tenth edition of the International Statistical Classification of Diseases and Related Health Problems, namely depressed mood, loss of interest, and reduced energy. 25 Thus, using data from the CRESCEND study, 7 this study aimed to estimate the serial changes in the centralities and network structures of the items of the 17-item Hamilton Depression Rating Scale (HAMD) 26,27 in a large sample of South Korean patients with DSM-defined depressive disorders. ...
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.
