Annals of Clinical Psychiatry最新文献

Background: Limited research has investigated parent-child conflict and mental health among adult children of parents with hoarding problems.

Methods: Four hundred fourteen participants who reported clinically significant parental hoarding completed assessments of parental hoarding characteristics (clutter, insight, difficulty discarding), feelings of rejection towards their parent, depression, and generalized anxiety. These latter 3 variables were retrospectively rated across childhood (age 0 to 12), adolescence (age 13 to 20), young adulthood (age 21 to 29), and adulthood (age ≥30 years). Path analyses assessed mediated relationships.

Results: More than one-half of respondents endorsed clinically significant generalized anxiety, and more than one-third endorsed clinically significant depressive symptoms across ages, with highest rates during adolescence. Parental insight was related to rejection across ages, and clutter was related to rejection from adolescence through adulthood. Rejection was significantly positively related to depressive symptoms and generalized anxiety in childhood and adolescence and to depressive symptoms in young adulthood. Poor insight was significantly indirectly related to depressive symptoms through rejection across childhood and adolescence and to generalized anxiety in childhood.

Conclusions: Results suggest that parental hoarding may be a risk factor for anxiety and depression. Feelings of rejection towards parents may account for the link between parental hoarding and psychological distress, particularly between poor insight and depressive symptoms.

Background: Mood disorders often are diagnosed by clinical interview, yet many cases are missed or misdiagnosed. Mood disorders increase the risk of suicide, making it imperative to diagnose and treat these disorders quickly. Artificial intelligence (AI) has been investigated for diagnosing mood disorders, but the merits of the literature have not been evaluated. This systematic review aims to understand and explain AI methods and evaluate their use in augmenting clinical diagnosis of mood disorders as well as identifying individuals at increased suicide risk.

Methods: We conducted a systematic literature review of all studies until August 1, 2020 examining the efficacy of different AI techniques for diagnosing mood disorders and identifying individuals at increased suicide risk because of a mood disorder.

Results: Our literature search generated 13 studies (10 of mood disorders and 3 describing suicide risk) where AI techniques were used. Machine learning and artificial neural networks were most commonly used; both showed merit in helping to diagnose mood disorders and assess suicide risk.

Conclusions: The data shows that AI methods have merit in improving the diagnosis of mood disorders as well as identifying suicide risk. More research is needed for bipolar disorder because only 2 studies explored this condition, and it is often misdiagnosed. Although only a few AI techniques are discussed in detail in this review, there are many more that can be employed, and should be evaluated in future studies.

Background: Anxiety disorders in youth are frequently underdiagnosed and untreated, partly due to a lack of screening in primary care. The Generalized Anxiety Disorder 7-item (GAD-7) scale is a brief self-report measure designed to screen for anxiety in primary care settings. However, little is known about the psychometrics of this scale with adolescents.

Methods: Participants included 579 youth age 11 to 17 years who received screening for depression in a primary care setting through a web-based application, VitalSign⁶, over a 4-year period. Psychometric analyses were completed based on classical test theory (CTT) and item response theory (IRT).

Results: Using CTT and IRT methods, the GAD-7 has a unidimensional structure with good psychometric properties. In addition, the IRT analysis demonstrates that items 1 and 2 are strongly associated with the total score, and thus are good choices as a 2-item screening tool. Convergent validity was demonstrated, with high correlations between the GAD-7 and other measures of anxiety, and discriminant validity was also demonstrated, with low correlations to measures of other psychological states.

Conclusions: This psychometric evaluation of the GAD-7 provides support for the utility of this measure with adolescents. The GAD-2 is a good estimate of GAD-7 total score.

Background: The aims of this study were to evaluate the characteristics of patients and the pattern and rate of use of deep brain stimulation (DBS) for major depressive disorder (MDD) in the United States.

Methods: Data from the 2012-2014 Nationwide Inpatient Sample (NIS) included 116,890 patients. Patient variables included age, gender, race, median household income, insurance, primary diagnosis, primary procedure, length of stay, and total cost. Hospital variables included ownership, location, teaching status, bed size, and geographic region.

Results: Patients who received DBS for MDD were primarily high- income White females with private insurance. The mean age was 49.1 years (SD 7.85). The length of inpatient stay was 1 to 1.6 days. Total cost was highest in the West and lowest in the Northeast. Deep brain stimulation was mostly used by private nonprofit urban teaching hospitals in the South region of the United States.

Conclusions: Deep brain stimulation was used in .03% of the total inpatient population with a primary diagnosis of MDD. If efficacy is established in definitive trials, DBS could fill a need for patients with treatment-resistant depression who do not respond to standard therapeutics or electro-convulsive therapy.