After the rapid implementation of digital health services during the COVID-19 pandemic, a paucity of research exists about the suitability of remote consulting in people with intellectual disabilities and their carers, particularly for neuropsychiatric reviews.

This study examines when remote neuropsychiatric routine consulting is suitable for this population.

A survey was conducted of people with intellectual disabilities and their carers, examining their preference between face-to-face and video consultations for ongoing neuropsychiatric reviews within a rural countywide intellectual disability service in Cornwall, England (population: 538 000). The survey was sent to all adults with intellectual disabilities open to the service on 30 July 2022, closing on 30 September 2022. Participants were asked to provide responses on 11 items predesigned and co-produced between clinicians and experts by experience. The entire service caseload of people had White ethnicity, reflecting the ethnic demographics of Cornwall. Responses received without consent were excluded from the study dataset.

Of 271 eligible participants, 119 responses were received, 104 of whom consented to having their anonymised data used for research analysis. There were no significant differences between preferences and age and gender variables. There was no statistically significant difference regarding preference for the reintroduction of face-to-face appointments (52.0%) compared with video consultations (48.0%). Travel distance (>10 miles) to the clinical setting was important but did not outweigh benefits for those preferring a face-to-face appointment.

This study offers insights into the factors that influence preferences about what type of neuropsychiatric appointment is most suitable for people with intellectual disabilities.

Current assessment and management models often do not adequately address the many aspects of managing complex brain disorders involving disordered affect, behaviour and cognition (ABC). A more collaborative model of care, where several specialties can jointly assess and manage patients with complex brain disorders, is gaining attention.

In this case report, we present two cases that highlight the benefits of the ‘brain medicine’ clinical model.

The Brain Medicine Clinic employs an integrated clinical model in which psychiatrists and neurologists provide integrated interdisciplinary assessments of patients with complex brain disorders, leading to comprehensive assessment. We describe the clinical model and the trajectories of two patients with complex brain disorders seen in this clinic. In these case descriptions, we explain how the brain medicine clinical approach leads to an improved patient experience.

The Brain Medicine Clinic assessments resulted in a neurobiopsychosocial formulation of symptoms and, consequently, holistic individualised treatment plans for two patients with complex brain disorders. This approach to patients’ conditions emerges from the understanding that there are multifactorial causes of brain disorders at the social, cultural, psychological and biological level.

Integrated interdisciplinary assessments allow for tailored treatment plans for individuals experiencing complex brain disorders, while creating efficiencies for the patient and the healthcare system.

Disasters occur globally and can impact emergency department (ED) services. Chemical, biological, radiological, and nuclear (CBRN) events have different characteristics in terms of onset and duration when compared to other disasters, such as wildfires, floods, and hurricanes. It is important to have an understanding of the impact of CBRN events on EDs to inform disaster preparedness. The purpose of this paper is to identify peer-reviewed published literature that describes the impact on EDs from CBRN events.

An integrative literature methodology was used, guided by the Preferred Reporting Items of Systematic reviews and Meta-Analysis (PRISMA) Guidelines. MEDLINE, PsycINFO, CINAHL, Pubmed, and Scopus were searched using terms relating to CBRN events and EDs. Papers were included if they focused on the impact of real-world CBRN event(s). Information from each included paper was extracted into a table, including author(s), CBRN event characteristics, ED response characteristics, patient presentation characteristics, and outcome characteristics.

Of the 15,982 studies that were identified from the database searches, 4,012 were duplicates and 11,696 were irrelevant at the title and abstract screening stage. Therefore, 274 were screened at the full-text stage resulting in 44 studies for inclusion. Included papers were mostly from the United States of America (n=22/44, 50%), followed by Turkey (n=4/44, 9.1%). Most of the events were chemical (n=36/44, 81.9%), with Chlorine (n=9/36, 25%) being the most frequently reported chemical agent. Between 1 and 5,500 people [M=54, IQR: 22-253] presented to EDs because of CBRN events.

Emergency departments assess and manage patients who present following CBRN events. Of these patients, the majority do not require hospital admission, suggesting that the ED is integral in the health response to CBRN events. As such, EDs should be adequately prepared, from a resource and process perspective to assess, manage and discharge large numbers of CBRN-related patients.

Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.

Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).

Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.

These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

Subthreshold/attenuated syndromes are established precursors of full-threshold mood and psychotic disorders. Less is known about the individual symptoms that may precede the development of subthreshold syndromes and associated social/functional outcomes among emerging adults.

We modeled two dynamic Bayesian networks (DBN) to investigate associations among self-rated phenomenology and personal/lifestyle factors (role impairment, low social support, and alcohol and substance use) across the 19Up and 25Up waves of the Brisbane Longitudinal Twin Study. We examined whether symptoms and personal/lifestyle factors at 19Up were associated with (a) themselves or different items at 25Up, and (b) onset of a depression-like, hypo-manic-like, or psychotic-like subthreshold syndrome (STS) at 25Up.

The first DBN identified 11 items that when endorsed at 19Up were more likely to be reendorsed at 25Up (e.g., hypersomnia, impaired concentration, impaired sleep quality) and seven items that when endorsed at 19Up were associated with different items being endorsed at 25Up (e.g., earlier fatigue and later role impairment; earlier anergia and later somatic pain). In the second DBN, no arcs met our a priori threshold for inclusion. In an exploratory model with no threshold, >20 items at 19Up were associated with progression to an STS at 25Up (with lower statistical confidence); the top five arcs were: feeling threatened by others and a later psychotic-like STS; increased activity and a later hypo-manic-like STS; and anergia, impaired sleep quality, and/or hypersomnia and a later depression-like STS.

These probabilistic models identify symptoms and personal/lifestyle factors that might prove useful targets for indicated preventative strategies.

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.

Coronavirus disease 2019 (COVID-19) has had a disproportionate impact on people with intellectual disability (PwID). PwID are at higher risk of mental illness and receive psychotropic prescribing ‘off licence' also, to manage distress behaviour. The lockdown and reduction of multidisciplinary face-to-face appointments had an impact on care delivery, the recourse possibly being psychotropic prescribing. It is imperative to comprehend the influence the pandemic had on psychotropic prescribing patterns to enable future planning.

The aim was to understand the impact of the pandemic by comparing psychotropic prescribing patterns during the England lockdown with the prescribing patterns before lockdown in specialist urban and rural psychiatric services for PwID.

Data was collected from Cornwall (rural) and London (urban) intellectual disability services in England as a service evaluation project to rationalise psychotropic prescribing. PwID in both services open across January 2020 to January 2021 were included. Baseline patient demographics including age, gender, ethnicity, intellectual disability level and neurodevelopmental and psychological comorbidities were collected. Baseline psychotropic prescribing and subsequent % change for each psychotropic group for the two services was compared using Pearson's chi-square and z-statistic (two tailed) with significance taken at P < 0.05.

The two centres London (n = 113) and Cornwall (n = 97) were largely comparable but for baseline differences in terms of presence of severe mental illness (37 v. 86, P < 0.001), challenging behaviour (44 v. 57, P < 0.05) and attention-deficit hyperactivity disorder (37 v. 3, P < 0.001). There was an overall increase in psychotropic prescribing during lockdown in urban as compared with rural settings (11% v. 2%).

The pandemic caused an increase in psychotropic prescribing associated with lockdown severity and urban settings. Team structures could have played a role.

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.

Discrepancies exist in reports of social cognition deficits in individuals with premanifest Huntington’s disease (HD); however, the reason for this variability has not been investigated. The aims of this study were to (1) evaluate group- and individual-level social cognitive performance and (2) examine intra-individual variability (dispersion) across social cognitive domains in individuals with premanifest HD.

Theory of mind (ToM), social perception, empathy, and social connectedness were evaluated in 35 individuals with premanifest HD and 29 healthy controls. Cut-off values beneath the median and 1.5 × the interquartile range below the 25th percentile (P25 – 1.5 × IQR) of healthy controls for each variable were established for a profiling method. Dispersion between social cognitive domains was also calculated.

Compared to healthy controls, individuals with premanifest HD performed worse on all social cognitive domains except empathy. Application of the profiling method revealed a large proportion of people with premanifest HD fell below healthy control median values across ToM (>80%), social perception (>57%), empathy (>54%), and social behaviour (>40%), with a percentage of these individuals displaying more pronounced impairments in empathy (20%) and ToM (22%). Social cognition dispersion did not differ between groups. No significant correlations were found between social cognitive domains and mood, sleep, and neurocognitive outcomes.

Significant group-level social cognition deficits were observed in the premanifest HD cohort. However, our profiling method showed that only a small percentage of these individuals experienced marked difficulties in social cognition, indicating the importance of individual-level assessments, particularly regarding future personalised treatments.

Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes.

Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2.

Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses.

ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.