Active clinical trials for "Depressive Disorder"

This study seeks to learn about brain function in adolescents with depression and to determine whether adding lithium carbonate to antidepressant medication can reduce depression in children and adolescents. Functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) will examine brain chemistry and function.

Studies have shown that admission to hospital during a coronavirus epidemic is associated with increased levels of anxiety, depression and panic disorder. During the SARS-CoV-2 pandemic in North London the Royal Free Hospital admitted over 500 patients with Covid-19. As part of the standard of care, these patients are screened at 8 weeks post discharge for signs of anxiety and depression. The Feeling Good app is a NHS approved digital application which utilises applied relaxation, mindfulness based cognitive therapy and positive visualisation through audio tracks for the treatment of anxiety and depression. This is a naturalistic cohort study aimed to track the post illness psychological symptoms of those who have been admitted with Covid-19 to the Royal Free hospital up to 5-7 months after discharge. The study population is those who are exhibiting anxiety or depressive symptoms as measure by the PHQ-2 or TSQ questionnaires. All those with symptoms will be offered free access to a NHS approved app for anxiety and depression, and followed up for 3 months after recruitment to track changes to their symptoms.

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.

Major Depression (MD) is highly prevalent and has associated a high burden and economic costs. Mild levels of MD could be treated without antidepressants at Primary Care (PC). Main objectives: 1) To calculate the cost-effectiveness of active monitoring (recommended by NICE) vs pharmacological antidepressant treatment to treat mild MD at PC level. Methods: 300 patients (≥18 years) with MD (diagnosed by the GP) will be recruited at the PC center. Depending on the level of symptoms, the GP will choose between: A) Active Monitoring (n=150) and B) pharmacological treatment (n=150). Patients will be followed-up for one year and data will be collected at baseline, 6 and 12 months. Severity will be assessed by Patient Health Questionnaire (PHQ-9), quality of life with the EuroQoL-5D (5 health dimensions), and the use of services with an adapted version of the Client Service Receipt Inventory (including lost productivity). Cost-effectiveness and cost-utility analysis will be calculated and 5000 bootstrapping replications will be conducted to asses uncertainty. Cost-acceptability curves will be done using two perspectives: the National Health Service perspective and the Societal perspective. The Propensity Score technique will minimize the absence of randomization, matching cases from both treatment options.

This study is investigating whether acute administration of citalopram is associated with a decrease in stress reactivity in healthy volunteers, compared to placebo administration. Using a parallel-group double-blind design, participants will be randomised to receive either an acute dose of citalopram or placebo. All participants will have come in for a screening visit. On the day of the research visit (following drug administration) participants will have completed a number of widely used computer-based cognitive tasks measuring emotional processing biases. They will then complete the Oxford Cognition Stress Task, a web-based acute stress induction paradigm, which is designed to induce mild transient increases in stress and arousal. Identifying early changes in stress reactivity following antidepressant treatment will increase the investigator's knowledge of how antidepressants operate, and provide putative targets to identify early response to antidepressants.

Major Depressive Disorder (MDD) is a common and debilitating illness. It affects a person's family and personal relationships, work, education, and life. It changes sleeping and eating habits and significantly impairs patients' general health. The disorder affects Veterans more than the general population, both as an isolated illness and in conjunction with posttraumatic stress disorder (PTSD) and suicidality. Symptoms in a notable proportion of patients (~30%) do not respond to behavioral and pharmacological interventions, and new treatments are in great need. One such treatment, transcranial magnetic stimulation (TMS), has been cleared by Food and Drug Administration for treatment in MDD. TMS is effective in around 60% of patients with treatment-resistant MDD but is associated with significant financial and time burden. Further insights into the neurobiological effects of TMS and markers for functional recovery prediction and treatment progression are of great value. The goal of this proposal is to use human electrophysiology (electroencephalography, hereafter EEG, in particular) and machine learning to predict treatment response in candidates for TMS treatment and also study TMS's mechanism of action. Doing so has several benefits for patients, as prediction of treatment helps providers in screening out the patients for whom TMS is ineffective and understanding the mechanism allows us to refine and individualize the treatment. The investigators will recruit 35 patients with treatment-resistant MDD and record resting state EEG signal with a dense electrode array before and after a 6-week clinical course of TMS treatment. The investigators will use machine learning (Sparse regressions) to predict treatment outcome using functional connectivity (Coherence) maps derived from the EEG signal. The investigators also will use classifiers to track changes in functional connectivity through the course of treatment. Based on our preliminary data, the investigators hypothesize that weaker functional connectivity between prefrontal cortex (where the stimulation is delivered) and parietal/posterior midline sites predict better response to treatment and that TMS treatment will enhance these connections. The data collected here would be used as a seed and preliminary data for future federal (NIH and the VA) career development awards which will focus on the use of EEG to better understand brain function and neuromodulation treatments.

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.

Our aim was to diagnose and initiate early treatment for postpartum depression by detecting the changes of melatonin levels in c-sections with different antesthesia modalities.

This study evaluates the effect of the addition of ketamine to a conscious sedation protocol including remifentanil during oocyte retrieval. The investigators will have 2 groups with different target effect site concentrations, namely 150 ng/ml and 200 ng/ml.