New insights into the pathophysiology of mental disorders and innovations in psychiatric care depend on the availability of representative, longitudinal and multidimensional datasets across diverse, transdiagnostic populations. Biobanks usually attempt to collect such data in parallel to clinical routine, which is resource-intensive, puts additional burden on health-care providers, and may reduce the generalizability of the results. Despite containing rich phenotypic and biological information, data generated in routine clinical care is seldomly used for research purposes, because it is usually unstructured and locked in data silos. To truly link clinical practice and research, solutions that optimize the generation and scientific utilization of real-world clinical data are needed.

Evaluation of a new digital infrastructure which warrants the efficient, automatized, and structured collection of real-world data in psychiatric care, and integrates the generated data into existing biobanking efforts.

We have developed a new documentation system which augments the existing IT-structures, enables the collection of routine clinical data in a structured format and involves patients in the data generation process. In an implementation science approach, to replicate and extend the findings of Blitz et al. (JMIR Ment Health 2021), we are investigating the acceptance, efficacy, and safety of the system in our outpatient clinic for affective disorders.

First results describing the technical safety, usage metrics, and acceptance of the system, and the quality of the collected data will be presented.

Challenges of collecting real-world data for biobanking and research purposes and perspectives on future digital solutions will be discussed.

No significant relationships.

The coronavirus disease 2019 (COVID-19) pandemic has required healthcare systems and hospitals to rapidly modify standard practice, including antimicrobial stewardship services. Our study examines the impact of COVID-19 on the antimicrobial stewardship pharmacist.

A survey was distributed nationally to all healthcare improvement company members.

Pharmacist participants were mostly leaders of antimicrobial stewardship programs distributed evenly across the United States and representing urban, suburban, and rural health-system practice sites.

Participants reported relative increases in time spent completing tasks related to medication access and preauthorization (300%; P = .018) and administrative meeting time (34%; P = .067) during the COVID-19 pandemic compared to before the pandemic. Time spent rounding, making interventions, performing pharmacokinetic services, and medication reconciliation decreased.

A shift away from clinical activities may negatively affect the utilization of antimicrobials.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Poor transition planning contributes to discontinuity of care at the child–adult mental health service boundary (SB), adversely affecting mental health outcomes in young people (YP). The aim of the study was to determine whether managed transition (MT) improves mental health outcomes of YP reaching the child/adolescent mental health service (CAMHS) boundary compared with usual care (UC).

A two-arm cluster-randomised trial (ISRCTN83240263 and NCT03013595) with clusters allocated 1:2 between MT and UC. Recruitment took place in 40 CAMHS (eight European countries) between October 2015 and December 2016. Eligible participants were CAMHS service users who were receiving treatment or had a diagnosed mental disorder, had an IQ ⩾ 70 and were within 1 year of reaching the SB. MT was a multi-component intervention that included CAMHS training, systematic identification of YP approaching SB, a structured assessment (Transition Readiness and Appropriateness Measure) and sharing of information between CAMHS and adult mental health services. The primary outcome was HoNOSCA (Health of the Nation Outcome Scale for Children and Adolescents) score 15-months post-entry to the trial.

The mean difference in HoNOSCA scores between the MT and UC arms at 15 months was −1.11 points (95% confidence interval −2.07 to −0.14, p = 0.03). The cost of delivering the intervention was relatively modest (€17–€65 per service user).

MT led to improved mental health of YP after the SB but the magnitude of the effect was small. The intervention can be implemented at low cost and form part of planned and purposeful transitional care.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.

We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.

Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.

We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.

We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Yoga may pose a promising complementary therapy in the multimodal treatment of schizophrenia spectrum disorders (SSD). However, to date, no studies have qualitatively examined the patients’ experience of practising Yoga.

This qualitative study aimed to assess the mechanisms and processes of Yoga-based group therapy (YBGT) for in-patients with SSD by exploring their subjective experiences.

Twenty-five semi-structured interviews were conducted with in-patients with SSD after they participated in a YBGT session. Interviews were transcribed, coded by two independent researchers, and analysed using an inductive thematic approach. The research team collaboratively discussed emerging categories to reduce redundancy and form meaningful themes and subthemes.

The analysis revealed seven main themes. YBGT was perceived as feasible and focusing on individual adaptation, captured by the theme ‘inclusivity’. Nevertheless, participants encountered ‘challenges’; thus, physical limitations need to be considered. While practising together, participants experienced ‘interconnectedness’ and developed a ‘mindful stance’ as they accepted their limitations and adapted exercises with self-compassion. Following the flow of asanas required physical persistence, which ultimately led many participants to experience ‘confidence’ and ‘relaxation’. YBGT affected ‘symptom representation’ as heightened awareness led participants to notice impeding as well as improved symptoms.

YBGT seemed to have various promising effects on in-patients with SSD. Future research should examine to what extent these effects can be sustained and how the mindful approach during YBGT can be transferred to areas outside the Yoga class. Furthermore, a randomised-controlled trial could investigate the effectiveness of a manualised YBGT.

No significant relationships.

Evaluate the difference in antibiotic prescribing between various levels of resident training or attending types.

Observational, retrospective study.

Tertiary-care, academic medical center in Madison, Wisconsin.

We measured antibiotic utilization from January 1, 2016, through December 31, 2018, in our general medicine (GM) and hospitalist services. The GM1 service is staffed by outpatient internal medicine physicians, the GM2 service is staffed by geriatricians and hospitalists, and the GM3 service is staffed by only hospitalists. The GMA service is led by junior resident physicians, and the GMB service is led by senior resident physicians. We measured utilization using days of therapy (DOT) per 1,000 patient days (PD). In a secondary analysis based on antibiotic spectrum, we used average DOT per 1,000 PD.

Teaching services prescribed more antibiotics than nonteaching services (671.6 vs 575.2 DOT per 1,000 PD; P < .0001). Junior resident–led services used more antibiotics than senior resident–led services (740.9 vs 510.0 DOT per 1,000 PD; P < .0001). Overall, antibiotic prescribing was numerically similar between various attending physician backgrounds. A secondary analysis showed that GM services prescribed more broad-spectrum, anti-MRSA, and anti-pseudomonal antibiotics than the hospitalist services. GM junior resident–led services prescribed more broad-spectrum, anti-MRSA, and antipseudomonal therapy compared to their senior counterparts.

Antibiotics were prescribed at a significantly higher rate in services associated with trainees than those without. Services led by a junior resident physician prescribed antibiotics at a significantly higher rate than services led by a senior resident. Interventions to reduce unnecessary antibiotic exposure should be targeted toward resident physicians, especially junior trainees.

Age-of-onset (AO) seems to be a phenotypic variable with a strong genetic component and therefore useful in molecular analysis of bipolar disorder (BP). A debate about the cut-off point for defining early AO has developed over the last few years. Using an Expectation-Maximization algorithm Bellivier et al. (2001) found the best fit for a model with three onset-groups, proposing the age 20-21 as cut-off for early onset, while using the same algorithm Kennedy et al. (2005) found the best fit for a two onset-group model with age 40 as cut-off with an incidence peak for mania in the age-band 21-25. Based on segregation analysis, we proposed a two AO-group model with cut-off age 25 for early onset (Grigoroiu-Serbanescu et al. 2001). The present study aimed at investigating the best AO-model in 500 Romanian BPI and 1458 German BPI patients using commingling analysis (SAGEv6.01-software) (Elston et al, 2009). The best model was selected according to Akaike's Information Criterion (AIC).

The two AO-group and three AO-group models provided similar AIC-values both in the Romanian and the German sample. The Romanian early-onset group (40% cases) had means around 18 years, SDs=6-7, while in the German early-onset group the mean AO was around 20 years (SDs=9-11) (50% cases). Thus the cut-off for early-onset (X +1SD) was different.

Our results overlapped with the findings of Kennedy et al (2005) showing that two-curve and three-curve AO mixtures similarly fit the AO-distribution in BPI disorder and the cut-offs for early-onset differ by sample.