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

Unipolar mania: Identification and characterisation of cases in France and the United Kingdom

BACKGROUND

Unipolar mania is a putative subtype of bipolar disorder (BD) in which individuals experience recurrent manic but not major depressive episodes. Few studies of unipolar mania have been conducted in developed countries and none in the UK. This study aimed to identify and characterise people with unipolar mania in the UK and France.

METHODS

People with unipolar mania were ascertained using a South London UK electronic case register and a French BD case series. Each unipolar mania group was compared to a matched group of people with BD who have experienced depressive episodes.

RESULTS

17 people with unipolar mania were identified in South London and 13 in France. The frequency of unipolar mania as a percentage of the BD clinical population was 1.2% for the South London cohort and 3.3% for the French cohort. In both cohorts, people with unipolar mania experienced more manic episodes than people with BD, and in the French cohort were more likely to experience a psychotic illness onset and more psychiatric admissions. Treatment and self-harm characteristics of people with unipolar mania were similar to people with BD.

LIMITATIONS

The relatively small number of people with unipolar mania identified by this study limits its power to detect differences in clinical variables.

CONCLUSIONS

People with unipolar mania can be identified in France and the UK, and they may experience a higher frequency of manic episodes but have similar treatment and self-harm characteristics as people with BD.

Mental health-related conversations on social media and crisis episodes: a time-series regression analysis

We aimed to investigate whether daily fluctuations in mental health-relevant Twitter posts are associated with daily fluctuations in mental health crisis episodes. We conducted a primary and replicated time-series analysis of retrospectively collected data from Twitter and two London mental healthcare providers. Daily numbers of ‘crisis episodes’ were defined as incident inpatient, home treatment team and crisis house referrals between 2010 and 2014. Higher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day crisis episodes for both sites. After adjusting for temporal trends, seven-day lagged analyses showed significant positive associations on day 1, changing to negative associations by day 4 and reverting to positive associations by day 7. There was a 15% increase in crisis episodes on days with above-median schizophrenia-related Twitter posts. A temporal association was thus found between Twitter-wide mental health-related social media content and crisis episodes in mental healthcare replicated across two services. Seven-day associations are consistent with both precipitating and longer-term risk associations. Sizes of effects were large enough to have potential local and national relevance and further research is needed to evaluate how services might better anticipate times of higher risk and identify the most vulnerable groups.

Mental health in UK Biobank - development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis

BACKGROUND

UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.

AIMS

Describe the development, implementation and results of this questionnaire.

METHOD

An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.

RESULTS

A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45-82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

CONCLUSIONS

The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

Association of physical health multimorbidity with mortality in people with schizophrenia spectrum disorders: Using a novel semantic search system that captures physical diseases in electronic patient records

OBJECTIVE

Single physical comorbidities have been associated with the premature mortality in people with schizophrenia-spectrum disorders (SSD). We investigated the association of physical multimorbidity (≥two physical health conditions) with mortality in people with SSD.

METHODS

A retrospective cohort study between 2013 and 2017. All people with a diagnosis of SSD (ICD-10: F20-F29), who had contact with secondary mental healthcare within South London during 2011-2012 were included. A novel semantic search system captured conditions from electronic mental health records, and all-cause mortality were retrieved. Hazard ratios (HRs) and population attributable fractions (PAFs) were calculated for associations between physical multimorbidity and all-cause mortality.

RESULTS

Among the 9775 people with SSD (mean (SD) age, 45.9 (15.4); males, 59.3%), 6262 (64%) had physical multimorbidity, and 880 (9%) died during the 5-year follow-up. The top three physical multimorbidity combinations with highest mortality were cardiovascular-respiratory (HR: 2.23; 95% CI, 1.49-3.32), respiratory-skin (HR: 2.06; 95% CI, 1.31-3.24), and respiratory-digestive (HR: 1.88; 95% CI, 1.14-3.11), when adjusted for age, gender, and all other physical disease systems. Combinations of physical diseases with highest PAFs were cardiovascular-respiratory (PAF: 35.7%), neurologic-respiratory (PAF: 32.7%), as well as respiratory-skin (PAF: 29.8%).

CONCLUSIONS

Approximately 2/3 of patients with SSD had physical multimorbidity and the risk of mortality in these patients was further increased compared to those with none or single physical conditions. These findings suggest that in order to reduce the physical health burden and subsequent mortality in people with SSD, proactive coordinated prevention and management efforts are required and should extend beyond the current focus on single physical comorbidities.

Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed

BACKGROUND

Natural language processing (NLP) is an important traditional field in computer science, but its application in medical research has faced many challenges. With the extensive digitalization of medical information globally and increasing importance of understanding and mining big data in the medical field, NLP is becoming more crucial.

OBJECTIVE

The goal of the research was to perform a systematic review on the use of NLP in medical research with the aim of understanding the global progress on NLP research outcomes, content, methods, and study groups involved.

METHODS

A systematic review was conducted using the PubMed database as a search platform. All published studies on the application of NLP in medicine (except biomedicine) during the 20 years between 1999 and 2018 were retrieved. The data obtained from these published studies were cleaned and structured. Excel (Microsoft Corp) and VOSviewer (Nees Jan van Eck and Ludo Waltman) were used to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods.

RESULTS

A total of 3498 articles were obtained during initial screening, and 2336 articles were found to meet the study criteria after manual screening. The number of publications increased every year, with a significant growth after 2012 (number of publications ranged from 148 to a maximum of 302 annually). The United States has occupied the leading position since the inception of the field, with the largest number of articles published. The United States contributed to 63.01% (1472/2336) of all publications, followed by France (5.44%, 127/2336) and the United Kingdom (3.51%, 82/2336). The author with the largest number of articles published was Hongfang Liu (70), while Stéphane Meystre (17) and Hua Xu (33) published the largest number of articles as the first and corresponding authors. Among the first author's affiliation institution, Columbia University published the largest number of articles, accounting for 4.54% (106/2336) of the total. Specifically, approximately one-fifth (17.68%, 413/2336) of the articles involved research on specific diseases, and the subject areas primarily focused on mental illness (16.46%, 68/413), breast cancer (5.81%, 24/413), and pneumonia (4.12%, 17/413).

CONCLUSIONS

NLP is in a period of robust development in the medical field, with an average of approximately 100 publications annually. Electronic medical records were the most used research materials, but social media such as Twitter have become important research materials since 2015. Cancer (24.94%, 103/413) was the most common subject area in NLP-assisted medical research on diseases, with breast cancers (23.30%, 24/103) and lung cancers (14.56%, 15/103) accounting for the highest proportions of studies. Columbia University and the talents trained therein were the most active and prolific research forces on NLP in the medical field.

Predicting high-cost care in a mental health setting

BACKGROUND

The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.

AIMS

We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.

METHOD

We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves.

RESULTS

In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12-46%).

CONCLUSIONS

EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.

Predictors of cerebrovascular event reoccurrence in patients with depression: a retrospective cohort study

OBJECTIVES

To identify predictors of recurrent cerebrovascular morbidity in a cohort of patients with depression and a cerebrovascular disease (CBVD) history.

METHODS

We used the Maudsley Biomedical Research Centre Case Register to identify patients aged 50 years or older with a diagnosis of depressive disorder between 2008 and 2017 and a previous history of hospitalised CBVD. Using depression diagnosis as the index date we followed patients until first hospitalised CBVD recurrence or death due to CBVD. Sociodemographic data, symptom and functioning scores of Health of the Nation Outcome Scales, medications and comorbidities were extracted and modelled in multivariate survival analyses to identify predictors of CBVD reoccurrence.

RESULTS

Of 1292 patients with depression and CBVD (mean age 75.6 years; 56.6% female), 264 (20.4%) experienced fatal/non-fatal CBVD recurrence during a median follow-up duration of 1.66 years. In multivariate Cox regression models, a higher risk of CBVD recurrence was predicted by older age (HR, 1.02; 95% CI, 1.01 to 1.04) (p=0.002), physical health problems (moderate to severe HR, 2.47; 95% CI, 1.45 to 4.19) (p=0.001), anticoagulant (HR, 1.40; 95% CI, 1.01 to 1.93) (p=0.041) and antipsychotic medication (HR, 0.66; 95% CI 0.44 to 0.99) (p=0.047). Neither depression severity, mental health symptoms, functional status, nor antidepressant prescribing were significantly associated with CBVD recurrence.

CONCLUSIONS

Approximately one in five patients with depression and CBVD experienced a CBVD recurrence over a median follow-up time of 20 months. Risk of CBVD recurrence was largely dependent on age and physical health rather than on severity of depressive symptoms, co-morbid mental health or functional problems, or psychotropic prescribing.

Co-occurring obsessive-compulsive disorder and autism spectrum disorder in young people: prevalence, clinical characteristics and outcomes

Obsessive-compulsive disorder (OCD) and autism spectrum disorders (ASD) commonly co-occur and are considered challenging to manage when they co-occur in youth. However, clinical characteristics and prognosis of this group remain poorly understood. This study examined the prevalence, clinical correlates and outcomes of paediatric OCD co-occurring with ASD (OCD + ASD) in a large clinical cohort. Data were extracted from electronic clinical records of young people aged 4-17 years who had attended a mental health trust in South London, United Kingdom. We identified young people with diagnoses of OCD + ASD (n = 335), OCD without ASD (n = 1010), and ASD without OCD (n = 6577). 25% of youth with OCD had a diagnosis of ASD, while 5% of those with ASD had a diagnosis of OCD. At diagnosis, youth with OCD + ASD had lower psychosocial functioning scores on the clinician-rated Child Global Assessment Scale (CGAS) compared to those with either OCD or ASD. Youth with OCD + ASD were equally likely to receive CBT compared to those with OCD but were more likely to be prescribed medication and use services for longer than either comparison group. Youth with OCD + ASD showed significant improvements in functioning (CGAS scores) after service utilisation but their gains were smaller than those with OCD. OCD + ASD commonly co-occur, conferring substantial impairment, although OCD may be underdiagnosed in youth with ASD. Young people with co-occurring OCD + ASD can make significant improvements in functioning with routine clinical care but are likely to remain more impaired than typically developing youth with OCD, indicating a need for longer-term support for these young people.

Diagnostic trajectories in child and adolescent mental health services: exploring the prevalence and patterns of diagnostic adjustments in an electronic mental health case register

Community-based epidemiological studies show transitions between psychiatric disorders are common during child development. However, little research has explored the prevalence or patterns of the diagnostic adjustments that occur in child and adolescent mental health services (CAMHS). Understanding diagnostic trajectories is necessary to inform theory development in developmental psychopathology and clinical judgements regarding risk and prognosis. In this study, data from CAMHS clinical records were extracted from a British mental health case register (N = 12,543). Analysis calculated the proportion of children whose clinical records showed a longitudinal diagnostic adjustment (i.e. addition of a subsequent diagnosis of a different diagnostic class, at > 30 days' distance from their first diagnosis). Regression analyses investigated typical diagnostic sequences and their relationships with socio-demographic variables, service use and standardised measures of mental health. Analysis found that 19.3% of CAMHS attendees had undergone a longitudinal diagnostic adjustment. Ethnicity, diagnostic class and symptom profiles significantly influenced the likelihood of a diagnostic adjustment. Affective and anxiety/stress-related disorders longitudinally predicted each other, as did hyperkinetic and conduct disorders, and hyperkinetic and pervasive developmental disorders. Results suggest that approximately one in five young service users have their original psychiatric diagnosis revised or supplemented during their time in CAMHS. By revealing the most common diagnostic sequences, this study enables policy makers to anticipate future service needs and clinicians to make informed projections about their patients' likely trajectories. Further research is required to understand how young people experience diagnostic adjustments and their psychological and pragmatic implications.

Fear N. Identifying Veterans Using Electronic Health Records in the United Kingdom: A Feasibility Study

There is a lack of quantitative evidence concerning UK (United Kingdom) Armed Forces (AF) veterans who access secondary mental health care services-specialist care often delivered in high intensity therapeutic clinics or hospitals-for their mental health difficulties. The current study aimed to investigate the utility and feasibility of identifying veterans accessing secondary mental health care services using National Health Service (NHS) electronic health records (EHRs) in the UK. Veterans were manually identified using the Clinical Record Interactive Search (CRIS) system-a database holding secondary mental health care EHRs for an NHS Trust in the UK. We systematically and manually searched CRIS for veterans, by applying a military-related key word search strategy to the free-text clinical notes completed by clinicians. Relevant data on veterans' socio-demographic characteristics, mental disorder diagnoses and treatment pathways through care were extracted for analysis. This study showed that it is feasible, although time consuming, to identify veterans through CRIS. Using the military-related key word search strategy identified 1600 potential veteran records. Following manual review, 693 (43.3%) of these records were verified as "probable" veterans and used for analysis. They had a median age of 74 years (interquartile range (IQR): 53-86); the majority were male (90.8%) and lived alone (38.0%). The most common mental diagnoses overall were depressive disorders (22.9%), followed by alcohol use disorders (10.5%). Differences in care pathways were observed between pre and post national service (NS) era veterans. This feasibility study represents a first step in showing that it is possible to identify veterans through free-text clinical notes. It is also the first to compare veterans from pre and post NS era.

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

BACKGROUND

Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results.

OBJECTIVE

The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records.

METHODS

We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste," which collectively impede efficient model reuse. We propose a phenotype embedding-based approach to minimize these sources of waste without the need for labelled data from new settings.

RESULTS

We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in "blind" model-adaptation approaches.

CONCLUSIONS

Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

Patterns of use of the Mental Health Act 1983, from 2007-2008 to 2016-2017, in two major London secondary mental healthcare providers

Trends in detention under the Mental Health Act 1983 in two major London secondary mental healthcare providers were explored using patient-level data in a historical cohort study between 2007-2008 and 2016-2017. An increase in the number of detention episodes initiated per fiscal year was observed at both sites. The rise was accompanied by an increase in the number of active patients; the proportion of active patients detained per year remained relatively stable. Findings suggest that the rise in the number of detentions reflects the rise of the number of people receiving secondary mental healthcare.

Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death

BACKGROUND

Free text in electronic health records (EHR) may contain additional phenotypic information beyond structured (coded) information. For major health events - heart attack and death - there is a lack of studies evaluating the extent to which free text in the primary care record might add information. Our objectives were to describe the contribution of free text in primary care to the recording of information about myocardial infarction (MI), including subtype, left ventricular function, laboratory results and symptoms; and recording of cause of death. We used the CALIBER EHR research platform which contains primary care data from the Clinical Practice Research Datalink (CPRD) linked to hospital admission data, the MINAP registry of acute coronary syndromes and the death registry. In CALIBER we randomly selected 2000 patients with MI and 1800 deaths. We implemented a rule-based natural language engine, the Freetext Matching Algorithm, on site at CPRD to analyse free text in the primary care record without raw data being released to researchers. We analysed text recorded within 90 days before or 90 days after the MI, and on or after the date of death.

RESULTS

We extracted 10,927 diagnoses, 3658 test results, 3313 statements of negation, and 850 suspected diagnoses from the myocardial infarction patients. Inclusion of free text increased the recorded proportion of patients with chest pain in the week prior to MI from 19 to 27%, and differentiated between MI subtypes in a quarter more patients than structured data alone. Cause of death was incompletely recorded in primary care; in 36% the cause was in coded data and in 21% it was in free text. Only 47% of patients had exactly the same cause of death in primary care and the death registry, but this did not differ between coded and free text causes of death.

CONCLUSIONS

Among patients who suffer MI or die, unstructured free text in primary care records contains much information that is potentially useful for research such as symptoms, investigation results and specific diagnoses. Access to large scale unstructured data in electronic health records (millions of patients) might yield important insights.

Causes of hospital admission and mortality among 6683 people who use heroin: A cohort study comparing relative and absolute risks

BACKGROUND

Mortality in high-risk groups such as people who use illicit drugs is often expressed in relative terms such as standardised ratios. These measures are highest for diseases that are rare in the general population, such as hepatitis C, and may understate the importance of common long-term conditions.

POPULATION

6683 people in community-based treatment for heroin dependence between 2006 and 2017 in London, England, linked to national hospital and mortality databases with 55,683 years of follow-up.

METHOD

Age- and sex-specific mortality and hospital admission rates in the general population of London were used to calculate the number of expected events. We compared standardised ratios (relative risk) to excess deaths and admissions (absolute risk) across ICD-10 chapters and subcategories.

RESULTS

Drug-related diseases had the highest relative risks, with a standardised mortality ratio (SMR) of 48 (95% CI 42-54) and standardised admission ratio (SAR) of 293 (95% CI 282-304). By contrast, other diseases had an SMR of 4.4 (95% CI 4.0-4.9) and an SAR of 3.15 (95% CI 3.11-3.19). However, the majority of the 621 excess deaths (95% CI 569-676) were not drug-related (361; 58%). The largest groups were liver disease (75 excess deaths) and COPD (45). Similarly, 80% (11,790) of the 14,668 excess admissions (95% CI 14,382-14,957) were not drug-related. The largest groups were skin infections (1073 excess admissions), alcohol (1060), COPD (812) and head injury (612).

CONCLUSIONS

Although relative risks of drug-related diseases are very high, most excess morbidity and mortality in this cohort was caused by common long-term conditions.

Mental health in South East London general hospitals: using electronic patient records to explore associations between psychiatric diagnoses and length of stay in a patient cohort receiving liaison psychiatry input

BACKGROUND

Psychiatric illnesses are prevalent in general hospitals and associated with length of stay (LOS). Liaison psychiatry teams provide psychiatric care in acute hospitals and can improve mental health-related outcomes but, to achieve ambitious policy targets, services must understand local need.

AIMS

Using electronic patient records, we investigate associations between psychiatric diagnoses and LOS in South East London hospitals.

METHOD

Patient records were extracted using the South London and Maudsley NHS Foundation Trust Biomedical Research Centre Case Register Interactive Search system. There were 6378 admissions seen by liaison psychiatry aged <65 years between 2011 and 2016. Linear mixed-effects models investigated the impact of psychiatric diagnoses on LOS. Potential confounders included medical diagnoses, gender, age, ethnicity, social deprivation, hospital site and investment per admission.

RESULTS

According to marginal means, longer LOS is associated with primary diagnoses of organic disorders (mean: 23 days, 95% CI 20.39-25.61), depressive disorders (mean: 11.03 days, 95% CI 9.74-25.61) and psychotic disorders (mean: 10.63 days, 95% CI 8.75-12.51). Shorter LOS is associated with personality disorders (mean: 6.28 days, 95% CI 4.12-8.45), bipolar affective disorders (mean 6.81 days, 95% CI 3.49-10.14) and substance-related problems (mean 7.53 days, 95% CI 6.01-9.05).

CONCLUSIONS

Psychiatric diagnoses have differential associations with in-patient LOS. Liaison psychiatry teams aim to mitigate the impact of psychiatric illness on patient and hospital outcomes but understanding local need and the wider context of care provision is needed to maximise potential benefits.

DECLARATION OF INTEREST

M.H. is a consultant liaison psychiatrist for King's College Hospital adult liaison psychiatry team. At the time of writing, H.T. was senior business manager at SLaM psychological medicine and integrated care clinical academic group. These may be considered financial and/or non-financial interests given the implications of findings for service funding.

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65-0.86) and 0.67 (0.55-0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.

Case-finding for common mental disorders in primary care using routinely collected data: a systematic review

PURPOSE

Case-finding for common mental disorders (CMD) in routine data unobtrusively identifies patients for mental health research. There is absence of a review of studies examining CMD-case-finding accuracy in routine primary care data. CMD-case definitions include diagnostic/prescription codes, signs/symptoms, and free text within electronic health records. This systematic review assesses evidence for case-finding accuracy of CMD-case definitions compared to reference standards.

METHODS

PRISMA-DTA checklist guided review. Eligibility criteria were outlined prior to study search; studies compared CMD-case definitions in routine primary care data to diagnostic interviews, screening instruments, or clinician judgement. Studies were quality assessed using QUADAS-2.

RESULTS

Fourteen studies were included, and most were at high risk of bias. Nine studies examined depressive disorders and seven utilised diagnostic interviews as reference standards. Receiver operating characteristic (ROC) planes illustrated overall variable case-finding accuracy across case definitions, quantified by Youden's index. Forest plots demonstrated most case definitions provide high specificity.

CONCLUSION

Case definitions effectively identify cases in a population with good accuracy and few false positives. For 100 anxiety cases, identified using diagnostic codes, between 12 and 20 will be false positives; 0-47 cases will be missed. Sensitivity is more variable and specificity is higher in depressive cases; for 100 cases identified using diagnostic codes, between 0 and 87 will be false positives; 4-18 cases will be missed. Incorporating context to case definitions may improve overall case-finding accuracy. Further research is required for meta-analysis and robust conclusions.

Understanding which people with dementia are at risk of inappropriate care and avoidable transitions to hospital near the end-of-life: a retrospective cohort study

BACKGROUND

transitions between care settings near the end-of-life for people with dementia can be distressing, lead to physical and cognitive deterioration, and may be avoidable.

OBJECTIVE

to investigate determinants of end-of-life hospital transitions, and association with healthcare use, among people with dementia.

DESIGN

retrospective cohort study.

SETTING

electronic records from a mental health provider in London, linked to national mortality and hospital data.

SUBJECTS

people with dementia who died in 2007-2016.

METHODS

end-of-life hospital transitions were defined as: multiple admissions in the last 90 days (early), or any admission in the last three days of life (late). Determinants were assessed using logistic regression.

RESULTS

of 8,880 people, 1,421 (16.0%) had at least one end-of-life transition: 505 (5.7%) had early, 788 (8.9%) late, and 128 (1.5%) both types. Early transitions were associated with male gender (OR 1.33, 95% CI 1.11-1.59), age (>90 vs <75 years OR 0.69, 95% CI 0.49-0.97), physical illness (OR 1.52, 95% CI 1.20-1.94), depressed mood (OR 1.49, 95% CI 1.17-1.90), and deprivation (most vs least affluent quintile OR 0.58, 95% CI 0.37-0.90). Care home residence was associated with fewer early (OR 0.63, 95% CI 0.53 to 0.76) and late (OR 0.80, 95% CI 0.65 to 0.97) transitions. Early transitions were associated with more hospital admissions throughout the last year of life compared to those with late and no transitions (mean 4.56, 1.89, 1.60; P < 0.001).

CONCLUSIONS

in contrast to late transitions, early transitions are associated with higher healthcare use and characteristics that are predictable, indicating potential for prevention.

How to measure the need for transition to adult services among young people with Attention Deficit Hyperactivity Disorder (ADHD): a comparison of surveillance versus case note review methods

BACKGROUND

Health services have not provided adequate support for young people with long term health conditions to transfer from child to adult services. National Institute of Health and Care (NICE) guidance on transition has been issued to address these gaps. However, data are often sparse about the number of young adults who might need to transition. Using Attention Deficit Hyperactivity Disorder (ADHD) as an exemplar, this study used an existing surveillance system and a case note review to capture the incidence of the transition process, and compared and contrasted the findings.

METHODS

The Child and Adolescent Psychiatry Surveillance System (CAPSS) was used to estimate the incident transition of young people with Attention Deficit Hyperactivity Disorder (ADHD) from child to adult services. This involves consultant child and adolescent psychiatrists from the United Kingdom (UK) and Republic of Ireland (ROI) reporting relevant young people as they are seen in clinics. In parallel, a case note review was conducted using the Maudsley Biomedical Research Centre (BRC) Clinical Records Interactive Search (CRIS). The study period ran for twelve months with a nine month follow up to see how the transition proceeded.

RESULTS

CRIS identified 76 cases in the study period, compared to 18 identified using surveillance via CAPSS. Methodological issues were experienced using both methods. Surveillance issues; eligibility criteria confusion, reporting errors, incomplete questionnaires, difficulties contacting clinicians, and surveillance systems do not cover non-doctors and psychiatrists who are not consultants. Case note review issues using CRIS included the need for researchers to interpret clinical notes, the availability and completeness of data in the notes, and data limited to the catchment of one particular mental health trust.

CONCLUSIONS

Both methods demonstrate strengths and weaknesses; the combination of both methods in the absence of strong routinely collected data, allowed a more robust estimate of the level of need for service planning and commissioning.

Depression and cause-specific mortality in an ethnically diverse cohort from the UK: 8-year prospective study

BACKGROUND

Depression is associated with increased mortality, however, little is known about its variation by ethnicity.

METHODS

We conducted a cohort study of individuals with ICD-10 unipolar depression from secondary mental healthcare, from an ethnically diverse location in southeast London, followed for 8 years (2007-2014) linked to death certificates. Age- and sex- standardised mortality ratios (SMRs), with the population of England and Wales as a standard population were derived. Hazard ratios (HRs) for mortality were derived through multivariable regression procedures.

RESULTS

Data from 20 320 individuals contributing 91 635 person-years at risk with 2366 deaths were used for analyses. SMR for all-cause mortality in depression was 2.55(95% CI 2.45-2.65), with similar trends by ethnicity. Within the cohort with unipolar depression, adjusted HR (aHRs) for all-cause mortality in ethnic minority groups relative to the White British group were 0.62(95% CI 0.53-0.74) (Black Caribbean), 0.53(95% CI 0.39-0.72) (Black African) and 0.69(95% CI 0.52-0.90) (South Asian). Male sex and alcohol/substance misuse were associated with an increased all-cause mortality risk [aHR:1.94 (95% CI 1.68-2.24) and aHR:1.18 (95% CI 1.01-1.37) respectively], whereas comorbid anxiety was associated with a decreased risk [aHR: 0.72(95% CI 0.58-0.89)]. Similar associations were noted for natural-cause mortality. Alcohol/substance misuse and male sex were associated with a near-doubling in unnatural-cause mortality risk, whereas Black Caribbean individuals with depression had a reduced unnatural-cause mortality risk, relative to White British people with depression.

CONCLUSIONS

Although individuals with depression experience an increased mortality risk, marked heterogeneity exists by ethnicity. Research and practice should focus on addressing tractable causes underlying increased mortality in depression.

Psychiatric comorbidity as a risk factor for the mortality of people with bulimia nervosa

BACKGROUND

Bulimia nervosa (BN) is associated with increased mortality. Frequent comorbidities of BN include substance use disorders, affective disorders and personality disorders (PD). These comorbidities may add an additional risk for mortality.

METHODS

We investigated the influence of these psychiatric comorbidities on all-cause mortality with demographic and socioeconomic factors considered as confounders over an observation period from January 2007 to March 2016 for 1501 people with BN using anonymised health records data from the South London and Maudsley NHS Foundation Trust (SLaM), retrieved through its Clinical Records Interactive Search (CRIS) data resource. Mortality was ascertained through monthly linkages to the nationwide tracing system administered by the Office for National Statistics (ONS). We used Cox proportional hazards regression to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable analyses were also performed to estimate effects when controlling for confounding of age, sex, ethnicity, borough, marital status and deprivation score.

RESULTS

A total of 18 patients with BN died during the observation period. The standardised mortality ratio (SMR) for our study cohort (against the population of England and Wales in 2012 as a standard) was 2.52 (95% CI 1.49-3.97). Cox regressions revealed significant associations of mortality with older age and male gender. Comorbid PD (HR: 3.36; 95% CI 1.05-10.73) was significantly associated with all-cause mortality, even after controlling for demographic and socioeconomic covariates.

CONCLUSIONS

These results highlight increased mortality in patients with BN and the importance of recognising and treating PDs in patients with BN.

Compulsory admission at first presentation to services for psychosis: does ethnicity still matter? Findings from two population-based studies of first episode psychosis

OBJECTIVES

Compared with the majority population, those from minority ethnic groups in the UK are more likely to be admitted compulsorily during a first episode of psychosis (FEP). We investigated whether these disparities in pathways in to care continue.

METHODS

We analysed data from two first episode psychosis studies, conducted in the same geographical area in south London 15 years apart: the Aetiology and Ethnicity in Schizophrenia and Other Psychosis (AESOP) and the Clinical Record Interactive Search-First Episode Psychosis (CRIS-FEP) studies. The inclusion/exclusion criteria for case ascertainment for first episode psychosis were identical across the two studies. We performed multivariable logistic regression to estimate odds of compulsory admission by ethnic group, controlling for confounders.

PARTICIPANTS

Two hundred sixty-six patients with first episode psychosis, aged 18-64 years, who presented to mental health services in south London in 1997-1999 and 446 with FEP who presented in 2010-2012.

RESULTS

When the two samples  were compared, ethnic differences in compulsory admission appear to have remained the same for black African patients, i.e. three times higher than white British in both samples: AESOP (adj. OR = 3.96; 95% CI = 1.80-8.71) vs. CRIS-FEP (adj. OR = 3.12; 95% CI = 1.52-6.35). Black Caribbean patients were three times more likely to be compulsorily admitted in AESOP (adj. OR = 3.20; 95% CI = 1.56-6.54). This was lower in the CRIS-FEP sample (adj. OR = 1.68; 95% CI = 0.71-3.98) and did not meet conventional levels for statistical significance.

CONCLUSION

Ethnicity is strongly associated with compulsory admissions at first presentation for psychosis with evidence of heterogeneity across groups, which deserves further research.

Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database

OBJECTIVES

To investigate recorded poor insight in relation to mental health and service use outcomes in a cohort with first-episode psychosis.

DESIGN

We developed a natural language processing algorithm to ascertain statements of poor or diminished insight and tested this in a cohort of patients with first-episode psychosis.

SETTING

The clinical record text at the South London and Maudsley National Health Service Trust in the UK was used.

PARTICIPANTS

We applied the algorithm to characterise a cohort of 2026 patients with first-episode psychosis attending an early intervention service.

PRIMARY AND SECONDARY OUTCOME MEASURES

Recorded poor insight within 1 month of registration was investigated in relation to (1) incidence of psychiatric hospitalisation, (2) odds of legally enforced hospitalisation, (3) number of days spent as a mental health inpatient and (4) number of different antipsychotic agents prescribed; outcomes were measured over varying follow-up periods from 12 months to 60 months, adjusting for a range of sociodemographic and clinical covariates.

RESULTS

Recorded poor insight, present in 48.9% of the sample, was positively associated with youngest and oldest age groups, unemployment and schizophrenia (compared with bipolar disorder) and was negatively associated with Asian ethnicity, married status, home ownership and recorded cannabis use. It was significantly associated with higher levels of all four outcomes over the succeeding 12 months. Associations with hospitalisation incidence and number of antipsychotics remained independently significant when measured over 60 and 48 months, respectively.

CONCLUSIONS

Recorded poor insight in people with recent onset psychosis predicted higher subsequent inpatient mental healthcare use. Improving insight might benefit patients' course of illness as well as reduce mental health service use.

Ethnic density and other neighbourhood associations for mortality in severe mental illness: a retrospective cohort study with multi-level analysis from an urbanised and ethnically diverse location in the UK

BACKGROUND

Neighbourhood social context might play a role in modifying mortality outcomes in severe mental illness, but has received little attention to date. Therefore, we aimed to assess in an ethnically diverse and urban location the association of neighbourhood-level characteristics and individual-level factors for all-cause, natural-cause, and unnatural-cause mortality in those with severe mental illness.

METHODS

We did a retrospective cohort study using a case-registry from a large secondary mental health-care Trust in an ethnically diverse and urban location in south London, UK. Linked data for deaths and areas of residence were identified from the case-registry. We included all individuals aged 15 years or more at the time of diagnosis for a severe mental illness from Jan 1, 2007, to Dec 31, 2014. We used individual-level information in our analyses, such as gender, marital status, and the presence of current or previous substance use disorders. We assessed neighbourhood or area-level indicators at the Lower Super Output Area level. Association of neighbourhood-level characteristics, which included the interaction between ethnicity and own ethnic density, deprivation, urbanicity, and social fragmentation, alongside individual-level factors for all-cause, natural-cause, and unnatural-cause mortality in those with severe mental illness was assessed.

FINDINGS

A total of 18 201 individuals were included in this cohort for analyses, with a median follow-up of 6·36 years. There were 1767 (9·7%) deaths from all causes, 1417 (7·8%) from natural causes, and 192 (1·1%) from unnatural causes. In the least ethnically dense areas, the adjusted rate ratio (aRR) for all-cause mortality in ethnic minority groups with severe mental illness compared with white British people with severe mental illness were similar (aRR 0·96, 95% CI 0·71-1·29); however in the highest ethnic density areas, ethnic minority groups with severe mental illness had a lower risk of death (aRR 0·52, 95% CI 0·38-0·71; p<0·0001), with similar trends for natural-cause mortality (p=0·071 for statistical interaction). In the cohort with severe mental illness, residency in deprived, urban, and socially fragmented neighbourhoods was not associated with higher mortality rates. Compared with the general population, age-standardised and gender-standardised mortality ratios were elevated in the cohort with severe mental illness across all neighbourhood-level characteristics assessed.

INTERPRETATION

For ethnic minority groups with severe mental illness, residency in areas of higher own-group ethnic density is associated with lower mortality compared to white British groups with severe mental illness.

FUNDING

Health Foundation, National Institute for Health Research, EU Seventh Framework, and National Institute of Mental Health.

Hospitalisation of people with dementia: evidence from English electronic health records from 2008 to 2016

Hospitalisation of people with dementia is associated with adverse outcomes and high costs. We aimed to examine general, i.e. non-psychiatric, hospitalisation rates, changes since 2008 and factors associated with admission. We also aimed to compare admission rates of people with dementia with age-matched people without dementia. We conducted a cohort study of adults ≥ 65 years, with dementia diagnosed during the 2008-2016 study window, derived from a large secondary mental healthcare database in South London, UK. We used national general hospital records to identify emergency and elective hospitalisations. We calculated the cumulative incidence and rate of hospitalisation and examined predictors of hospitalisation using negative binomial regression, with multiple imputation for missing covariate data. We calculated age-standardised admission ratio for people with dementia compared to those without. Of 10,137 people, 50.6% were admitted to hospital in the year following dementia diagnosis and 75.9% were admitted during median 2.5 years follow-up. Annual admission rate was 1.26/person-year of which 0.90/person-year were in emergency. Emergency hospitalisation rate increased throughout the study period. Compared to controls without diagnosed dementia in the catchment area, the age-standardised emergency admission ratio for people with dementia was 2.06 (95% CI 1.95, 2.18). Male, older, white and socio-economically deprived people and those with clinically significant comorbid physical illness, depressed mood, activity of daily living or living condition problems had more hospitalisations. Emergency hospitalisations of people with dementia are higher than those without, and increasing. Many factors associated with admission are social and psychological, and may be targets for future interventions that aim to reduce avoidable admissions.

Continuity of care and clinical outcomes in the community for people with severe mental illness

BACKGROUND

High continuity of care is prized by users of mental health services and lauded in health policy. It is especially important in long-term conditions like schizophrenia. However, it is not routinely measured, and therefore not often evaluated when service reorganisations take place. In addition, the impact of continuity of care on clinical outcomes is unclear.AimsWe set out to examine continuity of care in people with schizophrenia, and to relate this to demographic variables and clinical outcomes.

METHOD

Pseudoanonymised community data from 5552 individuals with schizophrenia presenting over 11 years were examined for changes in continuity of care using the numbers of community teams caring for them and the Modified Modified Continuity Index (MMCI). These and demographic variables were related to clinical outcomes measured with the Health of the Nation Outcome Scales (HoNOS). Data were analysed using generalised estimating equations and multivariate marginal models.

RESULTS

There was a significant decline in MMCI and significant worsening of HoNOS total scores over 11 years. Higher (worse) HoNOS scores were significantly and independently related to older age, later years and both lower MMCI and more teams caring for the individual in each year. Most HoNOS scales contributed to the higher total scores.

CONCLUSIONS

There is evidence of declining continuity of care in this 11-year study of people with schizophrenia, and of an independent effect of this on worse clinical outcomes. We suggest that this is related to reorganisation of services.Declaration of interestNone.

Invited commentary on… Continuity of care in the community for people with severe mental illness: does it matter?

In this issue, MacDonald et al have used data from the South London and Maudsley NHS Foundation Trust electronic patient record to investigate the relationship between service change, routine outcome data and 'continuity of care'. The period they have looked at was one of huge change in the configuration of services and the background to this is explored here.Declaration of interestF.H. was a clinical director of South London and Maudsley NHS Foundation Trust and its predecessor organisations from 1991 to 2010.

"Why have I not been told about this?": a survey of experiences of and attitudes to advance decision-making amongst people with bipolar

Background: The idea that people with severe mental illness should be able to plan in advance for periods of illness as a means of enhancing autonomy has been long debated and is increasingly being enshrined in codes of practice and mental health legislation. It has been argued that the ethical imperative for this is especially pronounced in bipolar (BP), a condition in which sufferers often experience episodic crises interspersed with periods of wellness. However, there is a paucity of published research investigating experiences of advance decision making (ADM) in people with BP or their attitudes towards it. Methods: An online survey of BPUK's mailing list was conducted. 932 people with BP completed the survey (response rate 5.61%). Descriptive statistics and regression analysis were conducted to compare experience of with attitudes towards ADM and variables associated with interest in ADM. Results: A majority indicated a desire to plan care in advance of losing capacity (88%) but most had not done so (64%). High numbers of respondents expressed a wish to request as well as refuse treatment and most wanted to collaborate with psychiatrists, including on issues around self-binding. The most frequent motivation to utilise ADM was a desire to be more involved in mental health decisions. Interest in self-binding was associated with experience of compulsory treatment and trust in mental health services. Interest in refusals of all medication was associated with younger age and lack of trust in mental health services. Interest in ADM in general was associated with younger age but not educational level, ethnicity or gender. Conclusions: This study demonstrates an appetite for ADM amongst people with bipolar that is independent of educational status and ethnicity. As states reform their mental health laws, attention needs to be given to the distinctive attitudes toward ADM amongst people with bipolar.

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system

OBJECTIVE

Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techniques to extract detailed clinical information from epilepsy clinic letters to enrich routinely collected data.

DESIGN

We used the general architecture for text engineering (GATE) framework to build an information extraction system, ExECT (extraction of epilepsy clinical text), combining rule-based and statistical techniques. We extracted nine categories of epilepsy information in addition to clinic date and date of birth across 200 clinic letters. We compared the results of our algorithm with a manual review of the letters by an epilepsy clinician.

SETTING

De-identified and pseudonymised epilepsy clinic letters from a Health Board serving half a million residents in Wales, UK.

RESULTS

We identified 1925 items of information with overall precision, recall and F1 score of 91.4%, 81.4% and 86.1%, respectively. Precision and recall for epilepsy-specific categories were: epilepsy diagnosis (88.1%, 89.0%), epilepsy type (89.8%, 79.8%), focal seizures (96.2%, 69.7%), generalised seizures (88.8%, 52.3%), seizure frequency (86.3%-53.6%), medication (96.1%, 94.0%), CT (55.6%, 58.8%), MRI (82.4%, 68.8%) and electroencephalogram (81.5%, 75.3%).

CONCLUSIONS

We have built an automated clinical text extraction system that can accurately extract epilepsy information from free text in clinic letters. This can enhance routinely collected data for research in the UK. The information extracted with ExECT such as epilepsy type, seizure frequency and neurological investigations are often missing from routinely collected data. We propose that our algorithm can bridge this data gap enabling further epilepsy research opportunities. While many of the rules in our pipeline were tailored to extract epilepsy specific information, our methods can be applied to other diseases and also can be used in clinical practice to record patient information in a structured manner.

Risk of readmission in patients with schizophrenia and schizoaffective disorder newly prescribed clozapine

BACKGROUND

Insight into the effect of clozapine is limited by a lack of controlling for confounding variables in current research. Our objective was to investigate the association between clozapine prescribed at discharge, following an inpatient episode, and risk of readmission into secondary mental health services in patients with schizophrenia and schizoaffective disorder, controlling extensively for confounding variables.

METHODS

Clinical records from 3651 patients were analysed in a retrospective observational cohort study. Cox proportional-hazards regression models were used to assess the risk of hospital readmission. A series of sensitivity analyses were also conducted. Propensity score methods were used to address confounding-by-indication.

RESULTS

Patients on clozapine ( n=202) had a reduced risk of readmission compared with patients on other antipsychotics (adjusted hazard ratio=0.79; 95% confidence interval: 0.64-0.99; p=0.043). Clozapine also had a protective effect on risk of readmission when compared with olanzapine (adjusted hazard ratio 0.76; 95% confidence interval: 0.60-0.96; p=0.021). The effect size remained consistent after adjusting for an array of possible confounders, as well as using propensity scores to address confounding-by-indication. A statistically significant result was also noted in all but two sensitivity analyses.

CONCLUSION

Our findings suggest that clozapine is associated with a reduced risk of readmission into secondary mental health services.

Psychiatric comorbidity as a risk factor for mortality in people with anorexia nervosa

Anorexia nervosa (AN) is found associated with increased mortality. Frequent comorbidities of AN include substance use disorders (SUD), affective disorders (AD) and personality disorders (PD). We investigated the influence of these psychiatric comorbidities on all-cause mortality with demographic and socioeconomic factors considered as confounders in the observation window between January 2007 and March 2016 for 1970 people with AN, using data from the case register of the South London and Maudsley (SLaM) NHS Foundation Trust, an almost monopoly-secondary mental healthcare service provider in southeast London. We retrieved data from its Clinical Records Interactive Search (CRIS) system as data source. Mortality was ascertained through nationwide tracing by the UK Office for National Statistics (ONS) linked to CRIS database on a monthly basis. A total of 43 people with AN died during the observation period. Standardized Mortality Ratio (SMR) with England and Wales population in 2012 as standard population for our study cohort was 5.21 (95% CI 3.77, 7.02). In univariate analyses, the comorbidity of SUD or PD was found to significantly increase the relative risks of mortality (HRs = 3.10, 95% CI 1.21, 7.92; and 2.58, 95% CI 1.23, 5.40, respectively). After adjustment for demographic and socioeconomic covariates as confounders, moderately but not significantly elevated risks were identified for SUD (adjusted HR = 1.39, 95% CI 0.53, 3.65) and PD (adjusted HR = 1.58, 95% CI 0.70, 3.56). These results suggest an elevated mortality in people with AN, which might be, at least partially, explained by the existence of the comorbidities SUD or PD.

Real World Implementation of a Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk of Psychosis in Clinical Routine: Study Protocol

Background: Primary indicated prevention in individuals at-risk for psychosis has the potential to improve the outcomes of this disorder. The ability to detect the majority of at-risk individuals is the main barrier toward extending benefits for the lives of many adolescents and young adults. Current detection strategies are highly inefficient. Only 5% (standalone specialized early detection services) to 12% (youth mental health services) of individuals who will develop a first psychotic disorder can be detected at the time of their at-risk stage. To overcome these challenges a pragmatic, clinically-based, individualized, transdiagnostic risk calculator has been developed to detect individuals at-risk of psychosis in secondary mental health care at scale. This calculator has been externally validated and has demonstrated good prognostic performance. However, it is not known whether it can be used in the real world clinical routine. For example, clinicians may not be willing to adhere to the recommendations made by the transdiagnostic risk calculator. Implementation studies are needed to address pragmatic challenges relating to the real world use of the transdiagnostic risk calculator. The aim of the current study is to provide in-vitro and in-vivo feasibility data to support the implementation of the transdiagnostic risk calculator in clinical routine. Method: This is a study which comprises of two subsequent phases: an in-vitro phase of 1 month and an in-vivo phase of 11 months. The in-vitro phase aims at developing and integrating the transdiagnostic risk calculator in the local electronic health register (primary outcome). The in-vivo phase aims at addressing the clinicians' adherence to the recommendations made by the transdiagnostic risk calculator (primary outcome) and other secondary feasibility parameters that are necessary to estimate the resources needed for its implementation. Discussion: This is the first implementation study for risk prediction models in individuals at-risk for psychosis. Ultimately, successful implementation is the true measure of a prediction model's utility. Therefore, the overall translational deliverable of the current study would be to extend the benefits of primary indicated prevention and improve outcomes of first episode psychosis. This may produce significant social benefits for many adolescents and young adults and their families.

Ethnic differences in cognition and age in people diagnosed with dementia: A study of electronic health records in two large mental healthcare providers

OBJECTIVES

Qualitative studies suggest that people from UK minority ethnic groups with dementia access health services later in the illness than white UK-born elders, but there are no large quantitative studies investigating this. We aimed to investigate interethnic differences in cognitive scores and age at dementia diagnosis.

METHODS

We used the Clinical Record Interactive Search (CRIS) applied to the electronic health records of two London mental health trusts to identify patients diagnosed with dementia between 2008 and 2016. We meta-analysed mean Mini Mental State Examination (MMSE) and mean age at the time of diagnosis across trusts for the most common ethnic groups, and used linear regression models to test these associations before and after adjustment for age, sex, index of multiple deprivation, and marital status. We also compared percentage of referrals for each ethnic group with catchment census distributions.

RESULTS

Compared with white patients (N = 9380), unadjusted mean MMSE scores were lower in Asian (-1.25; 95% CI -1.79, -0.71; N = 642) and black patients (-1.82, 95% CI -2.13, -1.52; N = 2008) as was mean age at diagnosis (Asian patients: -4.27 (-4.92, -3.61); black patients -3.70 (-4.13, -3.27) years). These differences persisted after adjustment. In general, ethnic group distributions in referrals did not differ substantially from those expected in the catchments.

CONCLUSIONS

People from black and Asian groups were younger at dementia diagnosis and had lower MMSE scores than white referrals.

General hospital admission rates in people diagnosed with personality disorder

OBJECTIVE

To determine the frequency of all-cause general hospital admissions for individuals with personality disorder (PD) in a large clinical population using linked secondary mental healthcare and hospitalisation data.

METHOD

A retrospective cohort study, using anonymised electronic mental health records from South London and Maudsley NHS Foundation Trust (SLaM), linked to Hospital Episodes Statistics in England. People with PD aged 15 years or older, receiving care within SLaM between April 2007 and March 2013, were identified and compared to residents from the local catchment area. Standardised admission ratios (SARs) were calculated for all major categories of causes of general hospital admission for this defined group, with local residents in 2011 UK Census as the standard population.

RESULTS

For the 7677 people identified with PD, SAR for all causes of admission was 2.75 (95% CI: 2.70, 2.81). Both men and women with PD had increased SARs across multiple ICD-10 categories, including circulatory, respiratory, digestive, nervous, and musculoskeletal system disorders and endocrine, blood and infectious disorders. Sensitivity analysis (removing the impact of repeated admissions by same individual for same diagnosis in the same year) yielded similar findings.

CONCLUSIONS

By comparison with members of the general population, individuals with a diagnosis of personality disorder are at significantly higher risk of hospital admission resulting from a wide range of physical health problems.

Clozapine and all-cause mortality in treatment-resistant schizophrenia: a historical cohort study

OBJECTIVE

Large-scale epidemiological studies have demonstrated a protective effect of clozapine on mortality in people with schizophrenia. Clozapine is reserved for use in patients with treatment-resistant schizophrenia (TRS), but evidence of clozapine's effect on mortality exclusively within TRS samples is inconclusive. Hence, we aimed to investigate the effect of clozapine use on all-cause mortality in TRS patients.

METHODS

A historical patient cohort sample of 2837 patients, who met criteria for TRS between 1 Jan 2008 and 1 Jan 2016, were selected from the South London and Maudsley NHS Foundation Trust (SLAM) electronic health records (EHR). The national Zaponex Treatment Access System (ZTAS) mandatory monitoring system linked to the SLAM EHR was used to distinguish which patients were initiated on clozapine (n = 1025). Cox proportional hazard models were used, adjusting for sociodemographics, clinical monitoring, mental and physical illness severity and functional status.

RESULTS

After controlling for potential confounders, the protective effect of clozapine on all-cause mortality was significant (adjusted hazard ratio 0.61; 95% confidence interval 0.38-0.97; P = 0.04).

CONCLUSIONS

Clozapine reduces the risk of mortality in patients who meet criteria for TRS. We provide further evidence that improving access to clozapine in TRS is likely to reduce the mortality gap in schizophrenia.

Place of death and other factors associated with unnatural mortality in patients with serious mental disorders: population-based retrospective cohort study

BACKGROUND

Patients with serious mental disorders have poorer healthcare outcomes at the end of life and are at greater risk of dying from unnatural causes.AimsTo explore place of death and demographic and clinical correlates of unnatural causes of death in patients with serious mental disorders.

METHOD

Routinely collected patient data were used to explore bivariate and adjusted associations between covariates and natural/unnatural cause of death.

RESULTS

In multivariable analysis (n = 1029), dying at home (odds ratio (OR) = 1.87, 95% CI 1.03-3.40), 'other' locations (OR = 16.50, 95% CI 7.57-36.00), younger age (OR = 17.26, 95% CI 8.28-36.00) and a diagnosis other than schizophrenia spectrum disorder (OR = 1.69, 95% CI 1.04-2.73) were correlates of unnatural cause of death.

CONCLUSIONS

Deaths from unnatural causes were high and more likely to occur at home and non-healthcare settings. Unnatural causes of death were higher in younger patients with non-schizophrenia spectrum disorder diagnoses.Declaration of interestF.G. has received support or honoraria for CME, advisory work and lectures from Bristol-Myers Squibb, Janssen, Lundbeck, Otsuka, Roche, and Sunovion, and has a family member with professional links to Lilly and GSK, including shares.

Relapse in the first three months postpartum in women with history of serious mental illness

BACKGROUND

Relapse of serious mental illness (psychotic and bipolar disorders; SMI) in the postpartum period is potentially devastating for mother and baby. There is limited evidence on whether medication in the perinatal period is protective against postpartum relapse for women with SMI particularly non-affective psychoses. We aimed to investigate risk factors for postpartum relapse, particularly the potential prophylactic effects of medication.

METHODS

Using an anonymised resource of comprehensive electronic secondary mental health care records linked with maternity data, women with history of SMI who gave birth from 2007 to 2011 were identified. Relapse was defined as admission to acute care in the first 3 months postpartum. Women who were exposed to regular medication were compared with women who were unexposed. Data were analysed by pregnancy using random effects models to account for repeated measures in women who had more than one pregnancy in the study period.

RESULTS

There were 452 full term pregnancies, of which 128 (28.3%) were associated with relapse in the first 3 months postpartum, with recent relapse an independent predictor (aOR; 95% CI:1.30-2.27). There was no evidence of a prophylactic effect of medication (crude OR = 0.65; 0.34-1.25) (aOR = 0.99; 0.54-1.83), in women with non-affective or affective psychoses (interaction test p = 0.453).

CONCLUSIONS

Recent relapse increases the risk of relapse in the postpartum period so women with severe illnesses with a recent history of relapse should be warned pre-conception about the high risk of relapse. The lack of evidence of a protective impact of medication prophylaxis may reflect confounding by indication.

"Why have I not been told about this?": a survey of experiences of and attitudes to advance decision-making amongst people with bipolar

Background: The idea that people with severe mental illness should be able to plan in advance for periods of illness as a means of enhancing autonomy has been long debated and is increasingly being enshrined in codes of practice and mental health legislation. It has been argued that the ethical imperative for this is especially pronounced in bipolar (BP), a condition in which sufferers often experience episodic crises interspersed with periods of wellness. However, there is a paucity of published research investigating experiences of advance decision making (ADM) in people with BP or their attitudes towards it. Methods: An online survey of BPUK's mailing list was conducted. 932 people with BP completed the survey (response rate 5.61%). Descriptive statistics and regression analysis were conducted to compare experience of with attitudes towards ADM and variables associated with interest in ADM. Results: A majority indicated a desire to plan care in advance of losing capacity (88%) but most had not done so (64%). High numbers of respondents expressed a wish to request as well as refuse treatment and most wanted to collaborate with psychiatrists, including on issues around self-binding. The most frequent motivation to utilise ADM was a desire to be more involved in mental health decisions. Interest in self-binding was associated with experience of compulsory treatment and trust in mental health services. Interest in refusals of all medication was associated with younger age and lack of trust in mental health services. Interest in ADM in general was associated with younger age but not educational level, ethnicity or gender. Conclusions: This study demonstrates an appetite for ADM amongst people with bipolar that is independent of educational status and ethnicity. As states reform their mental health laws, attention needs to be given to the distinctive attitudes toward ADM amongst people with bipolar.

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data

OBJECTIVES

Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services.

SETTING

The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013.

PARTICIPANTS

A clinical cohort of 35 509 children and young people (aged 4-17 years).

DESIGN

Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources.

OUTCOME MEASURES

Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases.

RESULTS

Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22).

CONCLUSIONS

The work described sets a precedent for education data being used for medical benefit in England. Linkage between health and education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.

Unmet needs in patients with brief psychotic disorders: Too ill for clinical high risk services and not ill enough for first episode services

BACKGROUND

Patients with acute and transient psychotic disorders (ATPDs) are by definition remitting, but have a high risk of developing persistent psychoses, resembling a subgroup of individuals at Clinical High Risk for Psychosis (CHR-P). Their pathways to care, treatment offered and long-term clinical outcomes beyond risk to psychosis are unexplored. We conducted an electronic health record-based retrospective cohort study including patients with ATPDs within the SLaM NHS Trust and followed-up to 8 years.

METHODS

A total of 2561 ATPDs were included in the study. A minority were detected (8%) and treated (18%) by Early Intervention services (EIS) and none by CHR-P services. Patients were offered a clinical follow-up of 350.40 ± 589.90 days. The cumulative incidence of discharges was 40% at 3 months, 60% at 1 year, 69% at 2 years, 77% at 4 years, and 82% at 8 years. Treatment was heterogeneous: the majority of patients received antipsychotics (up to 52%), only a tiny minority psychotherapy (up to 8%).

RESULTS

Over follow-up, 32.88% and 28.54% of ATPDS received at least one mental health hospitalization or one compulsory hospital admission under the Mental Health Act, respectively. The mean number of days spent in psychiatric hospital was 66.39 ± 239.44 days.

CONCLUSIONS

The majority of ATPDs are not detected/treated by EIS or CHR-P services, receive heterogeneous treatments and short-term clinical follow-up. ATPDs have a high risk of developing severe clinical outcomes beyond persistent psychotic disorders and unmet clinical needs that are not targeted by current mental health services.

Transdiagnostic Individualized Clinically Based Risk Calculator for the Detection of Individuals at Risk and the Prediction of Psychosis: Model Refinement Including Nonlinear Effects of Age

Background: The first rate-limiting step for primary indicated prevention of psychosis is the detection of young people who may be at risk. The ability of specialized clinics to detect individuals at risk for psychosis is limited. A clinically based, individualized, transdiagnostic risk calculator has been developed and externally validated to improve the detection of individuals at risk in secondary mental health care. This calculator employs core sociodemographic and clinical predictors, including age, which is defined in linear terms. Recent evidence has suggested a nonlinear impact of age on the probability of psychosis onset. Aim: To define at a meta-analytical level the function linking age and probability of psychosis onset. To incorporate this function in a refined version of the transdiagnostic risk calculator and to test its prognostic performance, compared to the original specification. Design: Secondary analyses on a previously published meta-analysis and clinical register-based cohort study based on 2008-2015 routine secondary mental health care in South London and Maudsley (SLaM) National Health Service (NHS) Foundation Trust. Participants: All patients receiving a first index diagnosis of non-organic/non-psychotic mental disorder within SLaM NHS Trust in the period 2008-2015. Main outcome measure: Prognostic accuracy (Harrell's C). Results: A total of 91,199 patients receiving a first index diagnosis of non-organic and non-psychotic mental disorder within SLaM NHS Trust were included in the derivation (33,820) or external validation (54,716) datasets. The mean follow-up was 1,588 days. The meta-analytical estimates showed that a second-degree fractional polynomial model with power (-2, -1: age1 = age^-2 and age2 = age^-1) was the best-fitting model (P < 0.001). The refined model that included this function showed an excellent prognostic accuracy in the external validation (Harrell's C = 0.805, 95% CI from 0.790 to 0.819), which was statistically higher than the original model, although of modest magnitude (Harrell's C change = 0.0136, 95% CIs from 0.006 to 0.021, P < 0.001). Conclusions: The use of a refined version of the clinically based, individualized, transdiagnostic risk calculator, which allows for nonlinearity in the association between age and risk of psychosis onset, may offer a modestly improved prognostic performance. This calculator may be particularly useful in young individuals at risk of developing psychosis who access secondary mental health care.

The Maudsley Biomedical Research Centre (BRC) data linkage service user and carer advisory group: creating and sustaining a successful patient and public involvement group to guide research in a complex area

BACKGROUND

Patient and Public Involvement (PPI) in health and social care research has been shown to improve the quality and relevance of research. PPI in data linkage research is important in ensuring the legitimacy of future health informatics initiatives, but remains sparse and under-developed. This article describes the setting up and evaluation of a service user and carer advisory group with the aim of providing feedback and advice to researchers developing or making use of database linkages in the field of mental health.

AIM

The aim of this study is to describe the creation and formative evaluation of the service user and carer advisory group after a trial period of 12 months.

METHOD

Six individuals were recruited to the group all of whom had personal experience of mental illness. A formative evaluation was conducted after a trial period of 12 months.

RESULTS

Evaluation revealed that the group succeeded in promoting dialogue between service users/carers and researchers. Factors that contributed to the success of the group's first year included the opportunity it provided for researchers to involve service users and carers in their projects, the training provided to group members, and the openness of researchers to receiving feedback from the group.

CONCLUSION

The group encourages the incorporation of PPI in data linkage research which helps to ensure the legitimacy of data linkage practices and governance systems whilst also improving the quality and relevance of the research being conducted using linked data.

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

Negative Symptoms in Early-Onset Psychosis and Their Association With Antipsychotic Treatment Failure

The prevalence of negative symptoms (NS) at first episode of early-onset psychosis (EOP), and their effect on psychosis prognosis is unclear. In a sample of 638 children with EOP (aged 10-17 y, 51% male), we assessed (1) the prevalence of NS at first presentation to mental health services and (2) whether NS predicted eventual development of multiple treatment failure (MTF) prior to the age of 18 (defined by initiation of a third trial of novel antipsychotic due to prior insufficient response, intolerable adverse-effects or non-adherence). Data were extracted from the electronic health records held by child inpatient and community-based services in South London, United Kingdom. Natural Language Processing tools were used to measure the presence of Marder Factor NS and antipsychotic use. The association between presenting with ≥2 NS and the development of MTF over a 5-year period was modeled using Cox regression. Out of the 638 children, 37.5% showed ≥2 NS at first presentation, and 124 (19.3%) developed MTF prior to the age of 18. The presence of NS at first episode was significantly associated with MTF (adjusted hazard ratio 1.62, 95% CI 1.07-2.46; P = .02) after controlling for a number of potential confounders including psychosis diagnostic classification, positive symptoms, comorbid depression, and family history of psychosis. Other factors associated with MTF included comorbid autism spectrum disorder, older age at first presentation, Black ethnicity, and family history of psychosis. In EOP, NS at first episode are prevalent and may help identify a subset of children at higher risk of responding poorly to antipsychotics.

Acute general hospital admissions in people with serious mental illness

BACKGROUND

Serious mental illness (SMI, including schizophrenia, schizoaffective disorder, and bipolar disorder) is associated with worse general health. However, admissions to general hospitals have received little investigation. We sought to delineate frequencies of and causes for non-psychiatric hospital admissions in SMI and compare with the general population in the same area.

METHODS

Records of 18 380 individuals with SMI aged ⩾20 years in southeast London were linked to hospitalisation data. Age- and gender-standardised admission ratios (SARs) were calculated by primary discharge diagnoses in the 10th edition of the World Health Organization International Classification of Diseases (ICD-10) codes, referencing geographic catchment data.

RESULTS

Commonest discharge diagnosis categories in the SMI cohort were urinary conditions, digestive conditions, unclassified symptoms, neoplasms, and respiratory conditions. SARs were raised for most major categories, except neoplasms for a significantly lower risk. Hospitalisation risks were specifically higher for poisoning and external causes, injury, endocrine/metabolic conditions, haematological, neurological, dermatological, infectious and non-specific ('Z-code') causes. The five commonest specific ICD-10 diagnoses at discharge were 'chronic renal failure' (N18), a non-specific code (Z04), 'dental caries' (K02), 'other disorders of the urinary system' (N39), and 'pain in throat and chest' (R07), all of which were higher than expected (SARs ranging 1.57-6.66).

CONCLUSION

A range of reasons for non-psychiatric hospitalisation in SMI is apparent, with self-harm, self-neglect and/or reduced healthcare access, and medically unexplained symptoms as potential underlying explanations.

The Incidence of Recorded Delirium Episodes Before and After Dementia Diagnosis: Differences Between Dementia With Lewy Bodies and Alzheimer's Disease

OBJECTIVES

To describe the incidence of delirium recording before and after a diagnosis of dementia is established in patients with dementia with Lewy bodies (DLB) and compare findings to a matched cohort of patients with Alzheimer's disease (AD).

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

A cohort of patients with dementia from a large mental health and dementia care database in South London, linked to hospitalization and mortality data. We identified 194 patients with DLB and 1:4 matched these with 776 patients diagnosed with AD on age, gender, and cognitive status.

MEASURES

We identified delirium episodes recorded in mental health and hospital records from 1 year before to 1 year after dementia diagnosis. Using dementia diagnosis as an index date we additionally followed patients until first episode of delirium, death or a censoring point without restricting the observation period.

RESULTS

Patients with DLB had significantly more episodes of delirium recorded in the year before dementia diagnosis than patients with AD (incidence rate 17.6 vs 3.2 per 100 person-years; P < .001). Whereas the incidence of recording of delirium episodes reduced substantially in patients with DLB after dementia diagnosis, it remained significantly higher than in patients with AD (incidence rate 6.2 vs 2.3 per 100 person-years; P = .032). Cox regression models indicate that patients with DLB remain at a higher risk of delirium than patients with AD after a dementia diagnosis.

CONCLUSIONS/RELEVANCE

Establishing a diagnosis of dementia reduces episodes classified as delirium in patients with DLB and might lead to fewer potentially harmful interventions such as hospitalization or use of antipsychotic medication.

Predictors of falls and fractures leading to hospitalization in people with schizophrenia spectrum disorder: A large representative cohort study

AIM

To investigate predictors of falls/fractures leading to hospitalisation in people with schizophrenia-spectrum disorders.

METHODS

A historical cohort of people with schizophrenia-spectrum disorders (ICD F20-29) from 01/2006-12/2012 was assembled using data from the South London and Maudsley NHS Biomedical Research Centre Case Register. Falls/fractures were ascertained from a linkage to national hospitalisation data. Separate multivariate Cox regression analyses were employed to identify predictors of falls and fractures.

RESULTS

Of 11,567 people with schizophrenia-spectrum disorders (mean age 42.6 years, 43% female), 579 (incidence rate 12.79 per 1000 person-years) and 528 (11.65 per 1000 person-years) had at least one reported hospital admission due to a fall or fracture respectively and 822 patients had at least either a recorded fall or a fracture during this period (i.e. 7.1% of sample). Overall, 6.69 and 10.74 years of inpatient hospital stay per 1000-person years of follow-up occurred due to a fall and fracture respectively. 14(0.12%) and 28(0.24%) died due to a fall and fracture respectively. In Multivariable analysis, increasing age, white ethnicity, analgesics, cardiovascular disease, hypertension, diseases of the genitourinary system, visual disturbance and syncope were significant risk factor for both falls and fractures. A previous fracture (HR 2.05, 95% CI 1.53-2.73) and osteoporosis (HR 6.79, 95% CI 4.71-9.78) were strong risk factors for consequent fractures.

CONCLUSION

Comorbid physical health conditions and analgesic medication prescription were associated with higher risk of falls and fractures. Osteoporosis and previous fracture were strong predictors for subsequent fractures. Interventions targeting bone health and falls/fractures need to be developed and evaluated in these populations.