Acta Psychiatrica Scandinavica最新文献

Introduction: Peripartum depression is common and treatment with mirtazapine may be indicated. However, evidence on its safety in pregnancy and lactation is fragmented. The objective of this systematic review was to evaluate the literature on the safety of mirtazapine in pregnancy and lactation.

Methods: PubMed, Embase, Medline, PsycInfo, and clinicaltrials.gov were searched for 'antidepressants' or 'mirtazapine' in combination with 'pregnancy', 'lactation' or 'offspring'. No restrictions on type of study were applied and selection was performed by two independent reviewers using Covidence. Two reviewers extracted data and performed risk of bias assessment and evidence synthesis was performed for each outcome individually. The protocol was registered at PROSPERO (registration number CRD42021275127).

Results: The initial search yielded 15,380 articles after removal of duplicates. After screening based on title and abstract, 431 articles remained for full text review. Of these, 41 studies were included (15 cohort studies, one case-control study, 11 case series, and 14 case reports). In most studies, the outcomes in mirtazapine-exposed pregnancies were comparable to controls. However, results on congenital malformations and spontaneous abortion were conflicting. Neonatal adaptation syndrome was reported after mirtazapine exposure in late pregnancy. Data on mirtazapine exposure during lactation were scarce.

Conclusions: We identified no substantial evidence indicating that mirtazapine exposure is associated with adverse outcomes in pregnancy or in offspring, other than neonatal adaptation syndrome. However, overall quality of evidence was low, and results on congenital malformations and spontaneous abortions were conflicting. Data on mirtazapine exposure through breastfeeding were limited and did not allow for conclusions.

Introduction: Accurate detection of cardiometabolic risk in early psychosis is crucial to reducing somatic morbidity and mortality in people with psychotic disorders. We conducted an external validation of the psychosis metabolic risk calculator (PsyMetRiC), a cardiometabolic risk prediction tool developed in the UK and tailored for young people with psychosis. We compared the predictive accuracy and clinical usefulness of PsyMetRiC and a general population-based risk prediction tool for type 2 diabetes, the Finnish Diabetes Risk Score (FINDRISC).

Methods: We included first-episode psychosis and ultra-high-risk for psychosis patients without metabolic syndrome aged 18-35 years from the Helsinki Early Psychosis and Turku Early Psychosis Study cohorts. We tested two versions of PsyMetRiC: the full model including age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations, and the partial-model excluding biochemical predictors, and the simplified FINDRISC including BMI, sex, systolic blood pressure, and fasting glucose. Discrimination, calibration, and decision curve analyses were used to assess the predictive performance and clinical usefulness of both PsyMetRiC and FINDRISC. We performed a site-specific re-calibration of PsyMetRiC (PsyMetRiC-Fi).

Results: The study sample consisted of 278 individuals (all White European ethnicity, 58.6% male, mean age 24.8 years, 37.8% smoking, mean BMI 23.5). Discrimination was marginally better in the PsyMetRiC full model (C = 0.72, 95% CI, 0.59-0.82) compared with partial model (C = 0.70, 95% CI 0.59-0.80) or FINDRISC (C = 0.63, 95% CI 0.54-0.71). Calibration plots displayed evidence of minor miscalibration for PsyMetRiC, which corrected following recalibration. Miscalibration was more pronounced for FINDRISC. Decision curve analysis showed that PsyMetRiC offers likely clinical usefulness in improving cardiometabolic risk management in early psychosis compared with giving everyone or no one an intervention.

Conclusion: PsyMetRiC has utility in predicting cardiometabolic risk in Finnish patients with early psychosis. It has better discriminatory accuracy and offers more accurate risk prediction compared to other available strategies.

Introduction: Suicidal ideation variability refers to within-day fluctuations in suicidal ideation, and has recently been proposed as an indicator of suicide risk. However, not much is known yet about its correlates and clinical relevance.

Methods: We examined characteristics of real-time suicidal ideation using Ecological Momentary Assessment in 82 individuals with current active suicidal ideation. Data were collected four times daily over 21 days. Latent profile analysis was used to identify subtypes of suicidal ideation. We further examined sociodemographic and clinical correlates of the profiles, and their association with the occurrence of suicide attempts during a 1-year follow-up.

Results: We identified three "digital" phenotypes of suicidal ideation that differed on the frequency, intensity and variability of ideation. The profiles were: high frequency, high intensity, moderate variability (Phenotype 1), moderate/high frequency, moderate intensity, high variability (Phenotype 2), and moderate frequency, low intensity, low variability (Phenotype 3). Phenotypes 1 and 2 were associated with a worse clinical profile at baseline (higher suicidal ideation and depressive symptom severity), and increased odds of suicide attempt during follow-up, compared to Phenotype 3. Phenotype 1 was further characterized by repeated suicidal behavior.

Conclusions: Two phenotypes of real-time suicidal ideation were identified that appear to confer a higher risk of suicidal behavior in the near future (12 months). These phenotypes were characterized by higher variability of suicidal ideation-and also higher intensity and frequency of ideation. Considering the small sample size, the clinical usefulness of the profiles remains to be demonstrated.

Introduction: The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

Results: A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

Discussion: Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Background: Facial expressions are a core aspect of non-verbal communication. Reduced emotional expressiveness of the face is a common negative symptom of schizophrenia, however, quantifying negative symptoms can be clinically challenging and involves a considerable element of rater subjectivity. We used computer vision to investigate if (i) automated assessment of facial expressions captures negative as well as positive and general symptom domains, and (ii) if automated assessments are associated with treatment response in initially antipsychotic-naïve patients with first-episode psychosis.

Method: We included 46 patients (mean age 25.4 (6.1); 65.2% males). Psychopathology was assessed at baseline and after 6 weeks of monotherapy with amisulpride using the Positive and Negative Syndrome Scale (PANSS). Baseline interview videos were recorded. Seventeen facial action units (AUs), that is, activation of muscles, from the Facial Action Coding System were extracted using OpenFace 2.0. A correlation matrix was calculated for each patient. Facial expressions were identified using spectral clustering at group-level. Associations between facial expressions and psychopathology were investigated using multiple linear regression.

Results: Three clusters of facial expressions were identified related to different locations of the face. Cluster 1 was associated with positive and general symptoms at baseline, Cluster 2 was associated with all symptom domains, showing the strongest association with the negative domain, and Cluster 3 was only associated with general symptoms. Cluster 1 was significantly associated with the clinically rated improvement in positive and general symptoms after treatment, and Cluster 2 was significantly associated with clinical improvement in all domains.

Conclusion: Using automated computer vision of facial expressions during PANSS interviews did not only capture negative symptoms but also combinations of the three overall domains of psychopathology. Moreover, automated assessments of facial expressions at baseline were associated with initial antipsychotic treatment response. The findings underscore the clinical relevance of facial expressions and motivate further investigations of computer vision in clinical psychiatry.

Introduction: Affective instability represents an important, transdiagnostic biobehavioural dimension of mental ill health and clinical outcome. The causes of affective instability remain unclear. This systematic review and meta-analysis evaluated the extent to which exposure to childhood adversity is associated with affective instability across psychiatric disorders, and which forms of adversity are most strongly associated with affective instability.

Methods: The review followed a published protocol (PROSPERO: CRD42020168676). Searches in Medline, Embase and PsychInfo identified studies using quantitative measures of childhood adversity and affective instability, published between January 1980 and July 2023. Data were analysed using a random effects meta-analysis separately for each outcome, namely affective lability, emotion dysregulation, and rapid cycling. The Mixed-Methods Appraisal Tool was used to appraise the quality of the literature.

Results: The search identified 36 studies involving 8431 participants. All reports focused on cross-sectional associations. We did not identify any prospective longitudinal research. The analysis showed small, but statistically significant effects of childhood adversity on affective lability (r = 0.09, 95% CI 0.02, 0.17), emotion dysregulation (r = 0.25, 95% CI 0.19, 0.32), and rapid cycling (OR = 1.39; 95% CI 1.14, 1.70). When considering adversity subtypes, emotional abuse showed the strongest effect on affective lability (r = 0.16, 95% CI 0.07, 0.24) and emotion dysregulation (r = 0.32, 95% CI 0.19, 0.44). Quality assessment scores were generally low. Most studies failed to control for confounding factors or offer assurances around the representativeness of the samples.

Conclusions: The findings suggest that childhood adversity, particularly emotional abuse, is associated emotional instability in adulthood, but further prospective longitudinal research is needed to confirm causality. The findings have implications for the prevention and treatment of affective instability across psychiatric disorders.

Introduction: Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD.

Aims of the study: We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase.

Methods: A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models.

Results: The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found.

Conclusions: Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.