Acta Psychiatrica Scandinavica最新文献

Background: Reproductive health and mental health are intertwined, but studies investigating family planning needs and desire for children in mental healthcare are scarce.

Methods: We studied the experiences of (former) patients, those with close relationships with the (former) patients (close ones) and mental health professionals (MHP) on discussing family planning and desire for children in mental healthcare. We combined quantitative (two nationwide surveys) and qualitative data (four focus groups) in a mixed-methods approach with sequential analytical design.

Results: Combined data from focus groups (n = 19 participants) and two surveys (n = 139 MHPs and n = 294 (former) patients and close ones) showed that a considerable group of MHPs (64.0%), patients (40.9%) and close ones (50.0%) found that family planning should be discussed by a psychiatrist. However, several obstacles impeded a conversation, such as fear of judgment, lack of time and knowledge and limited opportunity for in-depth exploration of life themes in therapeutic relationships.

Conclusions: To increase the autonomy of patients in discussing family planning, we suggest MHPs explore the desire to discuss family planning with all patients in the reproductive phase of life, prior to discussing contraceptive care. MHPs should receive education about psychiatric vulnerability in relation to family planning and desire for children, and patients and close ones should be empowered to initiate a conversation themselves.

The study conducted by Tapio Paljärvi et al., titled “Cardiovascular mortality in bipolar disorder: population-based cohort study,” sheds crucial light on the alarming rates of cardiovascular disease mortality among individuals with bipolar disorder.¹ The findings underscore a significant excess in mortality due to various cardiovascular causes, including coronary artery disease, cardiomyopathy, and hypertensive heart disease. This issue is part of a larger problem for people with severe mental illnesses (SMI), such as schizophrenia, bipolar disorders, and major depressive disorders. Individuals with SMI die up to 15 years earlier than the general population, and around 70% of this mortality gap is due to physical disorders.² Increased risk of physical disorders in those with mental disorders is due to reduced access to adequate physical health monitoring, care and prevention,^3-5 increased risk of adverse health behaviors⁶ such as sedentary behavior, poor diet, and smoking, comorbid substance abuse, and side effects of psychiatric medications.⁷

Despite the availability of clinical practice guidelines and evidence-based interventions aimed at reducing excess mortality rates due to physical comorbidities in persons with SMI, the problem still persists. There is a crucial need to better understand how to implement clinical practice guidelines and evidence-based interventions successfully in the real world. Individuals with comorbid mental and physical disorders face specific challenges in service organization, requiring complex treatment and intense resource utilization.⁸ The management of multimorbidity is recognized to be complex, with a high treatment burden in terms of understanding and self-managing the conditions, attending multiple appointments, and managing complex drug regimens.⁹ Evidence suggests that a key part of the problem is the lack of integration of care across service settings. There is major fragmentation in how care is coordinated between family doctors and hospitals, between physical and mental health care, and across health and social care. As proposed by Tapio Paljärvi et al.¹ and other authors,¹⁰ person-centered and integrated care models are a critical step to support effective implementation approaches to translate evidence into practice.

However, despite local and innovative experiences in several countries, the large-scale implementation of integrated care models faces numerous barriers and conservatisms. These obstacles hinder progress and maintain the status quo, perpetuating major health inequalities in individuals with SMI. A call for action in Europe, similar to initiatives already established in the United States,¹¹ is necessary. Currently, there are two main types

Background: Perinatal depression (PND) is a debilitating condition affecting maternal well-being and child development. Allopregnanolone (ALLO) is important to perinatal neuroplasticity, however its relationship with depression severity and postpartum structural brain volume is unknown.

Method: We examined perinatal temporal dynamics and bidirectional associations between ALLO and depression severity and the association between these variables and postpartum gray matter volume, using a random intercept cross-lagged panel model.

Results: We identified a unidirectional predictive relationship between PND severity and ALLO concentration, suggesting greater depression severity early in the perinatal period may contribute to subsequent changes in ALLO concentration (β = 0.26, p = 0.009), while variations in ALLO levels during the perinatal period influences the development and severity of depressive symptoms later in the postpartum period (β = 0.38, p = 0.007). Antepartum depression severity (Visit 2, β = 0.35, p = 0.004), ALLO concentration (Visit 2, β = 0.37, p = 0.001), and postpartum depression severity (Visit 3, β = 0.39, p = 0.031), each predicted the right anterior cingulate volume. Antepartum ALLO concentration (Visit 2, β = 0.29, p = 0.001) predicted left suborbital sulcus volume. Antepartum depression severity (Visit 1, β = 0.39, p = 0.006 and Visit 2, β = 0.48, p < 0.001) predicted the right straight gyrus volume. Postpartum depression severity (Visit 3, β = 0.36, p = 0.001) predicted left middle-posterior cingulate volume.

Conclusion: These results provide the first evidence of bidirectional associations between perinatal ALLO and depression severity with postpartum gray matter volume.

Background: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

Methods: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.

Results: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.

Conclusion: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Background: Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).

Methods: We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.

Results: A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry.

Conclusion: This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

Mental health among children, adolescents, and young adults deteriorated during the Covid-19 pandemic, leading to concurrent significant rises in utilization of psychotropics.^{1, 2} This escalation in psychotropic medication use raised concerns for potential long-term effects beyond the immediate aftermaths of the pandemic. We aimed to assess rates of incident psychotropic medication use in the post-pandemic period compared with pre-pandemic trends in Danish children, adolescents, and young adults.

In this population-based study, we used individual-level data from the Danish National Prescription Registry³ to identify all individuals aged 5–24 years who filled a prescription for psychotropic medication from January 1, 2015, to December 31, 2023. Prescriptions for the following psychotropic drug classes were included: antipsychotics (Anatomical Therapeutic Chemical [ATC] code N05A), anxiolytics (N05B), hypnotics and sedatives (N05C), antidepressants (N06A), and psychostimulants (N06B). For each month of the study period, we determined the number of incident users of each drug class and obtained the total number of 5–24-year-olds living in Denmark from the Danish Civil Registration System. Drug use was considered incident if there had been no prescription fill for the given drug in the previous 5 years.

Interrupted time series analysis was used to determine the monthly number of incident users of psychotropic medication in the post-pandemic period compared with the counterfactual pre-pandemic trend. Time-trends were modeled using Poisson regression with 105 data points. The model included three time segments: the pre-pandemic period (January 2015 to February 2020), the pandemic period (March 2020 to January 2022), and the post-pandemic period (from February 2022 until end of data availability). The end of the pandemic period was defined as January 2022, following the lifting of all Danish Covid-19-related restrictions. We modeled both the level and slope changes associated with each time segment and included Fourier terms and a scale parameter to account for seasonality and overdispersion (see Supplementary Methods).⁴ Using model predictions, we obtained the cumulative number of incident psychotropic drug users in the post-pandemic period compared with pre-pandemic predictions and estimated the number of excess cases of incident psychotropic use and corresponding risk ratios (RR) with 95% confidence intervals (CI). The analysis was repeated for each psychotropic drug class and stratified by sex and age groups (5–11, 12–17, 18–24 years).

From 2015 through 2023, 182,097 Danish individuals aged 5–24 years filled an incident prescription for a psychotropic medication (55% female, median age 18.8 years [interquartile range 14.7–22.0]). The yearly incidence rate (IR) of psychotropic medication use increased from 1166 new users per 100,000 person-years (PY) in 2015