One-Year Outcome After Preconception Consultation in Women With Bipolar Disorder

Background Bipolar disorder is a mental illness in which manic and depressive states are repeated, causing psychosocial dysfunction. Manic/hypomanic episodes cause problems with interpersonal, social and financial activities, but there is limited evidence regarding the predictors of manic/hypomanic episodes in real-world clinical practice. Methods The multicenter treatment survey on bipolar disorder (MUSUBI) in Japanese psychiatric clinics was administered in an observational study that was conducted to accumulate evidence regarding bipolar disorder in real-world clinical practice. Psychiatrists were asked to complete a questionnaire about patients with bipolar disorder who visited 176 member clinics of the Japanese Association of Neuro-Psychiatric Clinics by conducting a retrospective medical record survey. Our study extracted baseline patient characteristics from September to October 2016, including comorbidities, mental status, duration of treatment, Global Assessment of Functioning (GAF) score, and pharmacological treatment details. We investigated the presence or absence of manic/hypomanic episodes over the course of one year from baseline to September-October 2017. Results In total, 2231 participants were included in our study, 29.1% of whom had manic/hypomanic episodes over the course of one year from baseline. Binomial logistic regression analysis revealed that the presence of manic/hypomanic episodes was correlated with lower baseline GAF scores, rapid cycling, personality disorder, bipolar I disorder, and a mood state with manic or mixed features. Substance abuse was also a risk factor for manic episodes. There was no significant association between a baseline antidepressant prescription and manic/hypomanic episodes. Conclusions In Japan, 29.1% of outpatients with bipolar disorder had manic/hypomanic episodes over the course of one year. Our study suggested that a low GAF score, rapid cycling, personality disorder, bipolar I disorder, substance abuse, and baseline mood state could be predictors of manic/hypomanic episodes. Based on our findings, an antidepressant prescription is not a predictor of manic/hypomanic episodes.

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Perinatal Depression

10.2310/fm.19031 ◽

2018 ◽

Author(s):

Tiffany A. Moore Simas ◽

Bailey McGuiness ◽

Valerie Valant ◽

Nancy Byatt

Keyword(s):

Bipolar Disorder ◽

Selective Serotonin Reuptake Inhibitors ◽

Perinatal Depression ◽

Absolute Risk ◽

Psychiatric Evaluation ◽

Baby Blues ◽

One Year ◽

Effective Diagnosis ◽

Maternal Suicide ◽

In Pregnancy

Perinatal depression includes major and minor depression occurring in pregnancy and one year postpartum. Affecting one in seven women, it is one of the most common pregnancy complications; however, it is often under recognized and undertreated. A personal history of perinatal or non-perinatal depression significantly increases risk. Screening using a validated instrument is recommended in the context of systems to ensure effective diagnosis, treatment, and follow-up. Evidence-based treatment includes psychotherapy and pharmacotherapy. Selective serotonin reuptake inhibitors are well-studied in pregnancy, are associated with low overall absolute risk, and are differentially secreted into breast milk. If left untreated, perinatal depression is associated with significant short- and long-term negative maternal-child consequences including, among many others things, poor bonding. Of note, maternal suicide exceeds hemorrhage and hypertensive disorders as a cause of maternal mortality. It is critical to recognize that one in five women who screen positive for perinatal depression will have bipolar disorder and are at highest risk for postpartum psychosis, suicide, and infanticide, especially if prescribed unopposed anti-depressant monotherapy. Women who screen positive for having bipolar disorder should be referred for psychiatric evaluation. This review contains 6 figures, 13 tables and 54 references Keywords: Pregnancy, Postpartum, Perinatal, depression, Mood disorder, Baby blues, Bipolar disorder, Psychosis, Psychotherapy, Psychopharmacology

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Bipolar Disorder and Family Psychoeducational Treatment: A Comparison of One-Year Effects Using Repeated Measures Analysis of Variance and Random Regression Models

New Family Interventions and Associated Research in Psychiatric Disorders ◽

10.1007/978-3-7091-6148-7_11 ◽

2002 ◽

pp. 187-204

Author(s):

Jeffrey A. Richards ◽

David J. Miklowitz

Keyword(s):

Bipolar Disorder ◽

Analysis Of Variance ◽

Repeated Measures ◽

Regression Models ◽

Random Regression ◽

Repeated Measures Analysis ◽

Psychoeducational Treatment ◽

One Year

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One-year rehospitalization rates in bipolar disorder manic patients discharged on mood stabilizers versus mood stabilizer combined with antipsychotics

European Neuropsychopharmacology ◽

10.1016/s0924-977x(16)31394-3 ◽

2016 ◽

Vol 26 ◽

pp. S423 ◽

Cited By ~ 1

Author(s):

E. Hochman ◽

A. Krivoy ◽

A. Weizman ◽

A. Valevski

Keyword(s):

Bipolar Disorder ◽

Mood Stabilizer ◽

Mood Stabilizers ◽

One Year ◽

Manic Patients

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Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Brain Sciences ◽

10.3390/brainsci10110784 ◽

2020 ◽

Vol 10 (11) ◽

pp. 784

Author(s):

Peihao Fan ◽

Xiaojiang Guo ◽

Xiguang Qi ◽

Mallika Matharu ◽

Ravi Patel ◽

...

Keyword(s):

Bipolar Disorder ◽

High Risk ◽

Electronic Medical Records ◽

Medical Records ◽

Clinical Decision Making ◽

Mathematic Model ◽

Clinical Decision ◽

Predictive Values ◽

High Risk Populations ◽

One Year

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

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