Active clinical trials for "Depressive Disorder, Major"

To explore electrophysiologic parameters and biomarkers predicting treatment response of patients with major depressive disorder To explore electrophysiologic parameters and biomarkers predicting suicide risk of patients with major depressive disorder

Assessing FM and psychiatric state among PTSD, MDD and healthy participants

Assessing psychiatric and physiological co-morbidity in TRD and non-TRD patients

The purpose of this study is to identify genes involved in depression. Specifically, the investigators will analyze some genes that may be related to whether or not a person responds to antidepressant medication. This project is part of basic scientific research to increase understanding of the role of genetic influences in psychological and mental processes involved in the response to treatment for depression.

Major depressive disorder (MDD) affects around 7% of the population yearly. Although effective treatments are available, only around half of all patients participating in clinical trials respond to 6 to 12 weeks of antidepressant treatment. Given these high failure rates, the ability to predict as early as possible whether a patient is (un)likely to respond would be of great value, as it would enable physicians to change treatment strategies faster. Early improvement has consistently been found to be a strong predictor of later response. However, misclassification is still quite common, with perhaps a third of those who do not show early improvement going on to respond. Conversely, a substantial proportion of those who do show early improvement do not go on to respond. One possibility for improving the predictive power of early improvement is to examine individual symptoms, rather than the total score on a depression rating scale. Some items, for example, could reflect antidepressant side effects (e.g. gastrointestinal symptoms) and may not be very predictive. The proposed project aims to examine the relationship between early improvement in individual symptoms and response to antidepressants in a very large patient sample. This large sample size makes it possible to use more rigorous methods than previous studies, such as the use of cross-validation to confirm the findings. It also makes it possible to examine a large set of predictors, including possible interactions among early-improving symptoms and between symptoms and demographic factors like age and gender. The added value of individual symptoms over and above using the total symptom score alone will also be examined, as well as possible differences between different antidepressant classes. The project will use penalized (lasso) regression, which is well-suited to analyzing data with a large number of (potentially highly correlated) predictors. In the primary analysis, response after 6 weeks of treatment will be predicted. In secondary analyses, remission at week 6 and response and remission at week 12 will also be predicted.

Suicidal behavior (SB) is a public health problem. The clinical model currently admitted to the understanding of SB is a stress vulnerability model, but so far, all scientific works has no clinical application. The management of psychiatric patients, including depressed subjects, faces the inability to detect those with a high risk of SB. Many studies have shown a link between low cholesterol and SB. A study has recently proposed a total cholesterol threshold below which the risk of suicide could be increased. However, a prospective study is needed to assess the predictive nature of such an indicator.

The purpose of this observational antidepressant study is to determine the efficacy of vortioxetine on depression and cognitive function, and elucidate its potential effects on quality of life in patients with cancer (of any origin). We hypothesise that given its unique mechanism of action as a multimodal serotonin modulator, vortioxetine is set to achieve the above goals while maintaining a favourable side effect profile.

Research has shown that people with depressive symptoms maintain negative expectations even if they have positive experiences that contradict their expectations. Healthy people, however, change their expectations after unexpected positive experiences. In this experimental study, it will now be examined whether there are also differences between healthy people and people with depressive symptoms in dealing with unexpected negative experiences.

Background: MDD is a common mental disorder with significant morbidities and mortalities. Recent local data suggested that depressive disorders have a prevalence of over 12% in females and nearly 7 % in males in Hong Kong general adult population. Other than insomnia, patients with MDD often complained another sleep symptom - hypersomnia (defined as daytime sleepiness or excessive sleep). Interestingly, when compared to insomnia, there is much far less research on the role of hypersomnia in MDD. However, there are available data suggested that hypersomnia is associated with greater treatment-resistance, more recurrence, and increased suicidality, suggesting a need to investigate this problem in MDD patients. Objective: To investigate the prevalence and determine characteristics of hypersomnia amongst major depressive disorder. Design: 2-phase study design Setting: A case-control study Participants: Patients with a history of Major Depressive Disorder from out-patient clinics in New Territories East Cluster. Main outcome measures: Daytime sleepiness measured by MSLT, actigraphy and self-reported questionnaire (ESS), sleep duration as measured by sleep diary and actigraphy.

Current methods of choosing treatment for major depressive disorder (MDD) are inefficient. The Strategic Treatment to Achieve Remission of Depression (STAR*D) Trial revealed that only about 1/3 of patients treated with antidepressant drugs will go into remission with the first medication chosen. We hypothesize that pattern recognition software using Machine Learning methods can accurately predict response to a variety of antidepressant medications (ADM) or cognitive behavior therapy (CBT) after training using pre-treatment demographic, clinical, laboratory or electroencephalographic (EEG) data. These algorithms might assist the clinician to chose, for any given patient, an antidepressant treatment option with greater probability of favourable response than is achievable using current best practise methods.