Increased impulsivity as a vulnerability marker for bipolar disorder: Evidence from self-report and experimental measures in two high-risk populations

Abstract Study Objectives Nurses are a group at high risk for nightmares, yet little is known about the rate of nightmare disorder and associated psychosocial factors in this group in part attributable to the lack of a self-report questionnaire to assess DSM-5 criteria for nightmare disorder. Aims of the current study were to (1) report on development and initial validity of a self-report measure of DSM-5 nightmare disorder, and (2) examine the rate and associated factors of nightmare disorder among nurses. Methods Nurses (N = 460) completed baseline measures online including Nightmare Disorder Index (NDI), psychosocial and demographic questionnaires. A subset (n = 400) completed 14 days of sleep diaries and actigraphy. Results NDI demonstrated satisfactory psychometric characteristics as indicated by good internal consistency (α = 0.80), medium inter-item correlations (r = 0.50), medium to large item-total (r = 0.55–0.85) and convergent correlations (0.32–0.45), and small to medium discriminant correlations (–0.12–0.33). Per NDI, 48.7% of nurses reported no nightmares in the past month, 43.9% met partial/subthreshold criteria and 7.4% met full criteria for probable nightmare disorder. Nurses with nightmare disorder demonstrated significantly poorer psychosocial functioning (i.e. posttraumatic stress, depression, anxiety, stress) than those with subthreshold nightmare symptoms, who had poorer functioning than those with no nightmares. Conclusions NDI is an efficient and valid self-report assessment of nightmare disorder. Nurses have high rates of nightmares and nightmare disorder which are associated with poorer psychosocial functioning. We recommend increased nightmare screening particularly for high-risk populations such as healthcare workers.

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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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Risk and protective factors for drug dependence in two Moroccan high-risk male populations

PeerJ ◽

10.7717/peerj.5930 ◽

2018 ◽

Vol 6 ◽

pp. e5930

Author(s):

Anis Sfendla ◽

Dina Lemrani ◽

Britt Hedman Ahlström ◽

Meftaha Senhaji ◽

Nóra Kerekes

Keyword(s):

Mental Health ◽

High Risk ◽

Protective Factors ◽

Drug Dependence ◽

Mental Health Problems ◽

Significant Risk ◽

Significant Risk Factor ◽

Self Report ◽

High Risk Populations ◽

Multivariable Regression

Background Substance use is linked to biological, environmental, and social factors. This study provides insights on protective and risk factors for drug dependence in two Moroccan, high-risk, male samples. Methods Data from the “Mental and Somatic Health without borders” (MeSHe) survey were utilized in the present study. The MeSHe survey assesses somatic and mental health parameters by self-report from prison inmates (n = 177) and outpatients from an addiction institution (n = 54). The “Drug dependence” and the “No drug dependence” groups were identified based on the Arabic version of the Drug Use Disorder Identification Test’s (DUDIT) validated cutoff for identifying individuals with drug dependence, specifically in Morocco. Results The majority of participants who had at least high school competence (67.6%), were living in a partnership (53.7%), were a parent (43.1%), and/or had a job (86.8%) belonged to the “No drug dependence” group, while the presence of mental health problems was typical among the “Drug dependence” group (47.4%). A multivariable regression model (χ2 (df = 5, N = 156) = 63.90, p < 0.001) revealed that the presence of depression diagnosis remains a significant risk factor, while a higher level of education, having a child, and being employed are protective factors from drug dependence. Discussion Findings support the importance of increasing academic competence and treating depression as prevention from the persistence of drug addiction in male high-risk populations.

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