There is a significant mortality gap between the general population and people with psychosis. Completion rates of regular physical health assessments for cardiovascular risk in this group are suboptimal. Point-of-care testing (POCT) for diabetes and hyperlipidaemia – providing an immediate result from a finger-prick – could improve these rates.

To evaluate the impact on patient–clinician encounters and on physical health check completion rates of implementing POCT for cardiovascular risk markers in early intervention in psychosis (EIP) services in South East England.

A mixed-methods, real-world evaluation study was performed, with 40 POCT machines introduced across EIP teams in all eight mental health trusts in South East England from March to May 2021. Clinician training and support was provided. Numbers of completed physical health checks, HbA1c and lipid panel blood tests completed 6 and 12 months before and 6 months after introduction of POCT were collected for individual patients. Data were compared with those from the South West region, which acted as a control. Clinician questionnaires were administered at 2 and 8 months, capturing device usability and impacts on patient interactions.

Post-POCT, South East England saw significant increases in HbA1c testing (odds ratio 2.02, 95% CI 1.17–3.49), lipid testing (odds ratio 2.38, 95% CI 1.43–3.97) and total completed health checks (odds ratio 3.61, 95% CI 1.94–7.94). These increases were not seen in the South West. Questionnaires revealed improved patient engagement, clinician empowerment and patients’ preference for POCT over traditional blood tests.

POCT is associated with improvements in the completion and quality of physical health checks, and thus could be a tool to enhance holistic care for individuals with psychosis.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Despite the global expansion of electronic medical record (EMR) systems and their increased integration with artificial intelligence (AI), their utilization in disaster settings remains limited, and few studies have evaluated their implementation. We aimed to evaluate Fast Electronic Medical Record (fEMR), a novel, mobile EMR designed for resource-limited settings, based on user feedback.

We examined usage data through October 2022 to categorize the nature of its use for disaster response and determine the number of patients served. We conducted interviews with stakeholders and gathered input from clinicians who had experience using fEMR.

Over eight years, fEMR was employed 60 times in 11 countries across four continents by 14 organizations (universities, non-profits, and disaster response teams). This involved 37,500+ patient encounters in diverse settings including migrant camps at the US-Mexico and Poland-Ukraine borders, mobile health clinics in Kenya and Guatemala, and post-earthquake relief in Haiti. User feedback highlighted adaptability, but suggested hardware and workflow improvements.

EMR systems have the potential to enhance healthcare delivery in humanitarian responses, offer valuable data for planning and preparedness, and support measurement of effectiveness. As a simple, versatile EMR system, fEMR has been deployed to numerous disaster response and low-income settings.

The absence of clinical information in the aftermath of disasters in resource-constrained environments costs lives. fEMR– fast Electronic Medical Records–is a medical records system designed for mobile clinics and has proven useful in post-disaster settings. While the original version of the system was developed for areas without access to the Internet, a new version of this system was developed in 2019 to accommodate regions with connectivity.

We reviewed the design, implementation, and usage of fEMR from June 2014 to October 2022. We used logged data of the number of users, patient encounters, and the circumstances of each deployment. We compared usage between the original fEMR system and fEMR-on-chain.

The original fEMR system was created in an iterative process by students in Computer Science classes at three different American universities. The system creates a closed intranet signal to which clinicians connect their own device to access the software. The hardware is transported to the medical team in a carry-on suitcase prior to deployment. All data are stored on a laptop that acts as a server. The online version, fEMR On-Chain, was developed under a grant, but is sustained in development through academic partnerships. Both versions are designed so that the provider can complete an encounter with as few clicks as possible and with as little input as necessary to identify patients.The original fEMR system has been deployed to mobile clinics worldwide since 2014. The system has about 14,181 patients and 16,021 clinical encounters from 12 different countries. fEMR On-Chain has been deployed to refugee and migrant settings since 2019, containing about 18,000 patients and 22,000 encounters in two different countries.

Successive versions of the fEMR system have been used in a variety of conditions and settings, with usage accelerating since 2019 in refugee and migrant health centers.

Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment.

Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108).

Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application.

Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.

Recently, the Health of the Nation Outcome Scales 65+ (HoNOS65+) were revised. Twenty-five experts from Australia and New Zealand completed an anonymous web-based survey about the content validity of the revised measure, the HoNOS Older Adults (HoNOS OA).

All 12 HoNOS OA scales were rated by most (≥75%) experts as ‘important’ or ‘very important’ for determining overall clinical severity among older adults. Ratings of sensitivity to change, comprehensibility and comprehensiveness were more variable, but mostly positive. Experts’ comments provided possible explanations. For example, some experts suggested modifying or expanding the glossary examples for some scales (e.g. those measuring problems with relationships and problems with activities of daily living) to be more older adult-specific.

Experts agreed that the HoNOS OA measures important constructs. Training may need to orient experienced raters to the rationale for some revisions. Further psychometric testing of the HoNOS OA is recommended.

Transient ischaemic attack (TIA) can lead to lasting changes in brain structure and function resulting in cognitive impairment. Cognitive screening tools may lack sensitivity for detecting cognitive impairments, particularly executive function, which tends to be the earliest affected domain in vascular cognitive impairment.

In this preliminary study, we examine a working memory (WMem) task as a sensitive measure of cognitive impairment in TIA.

Patients referred to a TIA clinic for transient neurological symptoms completed a general cognitive screening tool (Montreal Cognitive Assessment; MoCA), and a WMem task (2-N-back) in a cross-sectional design.

TIA patients (n = 12) showed significantly reduced WMem performance on the N-back compared to patients diagnosed with mimic clinical conditions with overlapping symptoms (n = 16). No group differences were observed on the MoCA.

Assessing WMem may provide a sensitive measure of cognitive impairment after TIA, with implications for cognitive screening in TIA services to triage patients for further neuropsychological support, or for interventions to prevent vascular dementia.

There has been a notable increase in requests for psychiatric reports from District Courts for persons remanded to Ireland’s main remand prison, Cloverhill. We aimed to identify if reports were prepared for persons with severe mental illness and if they led to therapeutic benefits such as diversion to healthcare. Measures of equitability between Cloverhill and other District Courts were explored.

For District Court-requested reports completed by the Prison Inreach and Court Liaison Service (PICLS) at Cloverhill Prison from 2015 to 2017, we recorded clinical variables and therapeutic outcomes such as diversion to inpatient psychiatric settings.

Of 236 cases, over half were diverted to inpatient or outpatient psychiatric care. One-third of remand episodes were admitted to a psychiatric hospital, mainly in non-forensic settings. Nearly two-thirds had major mental illness, mainly schizophrenia and related conditions. Almost half had active psychosis. Cases in Cloverhill District Court and other District Courts were similarly likely to have active psychosis (47% overall) and hospital admission (33% overall). Voluntary reports were more likely to identify active psychosis, with over 90% diverted to inpatient or outpatient community treatment settings.

This is the first large scale study of diversion outcomes following requests for psychiatric advice from District Courts in Ireland. Requests were mainly appropriate. Over half led to diversion from the criminal justice system to healthcare settings. There is a need for a complementary network of diversion initiatives at every stage of the criminal justice system to effectively divert mentally ill individuals to appropriate settings at the earliest possible stage.

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.

A significant number of people with autism require in-patient psychiatric care. Although the requirement to adequately meet the needs of people with autism in these settings is enshrined in UK law and supported by national guidelines, little information is available on current practice.

To describe characteristics of UK in-patient psychiatric settings admitting people with autism. Also to examine psychiatric units for their suitability, and the resultant impact on admission length and restrictive interventions.

Multiple-choice questions about in-patient settings and their ability to meet the needs of people with autism and the impact on their outcomes were developed as a cross-sectional study co-designed with a national autism charity. The survey was distributed nationally, using an exponential and non-discriminatory snowballing technique, to in-patient unit clinicians to provide a current practice snapshot.

Eighty responses were analysed after excluding duplications, from across the UK. Significant variation between units across all enquired parameters exist. Lack of autism-related training and skills across staff groups was identified, this becoming disproportionate when comparing intellectual disability units with general mental health units particularly regarding psychiatrists working in these units (psychiatrists: 94% specialist skills in intellectual disability units versus 6% specialist skills in general mental health units). In total, 28% of survey respondents felt people with autism are more likely to be subject to seclusion and 40% believed in-patients with autism are likely to end in segregation.

There is no systematic approach to supporting people with autism who are admitted to in-patient psychiatric units. Significant concerns are highlighted of lack of professional training and skill sets resulting in variable clinical practice and care delivery underpinned by policy deficiency. This could account for the reported in-patient outcomes of longer stay and segregation experienced by people with autism.