Active clinical trials for "Bipolar Disorder"

The objective of this retrospective observational study is to compare commonly prescribed bipolar disorder medications for their impact on: (1) hospitalization; (2) suicide attempts and self-harm; and (3) risk of drug-induced adverse effects such as kidney disease and diabetes mellitus. In addition, the investigators will examine heterogeneity of treatment effect by co-morbidity within pediatric, adult, and elderly sub-populations. Patient focus groups are convened to elicit additional questions and provide feedback on results.

Through a recent cross species translational experiment, researchers have identified a set of epigenetic marks capable of predicting postpartum depression with greater than 85% accuracy. The researchers are looking to identify a group of women from both the general population and those with a history of mood disorders who are at risk for postpartum depression and obtain brain imaging data at a postpartum time period prior to the onset of depressive symptoms and compare it with those obtained during depressive episodes. The researchers will also evaluate the efficacy of postpartum depression biomarker prediction.

The purpose of this research is to understand if it is helpful for patients with mental illness to be connected to a psychiatrist and case manager at the time of cancer diagnosis.

The purpose of this study is to evaluate the safety and effectiveness of quetiapine in actual clinical settings.

The current research project aims at investigating whether it is possible to obtain an increase in physical activity among inpatients with schizophrenia spectrum disorders by the use of motivational interviewing and environmental interventions, and whether interventions will be associated with an increase in physical fitness and/or improvement in mental health. The Norwegian health care system operates with a catchment-area based organization, which make high levels of representability of patients possible. Patients will be recruited from intermediate-long term inpatient departments where patients typically have schizophrenia spectrum disorders and are admitted for longer periods. There are thus unique possibilities for coordinated efforts to motivate for and change dysfunctional habits.

The widespread and common use of quetiapine in childbearing and pregnant women demands more data to inform dosing and toxicity in pregnancy. The new FDA Pregnancy and Lactation Labeling Final Rule (PLLR) will go into effect on June 30th, 2015 and will replace the prior A, B, C, D, and X categories. Additionally, the PLLR will require information to aid prescribing decisions in three categories 1) Pregnancy (including labor and delivery), 2) Lactation, and 3) Females and Males of Reproductive Potential. The pregnancy category will include a subsection that will describe pharmacokinetic and pharmacodynamic characteristics of the medication in pregnancy, fetal risk, and data quality. The data collected in this study will update the FDA pregnancy pharmacokinetic section for quetiapine and inform physicians that prescribe to childbearing women.

Background: Antipsychotics can induce metabolic disorders such as obesity, hyperglycemia, dyslipidemia or metabolic syndrome. It has been observed that treatment with antipsychotic could be accompanied by a decrease in the concentration of serum magnesium. Low serum concentrations of magnesium are potentially a risk factor of cardiac sudden death (Peacock, 2010). Hypotheses linking magnesium and pathogenesis of cardiovacuscular diseases are multiple. Also, it seems to exist a close relationship between magnesium and carbohydrate metabolism. Most studies on the subject have generally studied plasmatic magnesium. Objective : Describe the relationship between changes in serum and intra-erythrocyte magnesium and cardiometabolic risk in patients innitiating an antipsychotic treatment. A secondary objective is to specify the frequency, magnitude and time to onset of changes in plasma of magnesium levels under antipsychotic treatment. Methods : This is a pilot single-center prospective cohort. After inclusion, patients status (including magnesium levels) will be evaluated (1 and 3 months of treatment) and that status will define the exposure criterion. Included patients will be followed for 1 year during which cardiometabolic markers will be measured. Population : patients who are more than 18 years old with schizophrenia schizoaffective disorder or bipolar disorder, naive to antipsychotic treatment or off for more than 3 months and requiring the introduction of antipsychotic drug therapy. Patients will be recruited during consultations and stays in care units of Adult Psychiatry Unit of Montpellier University Hospital. Factor studied: serum and intra-erythrocytic magnesium levels at beginning and during the antipsychotic treatment measured by a unique analyzer center. Changes in levels of hypomagnesemia expected during the treatment will determine exposure groups. Outcome: cardiometabolic risk markers measured at the beginning and during the treatment will be fasting blood glucose, fasting plasma insulin, HOMA-IR [Ins (uU / mL) x Gly (mmol / L) / 22.5], lipid profile (total cholesterol, LDL, HDL), BMI, waist circumference and ECG (QTc). Cofactors: age, sex, personal and family medical history, blood pressure, smoking, diet, physical activity, psychiatric disease, Global Impressions, anti-psychotic treatment and comedications. Perspectives : to show that decreased in magnesium levels observed among patients starting antipsychotic treatment is associated with deterioration of cardiometabolic risk markers. The demonstration of this association could explain at least part the increased cardiovascular risk observed in this population. In the longer term, the results of this study would argue the implementation of an intervention research project studying magnesium supplementation to minimize the metabolic effects of antipsychotic medications.

The purpose of this study is to determine if a mobile sensing platform can passively and objectively detect the presence of clinically significant mood disorder symptomatology and symptom progression over time. Meeting this goal will allow for improved risk categorization, prediction of relapse, and measurement of disease progression in a lifetime prevalence population.

This study is a single-center, retrospective, cross-sectional study. We plan to work with our network information center to analysis the related indicators of oxidative stress injury in patients with bipolar disorder based on oxidative stress data. During the study, machine learning was used as a data analysis method to screen out the biomarker risk factors with sensitivity and specificity for early recognition of bipolar disorder from major depression disorder with oxidative stress injury as the core. And then build up effective clinical predictive models for early identification of bipolar disorder, which can predict the early quantitative probabilistic of the onset of bipolar disorder.

Involuntary mental health care is permitted because it is believed to make people with severe mental disorders (SMD) better and prevent them from getting worse or even dying In this study we will investigate whether low levels of coercion in an area is connected with poorer outcomes in Norway. It can be assumed that too little involuntary care might lead to the opposite outcomes to those intended by the Norwegian Mental Health Act. The same law applies all over Norway, but the rate of involuntary care varies: there is up to five-fold difference between the catchment areas of the 69 Community Mental Health Centers. The investigators will estimate rates of involuntary care and adjust for age, sex, urbanity and area deprivation. The data source is the Norwegian Patients Registry, and all patients in treatment for a severe mental disorder in 2015 and their use of mental health care until 2018 will be followed. Model 1 follows all patients who were treated for a severe mental disorder in 2015. The model will test whether the rates of involuntary care in the area they live can predict the length of time to death. Model 2 follows patients with treatment for severe mental disorders that had no episode of voluntary care in 2015. The model will test whether the rate of involuntary care in their area predicts their use of mental health inpatient care in 2016 and 2017. Model 3 tests how long time patients with severe mental disorders that received only voluntary care in 2015 remain without a period of involuntary care in 2016-17, as a function of the rate of involuntary care in their area. Model 4 estimates changing the total number of patients with severe mental disorders in the catchment area in 2016-17 as a function of time and the rate of involuntary in 2015. Model 5 tests whether suicide rates for a catchment area varies as a function of its rate of involuntary care. Because suicides are rare, we will observe the variables over longer time periods, using involuntary care rates from 2015 to 2018 and suicide rates for 2015-2019. The study was evaluated by the Research Ethics Committee (ref 2018/795), who approved use of registry data, and by the Privacy Ombudsman at Akershus University Hospital (ref 2018-090).