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Background People with severe mental illness (SMI) are at increased risk of cardiovascular disease (CVD), and initiatives for CVD risk factor screening in the UK have not reduced disparities.Objectives To describe the annual screening prevalence for CVD risk factors in people with SMI from April 2000 to March 2018, and to identify factors associated with receiving no screening and regular screening.Methods We identified adults with a diagnosis of SMI (schizophrenia, bipolar disorder or ‘other psychosis’) from UK primary care records in Clinical Practice Research Datalink. We calculated the annual prevalence of screening for blood pressure, cholesterol, glucose, body mass index, alcohol consumption and smoking status using multinomial logistic regression to identify factors associated with receiving no screening and complete screening.Results Of 216 136 patients with SMI, 55% received screening for all six CVD risk factors at least once during follow-up and 35% received all six within a 1-month period. Our findings suggest that patient characteristics and financial incentivisation influence screening prevalence of individual CVD risk factors, the likelihood of receiving screening for all six CVD risk factors annually and risk of receiving no screening.Conclusions The low proportion of people with SMI receiving regular comprehensive CVD risk factor screening is concerning. Screening needs to be embedded as part of broad physical health checks to ensure the health needs of people with SMI are being met. If we are to improve cardiovascular health, interventions are needed where risk of receiving no screening or not receiving regular screening is highest.

Abstract As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.

Abstract Background Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

Aims and method Adverse effects are a common concern when prescribing and reviewing medication, particularly in vulnerable adults such as older people and those with intellectual disability. This paper describes the development of an app giving information on side-effects, called Medichec, and provides a description of the processes involved in its development and how drugs were rated for each side-effect. Medications with central anticholinergic action, dizziness, drowsiness, hyponatraemia, QTc prolongation, bleeding and constipation were identified using the British National Formulary (BNF) and frequency of occurrence of these effects was determined using the BNF, product information and electronic searches, including PubMed. Results Medications were rated using a traffic light system according to how commonly the adverse effect was known to occur or the severity of the effect. Clinical implications Medichec can facilitate access to side-effects information for multiple medications, aid clinical decision-making, optimise treatment and improve patient safety in vulnerable adults.

Background Severe mental illness (SMI), which includes schizophrenia, schizoaffective disorder and bipolar disorder, has profound health impacts, even in the elderly. Aims To evaluate relative risk of hospital admission and length of hospital stay for physical illness in elders with SMI. Method To construct a population-based retrospective cohort observed from April 2007 to March 2016, data from a case registry with full but de-identified electronic health records were retrieved for patients of the South London and Maudsley NHS Foundation Trust, the single secondary mental healthcare service provider in south-east London. We compared participants with SMI aged >60 years old with the general population of the same age and residing in the same areas through data linkage by age-, sex- and fiscal-year-standardised admission ratios (SARs) for primary diagnoses at hospital discharge. Furthermore, we compared the duration of hospital stay with an age-, sex- and cause-of-admission-matched random group by linear regression for major causes of admission. Results In total, records for 4175 older people with SMI were obtained, relating to 10 342 admission episodes, showing an overall SAR for all physical illnesses of 5.15 (95% CI: 5.05, 5.25). Among the top causes of admission, SARs ranged from 3.87 for circulatory system disorders (ICD-10 codes: I00–I99) to 6.99 for genitourinary system or urinary conditions (N00–N39). Specifically, the diagnostic group of ‘symptoms, signs and findings, not elsewhere classified’ (R00–R99) had an elevated SAR of 6.56 (95% CI: 6.22, 6.90). Elders with SMI also had significantly longer hospital stays than their counterparts in the general population, especially for digestive system illnesses (K00–K93), after adjusting for confounding. Conclusions Poorer overall physical health and specific patterns were identified in elders with SMI.

IntroductionExperiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms.MethodsApplying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition).ResultsPatients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females.DiscussionWe thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.

Abstract Background This study was initiated and co-designed by a Participant and Public Involvement (PPI) group attached to HOMESIDE, a randomized controlled trial that investigated music and reading interventions for people living with dementia and their family caregivers across five countries: Australia, Germany, Norway, Poland, and the UK. The aim was to capture experiences of PPI across the five countries, explore the benefits and challenges of PPI in dementia research, and identify contributions made to the study. Methods We surveyed PPI members and academic researchers who collaborated on the HOMESIDE study. The survey was co-designed through consultation with PPI members and academics, alongside a small scoping literature review. Survey questions covered four topics: (1) expectations for PPI, (2) perceived contributions of PPI to the research study, (3) benefits and challenges of PPI, and (4) recommendations for future PPI in dementia research. Results There were 23 responses, representing 50% of the PPI members (n = 16) and 29% of academics (n = 7). PPI was found to be beneficial to the research and individuals involved. Contributions to the research included supporting recruitment and publicity, advising on the design of participant-facing materials, guiding the design and delivery of the interventions, and identifying cultural differences affecting research delivery. PPI members benefited from building connections, sharing experiences and receiving support, learning about dementia and research, and gaining new unexpected experiences. Academics learned about the realities of living with dementia, which they felt informed and grounded their work. Several challenges were identified, including the need for clear expectations and objectives, inconsistency of PPI members across research stages, limitations of meeting online versus in-person, scheduling difficulties, and language barriers. Conclusions This study identifies important considerations for implementing PPI within dementia studies and international healthcare research more broadly. Our findings guided the development of five recommendations: (1) involve PPI members as early as possible and throughout the research process; (2) create a space for constructive criticism and feedback; (3) have clear tasks, roles, and expectations for PPI members; (4) involve PPI members with a diverse range of experiences and backgrounds; and (5) embed infrastructure and planning to support PPI.

IntroductionSocial media platforms such as Twitter and Weibo facilitate both positive and negative communication, including cyberbullying. Empirical evidence has revealed that cyberbullying increases when public crises occur, that such behavior is gendered, and that social media user account verification may deter it. However, the association of gender and verification status with cyberbullying is underexplored. This study aims to address this gap by examining how Weibo users’ gender, verification status, and expression of affect and anger in posts influence cyberbullying attitudes. Specifically, it investigates how these factors differ between posts pro- and anti-cyberbullying of COVID-19 cases during the pandemic.MethodsThis study utilized social role theory, the Barlett and Gentile Cyberbullying Model, and general strain theory as theoretical frameworks. We applied text classification techniques to identify pro-cyberbullying and anti-cyberbullying posts on Weibo. Subsequently, we used a standardized mean difference method to compare the emotional content of these posts. Our analysis focused on the prevalence of affective and anger-related expressions, particularly examining variations across gender and verification status of the users.ResultsOur text classification identified distinct pro-cyberbullying and anti-cyberbullying posts. The standardized mean difference analysis revealed that pro-cyberbullying posts contained significantly more emotional content compared to anti-cyberbullying posts. Further, within the pro-cyberbullying category, posts by verified female users exhibited a higher frequency of anger-related words than those by other users.DiscussionThe findings from this study can enhance researchers’ algorithms for identifying cyberbullying attitudes, refine the characterization of cyberbullying behavior using real-world social media data through the integration of the mentioned theories, and help government bodies improve their cyberbullying monitoring especially in the context of public health crises.

Background The South London and Maudsley NHS Foundation Trust (SLaM) is one of the largest providers of secondary mental healthcare in Europe. In 2007, SLaM developed the Clinical Record Interactive Search (CRIS) system which provides authorised researchers with regulated, secure access to anonymised information extracted from SLaM’s electronic clinical records system1. CRIS has been very successful as a research data resource and has supported over 300 peer reviewed publications over its 15+ years of operation. The CRIS website (www.maudsleybrc.nihr.ac.uk/facilities/clinical-record-interactive-search-cris) was developed to provide information to SLaM service users, the general public, and prospective research users. The website includes information on what CRIS is, approved CRIS projects and publications resulting from CRIS projects, as well as detailed information on data linkages involving CRIS and on Natural Language Processing (NLP) algorithms as a resource. The website is the main source of information available to academic users prior to meeting with a member of the CRIS Team and is a large component of the wider CRIS communications plan which aims to ensure that all potential CRIS stakeholders can access relevant information about CRIS in several different formats. Introduction We planned to conduct a lay review of the CRIS webpages involving both SLaM service users, carers, and CRIS academic users. The lay review was designed to support with alignment against the following UK Heath Data Research Alliance (HDRA) Transparency Standards2: • Standard 1: Open access application form and guidance • Standard 2: Transparent application process and criteria • Standard 3: Clear website navigation • Standard 4: Consider Target Audience Methods A lay review of the website was conducted with a group of five mental health service users and carers. The review was conducted in two stages, firstly, an individual in-depth review of two webpages per person with an online questionnaire, followed by a focus group. An academic review by two junior researchers was also conducted, this involved a review of the webpages and completion of an online questionnaire. Results The online questionnaires and focus group suggested several improvements to the website. Feedback on each page was reviewed and compared against the UK HDRA Transparency Standards. A list of changes was drafted in consultation with communications colleagues. Changes were made to pages, including updating images, adding additional information, making accessibility changes, and adding a new page specifically aimed at service users and the public. Service user consultees were sent a further online questionnaire to provide feedback on the updated pages. Feedback was positive. Conclusions There were challenges involved in designing a website that was informative to both lay public readers and potential research users. Following the lay review and subsequent changes to the CRIS website, the website better meets the UK HDRA Transparency Standards. References • Perera, G., Broadbent, M., Callard, F., Chang, C-K., Downs, J., Dutta, R., Fernandes, A., Hayes, R.D., Henderson, M., Jackson, R., Jewell, A., Kadra, G., Little, R., Pritchard, M., Shetty, H., Tulloch, A. and Stewart, R. (2016) Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource. BMJ Open, 6:e00872. • Macaulay, Y., UK Health Data Research Alliance., Pan-UK Data Governance Steering Group., HDR UK Public Advisory Board., Five Safes Action Force. (2023) Pan-UK Data Governance Steering Group Data Access Transparency Standards. Available from: https://zenodo.org/records/8262453. [Accessed 23.04.2024]

This review examines the complex role of Pin1 in the development and treatment of cancer. Pin1 is the only peptidyl–prolyl isomerase (PPIase) that can recognize and isomerize phosphorylated Ser/Thr-Pro peptide bonds. Pin1 catalyzes a structural change in phosphorylated Ser/Thr-Pro motifs that can modulate protein function and thereby impact cell cycle regulation and tumorigenesis. The molecular mechanisms by which Pin1 contributes to oncogenesis are reviewed, including Pin1 overexpression and its correlation with poor cancer prognosis, and the contribution of Pin1 to aggressive tumor phenotypes involved in therapeutic resistance is discussed, with an emphasis on cancer stem cells, the epithelial-to-mesenchymal transition (EMT), and immunosuppression. The therapeutic potential of Pin1 inhibition in cancer is discussed, along with the promise and the difficulties in identifying potent, drug-like, small-molecule Pin1 inhibitors. The available evidence supports the efficacy of targeting Pin1 as a novel cancer therapeutic by analyzing the role of Pin1 in a complex network of cancer-driving pathways and illustrating the potential of synergistic drug combinations with Pin1 inhibitors for treating aggressive and drug-resistant tumors.

Objective The objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care.Design, setting and participants The data were extracted from an anonymised version of the electronic health records of a large secondary mental healthcare provider serving a catchment of 1.3 million residents in south London. These included patients under active referral, aged 18+ at the index date of 1 July 2018 and having at least one clinical document (≥30 characters) between 1 July 2017 and 1 July 2019. This cohort was compared with linked primary care records from one of the four local government areas.Outcome The primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain.Results A total of 27 211 patients were retrieved. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15 to 1.19), females (OR 1.42, 95% CI 1.35 to 1.49), Asians (OR 1.30, 95% CI 1.16 to 1.45) or black (OR 1.49, 95% CI 1.40 to 1.59) ethnicities, living in deprived neighbourhoods (OR 1.64, 95% CI 1.55 to 1.73) showed higher odds of recorded pain. Patients with severe mental illnesses were found to be less likely to report pain (OR 0.43, 95% CI 0.41 to 0.46, p

Background Following the onset of the COVID-19 pandemic, healthcare trusts began to implement remote working arrangements, with little knowledge of their impact on staff well-being. Aims To investigate how remote working of healthcare workers during the pandemic may have been associated with stress, productivity and work satisfaction at that time, and associations between loneliness, workplace isolation, perceived social support and well-being. Method A questionnaire was developed to explore remote working and productivity, stress and work satisfaction during time spent working remotely. Associations between current loneliness, workplace isolation and well-being, and the influence of perceived social support, were explored with perceived social support as a potential moderator. Results A total of 520 participants responded to the study, of whom 112 were men (21.5%) and 406 were women (78.1%), with an age range of 21–77 years (mean 40.0, s.d. = 12.1). Very few (3.1%) worked remotely before the COVID-19 pandemic, and this had increased significantly (96.9%). Those who worked ≥31 h a week remotely reported higher stress and lower workplace satisfaction at that time, compared with office work, yet also felt more productive. Current loneliness, workplace isolation and perceived social support were cross-sectionally associated with lower current well-being. Conclusions Those who worked more hours a week remotely during the pandemic reported increased stress, which may be related to the lack of resources in place to support this change in work.

Objectives Ethnicity data are critical for identifying inequalities, but previous studies suggest that ethnicity is not consistently recorded between different administrative datasets. With researchers increasingly leveraging cross-domain data linkages, we investigated the completeness and consistency of ethnicity data in two linked health and education datasets.Design Cohort study.Setting South London and Maudsley NHS Foundation Trust deidentified electronic health records, accessed via Clinical Record Interactive Search (CRIS) and the National Pupil Database (NPD) (2007–2013).Participants N=30 426 children and adolescents referred to local Child and Adolescent Mental Health Services.Primary and secondary outcome measures Ethnicity data were compared between CRIS and the NPD. Associations between ethnicity as recorded from each source and key educational and clinical outcomes were explored with risk ratios.Results Ethnicity data were available for 79.3% from the NPD, 87.0% from CRIS, 97.3% from either source and 69.0% from both sources. Among those who had ethnicity data from both, the two data sources agreed on 87.0% of aggregate ethnicity categorisations overall, but with high levels of disagreement in Mixed and Other ethnic groups. Strengths of associations between ethnicity, educational attainment and neurodevelopmental disorder varied according to which data source was used to code ethnicity. For example, as compared with White pupils, a significantly higher proportion of Asian pupils achieved expected educational attainment thresholds only if ethnicity was coded from the NPD (RR=1.46, 95% CI 1.29 to 1.64), not if ethnicity was coded from CRIS (RR=1.11, 0.98 to 1.26).Conclusions Data linkage has the potential to minimise missing ethnicity data, and overlap in ethnicity categorisations between CRIS and the NPD was generally high. However, choosing which data source to primarily code ethnicity from can have implications for analyses of ethnicity, mental health and educational outcomes. Users of linked data should exercise caution in combining and comparing ethnicity between different data sources.

Abstract Late-life depression (LLD) is a multifactorial disorder, with susceptibility and vulnerability potentially influenced by gene-environment interaction. The aim of this study was to investigate whether the 5-HTTLPR polymorphism is associated with LLD. The sample of 353 participants aged 65 years and over was randomly selected from the list of Kaunas city inhabitants by Residents’ Register Service of Lithuania. Depressive symptoms were ascertained using the EURO-D scale. The List of Threatening Events Questionnaire was used to identify stressful life events that happened over the last 6 months and during lifetime. A 5-HTTLPR and lifetime stressful events interaction was indicated by higher odds of depression in those with s/s genotype who experienced high stress compared to l/l carriers with low or medium stress, while 5-HTTLPR and current stressful events interaction analysis revealed that carriers of either one or two copies of the s allele had increased odds of depressive symptoms associated with stress compared to participants with the l/l genotype not exposed to stressful situations. Although no significant direct association was found between the 5-HTTLPR short allele and depression, our findings demonstrated that lifetime or current stressful life events and their modification by 5-HTTLPR genotype are risk factors for late-life depression.

Abstract Background Currently, the main pharmaceutical intervention for COVID-19 is vaccination. While antidepressant (AD) drugs have shown some efficacy in treatment of symptomatic COVID-19, their preventative potential remains largely unexplored. Analysis of association between prescription of ADs and COVID-19 incidence in the population would be beneficial for assessing the utility of ADs in COVID-19 prevention. Methods Retrospective study of association between AD prescription and COVID-19 diagnosis was performed in a cohort of community-dwelling adult mental health outpatients during the 1st wave of COVID-19 pandemic in the UK. Clinical record interactive search (CRIS) was performed for mentions of ADs within 3 months preceding admission to inpatient care of the South London and Maudsley (SLaM) NHS Foundation Trust. Incidence of positive COVID-19 tests upon admission and during inpatient treatment was the primary outcome measure. Results AD mention was associated with approximately 40% lower incidence of positive COVID-19 test results when adjusted for socioeconomic parameters and physical health. This association was also observed for prescription of ADs of the selective serotonin reuptake inhibitor (SSRI) class. Conclusions This preliminary study suggests that ADs, and SSRIs in particular, may be of benefit for preventing COVID-19 infection spread in the community. The key limitations of the study are its retrospective nature and the focus on a mental health patient cohort. A more definitive assessment of AD and SSRI preventative potential warrants prospective studies in the wider demographic.

Transcatheter aortic valve replacement is not widely used in patients with congenital heart disease. We describe our single-center experience of transcatheter aortic valve replacement in congenital heart disease, demonstrating short-term feasibility and safety, role in lifetime management of congenital aortic valve disease, and use as a bridge to recovery, future surgery, or transplantation.

Objectives To address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider. This paper describes the linkage process and methods for mitigating bias due to non-matching.Setting South London and Maudsley NHS Foundation Trust (SLaM), a mental healthcare provider in Southeast London.Design Clinical records from SLaM were supplied to the Office of National Statistics for linkage to the census through a deterministic matching algorithm. We examined clinical (International Classification of Disease-10 diagnosis, history of hospitalisation, frequency of service contact) and socio-demographic (age, gender, ethnicity, deprivation) information recorded in Clinical Record Interactive Search (CRIS) as predictors of linkage success with the 2011 census. To assess and adjust for potential biases caused by non-matching, we evaluated inverse probability weighting for mortality associations.Participants Individuals of all ages in contact with SLaM up until December 2019 (N=459 374).Outcome measures Likelihood of mental health records’ linkage to census.Results 220 864 (50.4%) records from CRIS linked to the 2011 census. Young adults (prevalence ratio (PR) 0.80, 95% CI 0.80 to 0.81), individuals living in more deprived areas (PR 0.78, 95% CI 0.78 to 0.79) and minority ethnic groups (eg, Black African, PR 0.67, 0.66 to 0.68) were less likely to match to census. After implementing inverse probability weighting, we observed little change in the strength of association between clinical/demographic characteristics and mortality (eg, presence of any psychiatric disorder: unweighted PR 2.66, 95% CI 2.52 to 2.80; weighted PR 2.70, 95% CI 2.56 to 2.84).Conclusions Lower response rates to the 2011 census among people with psychiatric disorders may have contributed to lower match rates, a potential concern as the census informs service planning and allocation of resources. Due to its size and unique characteristics, the linked data set will enable novel investigations into the relationship between socioeconomic factors and psychiatric disorders.

BackgroundIndividualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient’s health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.SettingEHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.ObjectivesTo determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning.MethodsThe Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60–X84; Y10–Y34; Y87.0/Y87.2).Findingsn = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been “monitored” by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram).ConclusionEHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

Abstract Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Background Little is known about the role of air pollution in how people with dementia use mental health services.Objective We examined longitudinal associations between air pollution exposure and mental health service use in people with dementia.Methods In 5024 people aged 65 years or older with dementia in South London, high resolution estimates of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. Associations between air pollution and Community Mental Health Team (CMHT) events (recorded over 9 years) were examined using negative binomial regression models. Cognitive function was measured using the Mini Mental State Examination (MMSE) and health and social functioning was measured using the Health of the Nation Outcomes Scale (HoNOS65+). Associations between air pollution and both MMSE and HoNOS65+ scores were assessed using linear regression models.Findings In the first year of follow-up, increased exposure to all air pollutants was associated with an increase in the use of CMHTs in a dose-response manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) (eg, NO2: adjusted incidence rate ratio (aIRR) 1.27, 95% CI 1.11 to 1.45, p

Objectives To investigate factors associated with the recording of glycated haemoglobin (HbA1c) in people with first diagnoses of serious mental illness (SMI) in a large mental healthcare provider, and factors associated with HbA1c levels, when recorded. To our knowledge this is the first such investigation, although attention to dysglycaemia in SMI is an increasing priority in mental healthcare.Design The study was primarily descriptive in nature, seeking to ascertain the frequency of HbA1c recording in the mental healthcare sector for people following first SMI diagnosis.Settings A large mental healthcare provider, the South London and Maudsley National Health Service Trust.Participants Using electronic mental health records data, we ascertained patients with first SMI diagnoses (schizophrenia, schizoaffective disorder, bipolar disorder) from 2008 to 2018.Outcome measures Recording or not of HbA1c level was ascertained from routine local laboratory data and supplemented by a natural language processing (NLP) algorithm for extracting recorded values in text fields (precision 0.89%, recall 0.93%). Age, gender, ethnic group, year of diagnosis, and SMI diagnosis were investigated as covariates in relation to recording or not of HbA1c and first recorded levels.Results Of 21 462 patients in the sample (6546 bipolar disorder; 14 916 schizophrenia or schizoaffective disorder; mean age 38.8 years, 49% female), 4106 (19.1%) had at least one HbA1c result recorded from laboratory data, increasing to 6901 (32.2%) following NLP. HbA1c recording was independently more likely in non-white ethnic groups (black compared with white: OR 2.45, 95% CI 2.29 to 2.62), and was negatively associated with age (OR per year increase 0.93, 0.92–0.95), female gender (0.83, 0.78–0.88) and bipolar disorder (0.49, 0.45–0.52).Conclusions Over a 10-year period, relatively low level of recording of HbA1c was observed, although this has increased over time and ascertainment was increased with text extraction. It remains important to improve the routine monitoring of dysglycaemia in these at-risk disorders.

BackgroundPain is a widespread issue, with 20% of adults (1 in 5) experiencing it globally. A strong association has been demonstrated between pain and mental health conditions, and this association is known to exacerbate disability and impairment. Pain is also known to be strongly related to emotions, which can lead to damaging consequences. As pain is a common reason for people to access health care facilities, electronic health records (EHRs) are a potential source of information on this pain. Mental health EHRs could be particularly beneficial since they can show the overlap of pain with mental health. Most mental health EHRs contain the majority of their information within the free-text sections of the records. However, it is challenging to extract information from free text. Natural language processing (NLP) methods are therefore required to extract this information from the text. ObjectiveThis research describes the development of a corpus of manually labeled mentions of pain and pain-related entities from the documents of a mental health EHR database, for use in the development and evaluation of future NLP methods. MethodsThe EHR database used, Clinical Record Interactive Search, consists of anonymized patient records from The South London and Maudsley National Health Service Foundation Trust in the United Kingdom. The corpus was developed through a process of manual annotation where pain mentions were marked as relevant (ie, referring to physical pain afflicting the patient), negated (ie, indicating absence of pain), or not relevant (ie, referring to pain affecting someone other than the patient, or metaphorical and hypothetical mentions). Relevant mentions were also annotated with additional attributes such as anatomical location affected by pain, pain character, and pain management measures, if mentioned. ResultsA total of 5644 annotations were collected from 1985 documents (723 patients). Over 70% (n=4028) of the mentions found within the documents were annotated as relevant, and about half of these mentions also included the anatomical location affected by the pain. The most common pain character was chronic pain, and the most commonly mentioned anatomical location was the chest. Most annotations (n=1857, 33%) were from patients who had a primary diagnosis of mood disorders (International Classification of Diseases—10th edition, chapter F30-39). ConclusionsThis research has helped better understand how pain is mentioned within the context of mental health EHRs and provided insight into the kind of information that is typically mentioned around pain in such a data source. In future work, the extracted information will be used to develop and evaluate a machine learning–based NLP application to automatically extract relevant pain information from EHR databases.

Objectives This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. Methods Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results. Results Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17–1.43 and OR = 1.16; 95% CI, 1.05–1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78–0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity. Conclusions This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

Abstract Background Individuals with autism spectrum disorder (ASD) are at particularly high risk of suicide and suicide attempts. Presentation to a hospital with self-harm is one of the strongest risk factors for later suicide. We describe the use of a novel data linkage between routinely collected education data and child and adolescent mental health data to examine whether adolescents with ASD are at higher risk than the general population of presenting to emergency care with self-harm. Methods A retrospective cohort study was conducted on the population aged 11–17 resident in four South London boroughs between January 2009 and March 2013, attending state secondary schools, identified in the National Pupil Database (NPD). Exposure data on ASD status were derived from the NPD. We used Cox regression to model time to first self-harm presentation to the Emergency Department (ED). Results One thousand twenty adolescents presented to the ED with self-harm, and 763 matched to the NPD. The sample for analysis included 113,286 adolescents (2.2% with ASD). For boys only, there was an increased risk of self-harm associated with ASD (adjusted hazard ratio 2·79, 95% CI 1·40–5·57, P

Abstract Acute coronary syndrome (ACS) is related to an increased risk of suicide. Although both diabetes and the brain-derived neurotrophic factor (BDNF) pathway are closely associated with ACS and suicide, the effects of these factors on suicidal behavior in ACS patients have not been assessed. We investigated the individual and interaction effects of diabetes and BDNF-related markers, namely the serum BDNF (sBDNF) level and the BDNF Val66Met polymorphism, on suicidal ideation (SI) in ACS patients. The presence of diabetes was ascertained, and sBDNF levels and the presence of the BDNF Val66Met polymorphism were measured in 969 patients within 2 weeks after an ACS episode. 711 patients were followed up at 1 year after the ACS episode. SI was assessed using the relevant items of the Montgomery–Åsberg Depression Rating Scale at baseline (acute SI) and the 1-year follow-up (chronic SI). Significant individual effects of low sBDNF levels were found on acute SI. The presence of both diabetes and a low sBDNF level or the BDNF Met/Met genotype was associated with acute SI, with multivariate logistic regression analyses revealing significant interaction effects. The highest frequency of chronic SI was seen in diabetic patients with an sBDNF level in the lowest tertile or with the BDNF Met/Met genotype, although the interaction terms were not statistically significant. Our study suggests that the combination of diabetes and BDNF-related markers, such as the sBDNF level and the BDNF Val66Met polymorphism, might provide a useful predictor of acute SI in ACS patients.

Background: This study sought to determine the impact of the COVID-19 pandemic response to healthcare delivery on outcomes in patients with cardiovascular disease. Methods: This is a population-based cohort study performed in the province of Nova Scotia, Canada (population 979,499), between the pre-COVID (March 1, 2017-March 16, 2020) and in-COVID (March 17, 2020-December 31, 2020) periods. Adult patients (age ≥ 18 years) with new-onset or existing cardiovascular disease were included for comparison between periods. The main outcome measures included the following: cardiovascular emergency department visits or hospitalizations, mortality, and out-of-hospital cardiac arrest. Results: In the first month of the in-COVID period, emergency department visits (n = 51,750) for cardiac symptoms decreased by 20.8% (95% confidence interval [CI] 14.0%-27.0%, P < 0.001). Cardiovascular hospitalizations (n = 20,609) declined by 48.1% (95% CI 40.4% to 54.9%, P < 0.001). The in-hospital mortality rate increased in patients with cardiovascular admissions in secondary care institutions by 55.1% (95% CI 10.1%-118%, P = 0.013). A decline of 20.4%-44.0% occurred in cardiovascular surgical/interventional procedures. The number of out-of-hospital cardiac arrests (n = 5528) increased from a monthly mean of 115 ± 15 to 136 ± 14, beginning in May 2020. Mortality for ambulatory patients awaiting cardiac intervention (n = 14,083) increased from 0.16% (n = 12,501) to 2.49% (n = 361) in the in-COVID period (P < 0.0001). Conclusions: This study demonstrates increased cardiovascular morbidity and mortality during restrictions maintained during the COVID-19 period, in an area with a low burden of COVID-19. As the healthcare system recovers or enters subsequent waves of COVID-19, these findings should inform communication to the public regarding cardiovascular symptoms, and policy for delivery of cardiovascular care. Résumé: Contexte: Cette étude visait à déterminer les répercussions de la réponse à la pandémie de COVID-19 sur la prestation des soins de santé et son incidence sur les résultats obtenus par les patients atteints d’une maladie cardiovasculaire. Méthodologie: Il s’agit d’une étude de cohorte représentative de la population réalisée dans la province de la Nouvelle-Écosse, au Canada (population de 979 499 habitants), entre la période précédant le début de la pandémie de COVID-19 (du 1er mars 2017 au 16 mars 2020) et la période de pandémie (du 17 mars 2020 au 31 décembre 2020). Des patients adultes (âge ≥ 18 ans) atteints d’une maladie cardiovasculaire préexistante ou d’apparition récente ont été inclus pour la comparaison entre les périodes. Les principaux paramètres d’évaluation comprenaient les visites ou hospitalisations dans un service d’urgences cardiovasculaires, la mortalité et l’arrêt cardiaque en milieu extrahospitalier. Résultats: Au cours du premier mois de la période de pandémie, les visites aux services des urgences (n = 51 750) pour des symptômes cardiaques ont diminué de 20,8 % (intervalle de confiance [IC] à 95 % : 14,0 % – 27,0 %, p < 0,001). Les hospitalisations en raison d’un événement cardiovasculaire (n = 20 609) ont décliné de 48,1 % (IC à 95 % : 40,4 % – 54,9 %, p < 0,001). Le taux de mortalité hospitalière parmi les patients admis dans des établissements de soins secondaires a augmenté de 55,1 % (IC à 95 % : 10,1 % – 118 %, p = 0,013). Une baisse de 20,4 à 44,0 % du nombre d’interventions chirurgicales ou interventionnelles visant à prendre en charge un événement cardiovasculaire a également été enregistrée. Le nombre d’arrêts cardiaques survenus en milieu extrahospitalier (n = 5 528) est passé d’une moyenne mensuelle de 115 ± 15 à 136 ± 14, à compter de mai 2020. La mortalité des patients ambulatoires en attente d’une intervention cardiaque (n = 14 083) a augmenté, passant de 0,16 % (n = 12 501) à 2,49 % (n = 361) pendant la période de pandémie (p < 0,0001). Conclusions: Cette étude révèle une augmentation de la morbidité et de la mortalité cardiovasculaires durant le maintien des restrictions liées à la COVID-19 dans une région où le fardeau associé à cette maladie est faible. À mesure que le système de santé se rétablit ou affronte les vagues subséquentes de COVID-19, ces résultats devraient éclairer les communications au public concernant les symptômes cardiovasculaires et orienter la politique de prestation de soins cardiovasculaires.

IntroductionThis study investigated the effects of total cholesterol levels on prevalent, and incident suicidal behaviors according to age group (

Mariana Pinto da Costa and Robert Stewart provide commentary on a large prospective panel survey of mental health during the pandemic and consider the implications of such data science initiatives.

Objective To examine the prevalence of HIV in a cohort of people who have used secondary mental health services in the UK.Design Retrospective cohort study.Setting Routinely collected clinical data from secondary mental health services in South London, UK available for research through the Clinical Record Interactive Search tool at the National Institute for Health and Care Research Maudsley Biomedical Research Centre were matched with pseudonymised national HIV surveillance data held by the UK Health Security Agency using a deterministic matching algorithm.Participants All adults aged 16+ who presented for the first time to mental health services in the South London and Maudsley (SLaM) National Health Service Trust between 1 January 2007 and 31 December 2018 were included.Primary outcome Point prevalence of HIV.Results There were 181 177 people who had contact with mental health services for the first time between 2007 and 2018 in SLaM. Overall, 2.47% (n=4481) of those had a recorded HIV diagnosis in national HIV surveillance data at any time (before, during or after contact with mental health services), 24.73 people per 1000. HIV point prevalence was highest in people with a diagnosed substance use disorder at 3.77% (n=784). A substantial percentage of the sample did not have a formal mental health diagnosis (27%), but even with those excluded, the point prevalence remained high at 2.31%. Around two-thirds of people had their diagnosis of HIV before contact with mental health services (67%; n=1495).Conclusions The prevalence of HIV in people who have had contact with mental health services was approximately 2.5 times higher than the general population in the same geographical area. Future work should investigate risk factors and disparities in HIV outcomes between those with and without mental health service contact.

BackgroundAccurate recognition and recording of intellectual disability in those who are admitted to general hospitals is necessary for making reasonable adjustments, ensuring equitable access, and monitoring quality of care. In this study, we determined the rate of recording of intellectual disability in those with the condition who were admitted to hospital and factors associated with the condition being unrecorded.Methods and findingsRetrospective cohort study using 2 linked datasets of routinely collected clinical data in England. We identified adults with diagnosed intellectual disability in a large secondary mental healthcare database and used general hospital records to investigate recording of intellectual disability when people were admitted to general hospitals between 2006 and 2019. Trends over time and factors associated with intellectual disability being unrecorded were investigated. We obtained data on 2,477 adults with intellectual disability who were admitted to a general hospital in England at least once during the study period (total number of admissions = 27,314; median number of admissions = 5). People with intellectual disability were accurately recorded as having the condition during 2.9% (95% CI 2.7% to 3.1%) of their admissions. Broadening the criteria to include a nonspecific code of learning difficulty increased recording to 27.7% (95% CI 27.2% to 28.3%) of all admissions. In analyses adjusted for age, sex, ethnicity, and socioeconomic deprivation, having a mild intellectual disability and being married were associated with increased odds of the intellectual disability being unrecorded in hospital records. We had no measure of quality of hospital care received and could not relate this to the presence or absence of a record of intellectual disability in the patient record.ConclusionsRecognition and recording of intellectual disability in adults admitted to English general hospitals needs to be improved. Staff awareness training, screening at the point of admission, and data sharing between health and social care services could improve care for people with intellectual disability.

Native Americans are disproportionately affected by the criminal legal system, yet comparative analyses of criminal legal outcomes and experiences among racial and ethnic groups rarely center the experiences of Native Americans. This multimethod study examines how monetary sanctions are affecting Native American populations in Minnesota. Drawing on administrative criminal court data and qualitative fieldwork, we find that Native Americans are subject to among the largest overall legal financial obligations (LFOs) in criminal court and carry the largest average LFO debt loads relative to other racial and ethnic groups in Minnesota, particularly when proximal to tribal lands. Moreover, monetary sanctions exacerbate existing poverty and spatial isolation in rural areas, compounding and further entrenching historical, systemic disadvantages that Native communities already face. We contextualize these findings within the broader history of U.S. settler colonialism, resource extraction, and dispossession.

Abstract Despite the recognized antidepressant role of serotonin (5-hydroxytryptamine [5-HT]) signaling pathways in the central nervous system, the association between baseline peripheral 5-HT level and the antidepressant treatment response in clinical studies remains debatable. We investigated the interaction effects of baseline serum 5-HT level and age on the 12-week remission in outpatients with depressive disorders who received stepwise antidepressant treatment. Baseline serum serotonin levels were measured and the age of 1094 patients recorded. The patients received initial antidepressant monotherapy; then, patients with an insufficient response or who experienced uncomfortable side effects received alternative treatments every 3 weeks (3, 6, and 9 weeks). Subsequently, 12-week remission, defined as a Hamilton Depression Rating Scale (HAMD) score of ≤ 7, was evaluated. Individual and interaction effects of serum 5-HT level (as a binary [low vs. high, based on the median value of 72.6 ng/mL] or continuous variable) and age (as a binary [

Background The mental wellbeing implications of a prostate cancer diagnosis are increasingly being realised. Significant mental health symptoms such as depression and anxiety, along with related constructs such as fear of cancer recurrence, body image and masculine self-esteem issues are prevalent. However, less is understood about potential prognostic factors for these outcomes in prostate cancer patients. Therefore, this study aims to primarily explore potential treatment, patient and oncological factors associated with mental wellbeing outcomes in the initial prostate cancer follow-up period. Methods MIND-P is a multi-institutional prospective cohort study recruiting newly diagnosed prostate cancer patients for 12-month follow up. It will aim to recruit a final sample of 300 participants undergoing one of four treatment options: active surveillance, radical prostatectomy, radical radiotherapy, or hormone monotherapy. Questionnaire-based data collection consists of multiple validated mental, physical, and social wellbeing outcomes at baseline and 3-monthly intervals until study completion. Primary analysis will include evaluation of treatment undergone against multiple mental wellbeing outcomes. Secondary analysis will additionally explore multiple patient and oncological prognostic factors of potential importance, along with the cumulative incidence of these outcomes, symptom trajectory and their association with subsequent functional and social outcomes. Conclusion This cohort study aims to add to the existing limited literature evaluating significant prognostic factors for multiple mental wellbeing outcomes in newly diagnosed prostate cancer patients. This may be of potential use for guiding future prognosis research and of clinical use for identifying individuals potentially requiring additional surveillance or support during routine cancer follow up. Study registration This study was prospectively registered on ClinicalTrials.gov (NCT04647474).

Background Dementia with Lewy bodies (DLBs) is a common cause of dementia but has higher mortality than Alzheimer’s disease (AD). The reasons for this are unclear, but antidementia drugs (including acetylcholinesterase inhibitors [AChEIs] and memantine) symptomatically benefit people with DLB and might improve outcomes. We investigated whether AChEIs and/or memantine were associated with reduced hospital admissions and mortality. Methods and findings We performed a retrospective cohort study of those diagnosed with DLB between 1 January 2005 and 31 December 2019, using data from electronic clinical records of secondary care mental health services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), United Kingdom (catchment area population approximately 0.86 million), as well as linked records from national Hospital Episode Statistics (HES) data. Eligible patients were those who started AChEIs or memantine within 3 months of their diagnosis (cases) and those who never used AChEIs or memantine (controls). Outcomes included admission, length of stay, and mortality. Cox proportional hazard and linear regression models were used. Of 592 patients with DLB, 219 never took AChEIs or memantine, 100 took AChEIs only, and 273 took both AChEIs and memantine. The cohorts were followed up for an average of 896 days, 981 days, and 1,004 days, respectively. There were no significant differences in the cohorts’ baseline characteristics, except for socioeconomic status that was lower in patients who never took AChEIs or memantine (χ2 = 23.34, P = 0.003). After controlling for confounding by sociodemographic factors (age, sex, marital status, ethnicity, socioeconomic status), antipsychotic use, antidepressant use, cognitive status, physical comorbidity, anticholinergic burden, and global health performance, compared with patients who never took AChEIs or memantine, patients taking AChEIs only or taking both had a significantly lower risk of death (adjusted hazard ratio (HR) = 0.67, 95% CI = 0.48 to 0.93, p = 0.02; adjusted HR = 0.64, 95% CI = 0.50 to 0.83, P = 0.001, respectively). Those taking AChEIs or both AChEIs and memantine had significantly shorter periods of unplanned hospital admission for physical disorders (adjusted coefficient −13.48, 95% CI = [−26.87, −0.09], P = 0.049; adjusted coefficient −14.21, 95% CI = [−24.58, −3.85], P = 0.007, respectively), but no difference in length of stay for planned admissions for physical disorders, or for admissions for mental health disorders. No significant additional associations of memantine on admission, length of stay, and mortality were found (all P > 0.05). The main limitation was that this was a naturalistic study and possible confounds cannot be fully controlled, and there may be selection bias resulting from nonrandom prescription behaviour in clinical practice. However, we mimicked the intention-to-treat design of clinical trials, and the majority of baseline characters were balanced between cohorts. In addition, our series of sensitivity analyses confirmed the consistency of our results. Conclusion In this study, we observed that use of AChEIs with or without memantine in DLB was associated with shorter duration of hospital admissions and decreased risk of mortality. Although our study was naturalistic, it supports further the use of AChEIs in DLB. Author summary Why was this study done? Compared to Alzheimer’s disease (AD), dementia with Lewy bodies (DLBs) is associated with accelerated cognitive decline, lower quality of life, higher caregiver burden, shorter lifespan, and higher costs of care, as well as increased rates of admission to general hospitals and residential care and longer length of stay. Although there are no disease-modifying medications for DLB, there is evidence suggesting that acetylcholinesterase inhibitors (AChEIs) and glutamate N-methyl-D-aspartate receptor antagonists (specifically, memantine) are efficacious in DLB, though an impact on mortality is not clear. Distinct evidence on any survival benefit of AChEIs and memantine in DLB needs to be explored specifically, as DLB is pathologically and clinically different from AD and other forms of dementia. What did the researchers do and find? We identified 592 patients with DLB, including 219 who never took AChEIs or memantine, 100 who took AChEIs only, and 273 who took both AChEIs and memantine, between 1 January 2005 and 31 December 2019, using data from electronic clinical records of secondary care mental health services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), United Kingdom and linked records from national Hospital Episode Statistics (HES) data. We investigated the associations of antidementia drug use in DLB with hospital admissions, length of stay, and mortality. We found that taking AChEIs alone or with memantine was associated with a significantly reduced risk of death and shorter hospital stays after unplanned admissions for physical disorders. Taking AChEIs alone or with memantine was not associated with the probability of admission for mental disorders or physical disorders (planned or unplanned), or the length of stay after admission for mental disorders or planned admission for physical disorders, compared to those not taking AChEIs or memantine. We found no significant additional association of memantine with admission, length of stay, or mortality, compared to those taking AChEI therapy alone. What do these findings mean? We observed that AChEIs (with or without memantine) may be associated with a shorter length of stay associated with unplanned hospital admissions for physical disorders and reduced risk of mortality, warranting further study. The findings in our study may help to address potential concerns that AChEIs could have the opposite effect (to increase mortality in DLB), thus giving both clinicians and people with DLB more evidence to support treatment decisions. The main limitation was that as a naturalistic study, possible confounds cannot be fully controlled and there may be selection bias resulting from nonrandom prescription behaviour in clinical practice. However, we mimicked the intention-to-treat design of clinical trials, and our series of sensitivity analyses confirmed the consistency of our results.

Objectives Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood disorder, but often goes unrecognised and untreated. To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.Results Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). Precision-recall curve analyses were less favourable. Sociodemographic biases were effectively reduced by a fair pre-processing algorithm without loss of accuracy.Conclusions ML approaches using linked routinely collected education and health data offer accurate, low-cost and scalable prediction models of ADHD. These approaches could help identify areas of need and inform resource allocation. Introducing ‘fairness weighting’ attenuates some sociodemographic biases which would otherwise underestimate ADHD risk within minority groups.

Abstract Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder’s heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.

Abstract Inflammation is potentially associated with poor antidepressant treatment outcomes. Pro-inflammatory cytokines are influenced by hazardous alcohol consumption. The aim of the present study was to investigate the effects of the serum tumor necrosis factor-α (sTNF-α) level on antidepressant treatment outcomes in terms of the 12-week and 12-month remission rates and 24-month relapse rate, and to investigate the potential modifying effects of alcohol consumption on these associations in patients with depressive disorders. At baseline, sTNF-α was measured and alcohol-related data from the Alcohol Use Disorders Identification Test (AUDIT) and consumption history were collected from 1094 patients. Patients received stepwise antidepressant treatment. Remission at 12 weeks and 12 months was defined as a Hamilton Depression Rating Scale (HAMD) score ≤ 7. Relapse (HAMD score ≥ 14) was identified until 24 months for those who had initially responded (HAMD score 8 or current drinking). Significant interactions were found for the 12-month non-remission and relapse rates, although the interaction was not statistically significant for 12-week remission. In conclusion, baseline sTNF-α levels may be a useful predictor for both short- and long-term antidepressant treatment outcomes, and the consideration of alcohol consumption status may increase predictability, in particular for long-term outcomes.

Abstract Increased prevalence of sleep disorders has been found in patients with functional dyspepsia; however, direction of causality remains unclear. Our aim was to compare the risk of incident functional dyspepsia between patients with and without sleep disturbance from a large population-based sample. Utilizing a nation-wide health insurance administrative dataset, we assembled an 11-year historic cohort study to compare subsequent incidence of diagnosed functional dyspepsia between adult patients with any diagnosis of sleep disturbance and age- and gender-matched controls. Hazard ratios adjusted for other relevant comorbidities and medications were calculated using Cox regression models. 45,310 patients with sleep disorder and 90,620 controls were compared. Patients with sleep apnea had a 3.3-fold (95% confidence interval: 2.82 ~ 3.89) increased hazard of functional dyspepsia compared with controls. This increased risk persisted regardless of previously diagnosed depression coexisted. Sleep disturbance was associated with an increased risk of subsequent functional dyspepsia. Potential mechanisms are discussed.

Background People with schizophrenia have a high premature mortality risk. Obesity is a key potential underlying risk factor that is relatively unevaluated to date.Aims In this study, we investigated the associations of routinely recorded body size with all-cause mortality and deaths from common causes in a large cohort of people with schizophrenia spectrum disorders.Methods We assembled a retrospective observational cohort using data from a large mental health service in South London. We followed all patients over the age of 18 years with a clinical diagnosis of schizophrenia spectrum disorders from the date of their first recorded body mass index (BMI) between 1 January 2007 and 31 March 2018.Results Of 11 900 patients with a BMI recording, 1566 died. The Cox proportional hazards regression models, after adjusting for sociodemographic, socioeconomic variables and comorbidities, indicated that all-cause mortality was only associated with underweight status compared with healthy weight status (hazard ratio (HR): 1.33, 95% confidence interval (CI): 1.01 to 1.76). Obesity (HR: 1.24, 95% CI: 1.01 to 1.52) and morbid obesity (HR: 1.54, 95% CI: 1.03 to 2.42) were associated with all-cause mortality in the 18–45 years age range, and obesity was associated with lower risk (HR: 0.66, 95% CI: 0.50 to 0.87) in those aged 65+ years. Cancer mortality was raised in underweight individuals (HR: 1.93, 95% CI: 1.03 to 4.10) and respiratory disease mortality raised in those with morbid obesity (HR: 2.17, 95% CI: 1.02 to 5.22).Conclusions Overall, being underweight was associated with higher mortality in this disorder group; however, this was potentially accounted for by frailty in older age groups, and obesity was a risk factor for premature mortality in younger ages. The impact of obesity on life expectancy for people with schizophrenia spectrum disorders is clear from our findings. A deeper biological understanding of the relationship between these diseases and schizophrenia will help improve clinical practice.