Early worsening of plasma lipid levels (EWL; ≥5% change after 1 month) induced by at-risk psychotropic treatments predicts considerable exacerbation of plasma lipid levels and/or dyslipidaemia development in the longer term.

We aimed to determine which clinical and genetic risk factors could predict EWL.

Predictive values of baseline clinical characteristics and dyslipidaemia-associated single nucleotide polymorphisms (SNPs) on EWL were evaluated in a discovery sample (n = 177) and replicated in two samples from the same cohort (PsyMetab; n1 = 176; n2 = 86).

Low baseline levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C) and triglycerides, and high baseline levels of high-density lipoprotein cholesterol (HDL-C), were risk factors for early increase in total cholesterol (P = 0.002), LDL-C (P = 0.02) and triglycerides (P = 0.0006), and early decrease in HDL-C (P = 0.04). Adding genetic parameters (n = 17, 18, 19 and 16 SNPs for total cholesterol, LDL-C, HDL-C and triglycerides, respectively) improved areas under the curve for early worsening of total cholesterol (from 0.66 to 0.91), LDL-C (from 0.62 to 0.87), triglycerides (from 0.73 to 0.92) and HDL-C (from 0.69 to 0.89) (P ≤ 0.00003 in discovery sample). The additive value of genetics to predict early worsening of LDL-C levels was confirmed in two replication samples (P ≤ 0.004). In the combined sample (n ≥ 203), adding genetics improved the prediction of new-onset dyslipidaemia for total cholesterol, LDL-C and HDL-C (P ≤ 0.04).

Clinical and genetic factors contributed to the prediction of EWL and new-onset dyslipidaemia in three samples of patients who started at-risk psychotropic treatments. Future larger studies should be conducted to refine SNP estimates to be integrated into clinically applicable predictive models.

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Depression and anxiety are the leading contributors to the global burden of disease among young people, accounting for over a third (34.8%) of years lived with disability. Yet there is limited evidence for interventions that prevent adolescent depression and anxiety in low- and middle-income countries (LMICs), where 90% of adolescents live. This article introduces the ‘Improving Adolescent mentaL health by reducing the Impact of poVErty (ALIVE)’ study, its conceptual framework, objectives, methods and expected outcomes. The aim of the ALIVE study is to develop and pilot-test an intervention that combines poverty reduction with strengthening self-regulation to prevent depression and anxiety among adolescents living in urban poverty in Colombia, Nepal and South Africa.

This aim will be achieved by addressing four objectives: (1) develop a conceptual framework that identifies the causal mechanisms linking poverty, self-regulation and depression and anxiety; (2) develop a multi-component selective prevention intervention targeting self-regulation and poverty among adolescents at high risk of developing depression or anxiety; (3) adapt and validate instruments to measure incidence of depression and anxiety, mediators and implementation parameters of the prevention intervention; and (4) undertake a four-arm pilot cluster randomised controlled trial to assess the feasibility, acceptability and cost of the selective prevention intervention in the three study sites.

The contributions of this study include the active engagement and participation of adolescents in the research process; a focus on the causal mechanisms of the intervention; building an evidence base for prevention interventions in LMICs; and the use of an interdisciplinary approach.

By developing and evaluating an intervention that addresses multidimensional poverty and self-regulation, ALIVE can make contributions to evidence on the integration of mental health into broader development policy and practice.

Primary health care (PHC) supported long-term care facilities (LTCFs) in attending COVID-19 patients. The aim of this study is to describe the role of PHC in LTCFs in Europe during the early phase of the pandemic.

Retrospective descriptive study from 30 European countries using data from September 2020 collected with an ad hoc semi-structured questionnaire. Related variables are SARS-CoV-2 testing, contact tracing, follow-up, additional testing, and patient care.

Twenty-six out of the 30 European countries had PHC involvement in LTCFs during the COVID-19 pandemic. PHC participated in initial medical care in 22 countries, while, in 15, PHC was responsible for SARS-CoV-2 test along with other institutions. Supervision of individuals in isolation was carried out mostly by LTCF staff, but physical examination or symptom’s follow-up was performed mainly by PHC.

PHC has participated in COVID-19 pandemic assistance in LTCFs in coordination with LTCF staff, public health officers, and hospitals.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

We evaluated the added value of infection control-guided, on demand, and locally performed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) genomic sequencing to support outbreak investigation and control in acute-care settings.

This 18-month prospective molecular epidemiology study was conducted at a tertiary-care hospital in Montreal, Canada. When nosocomial transmission was suspected by local infection control, viral genomic sequencing was performed locally for all putative outbreak cases. Molecular and conventional epidemiology data were correlated on a just-in-time basis to improve understanding of coronavirus disease 2019 (COVID-19) transmission and reinforce or adapt control measures.

Between April 2020 and October 2021, 6 outbreaks including 59 nosocomial infections (per the epidemiological definition) were investigated. Genomic data supported 7 distinct transmission clusters involving 6 patients and 26 healthcare workers. We identified multiple distinct modes of transmission, which led to reinforcement and adaptation of infection control measures. Molecular epidemiology data also refuted (n = 14) suspected transmission events in favor of community acquired but institutionally clustered cases.

SARS-CoV-2 genomic sequencing can refute or strengthen transmission hypotheses from conventional nosocomial epidemiological investigations, and guide implementation of setting-specific control strategies. Our study represents a template for prospective, on site, outbreak-focused SARS-CoV-2 sequencing. This approach may become increasingly relevant in a COVID-19 endemic state where systematic sequencing within centralized surveillance programs is not available.

clinicaltrials.gov identifier: NCT05411562

While shared clinical decision-making (SDM) is the preferred approach to decision-making in mental health care, its implementation in everyday clinical practice is still insufficient. The European Psychiatric Association undertook a study aiming to gather data on the clinical decision-making style preferences of psychiatrists working in Europe.

We conducted a cross-sectional online survey involving a sample of 751 psychiatrists and psychiatry specialist trainees from 38 European countries in 2021, using the Clinical Decision-Making Style – Staff questionnaire and a set of questions regarding clinicians’ expertise, training, and practice.

SDM was the preferred decision-making style across all European regions ([central and eastern Europe, CEE], northern and western Europe [NWE], and southern Europe [SE]), with an average of 73% of clinical decisions being rated as SDM. However, we found significant differences in non-SDM decision-making styles: participants working in NWE countries more often prefer shared and active decision-making styles rather than passive styles when compared to other European regions, especially to the CEE. Additionally, psychiatry specialist trainees (compared to psychiatrists), those working mainly with outpatients (compared to those working mainly with inpatients) and those working in community mental health services/public services (compared to mixed and private settings) have a significantly lower preference for passive decision-making style.

The preferences for SDM styles among European psychiatrists are generally similar. However, the identified differences in the preferences for non-SDM styles across the regions call for more dialogue and educational efforts to harmonize practice across Europe.

A primary admission of patients with suspected acute ischemic stroke and large vessel occlusion (LVO) to centers capable of providing endovascular stroke therapy (EVT) may induce shorter time to treatment and better functional outcomes. One of the limitations in this strategy is the need for accurately identifying LVO patients in the prehospital setting. We aimed to study the feasibility and diagnostic performance of point-of-care ultrasound (POCUS) for the detection of LVO in patients with acute stroke.

We conducted a proof-of-concept study and selected 15 acute ischemic stroke patients with angiographically confirmed LVO and 15 patients without LVO. Duplex ultrasonography (DUS) of the common carotid arteries was performed, and flow profiles compatible with LVO were scored independently by one experienced and one junior neurologist.

Among the 15 patients with LVO, 6 patients presented with an occlusion of the carotid-T and 9 patients presented with an M1 occlusion. Interobserver agreement between the junior and the experienced neurologist was excellent (kappa = 0.813, p < 0.001). Flow profiles of the CAA allowed the detection of LVO with a sensitivity of 73%, a positive predictive value of 92 and 100%, and a c-statistics of 0.83 (95%CI = 0.65–0.94) and 0.87 (95%CI = 0.69–0.94) (experienced neurologist and junior neurologist, respectively). In comparison with clinical stroke scales, DUS was associated with better trade-off between sensitivity and specificity.

POCUS in acute stroke setting is feasible, it may serve as a complementary tool for the detection of LVO and is potentially applicable in the prehospital phase.