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Background: Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions (ADRs). Electronic health records (EHRs) offer an opportunity to examine APP using real-world data. In this study, we use EHR data to identify periods when patients were prescribed 2 + antipsychotics and compare these with periods of antipsychotic monotherapy. To determine the relationship between APP and subsequent instances of ADRs: QT interval prolongation, hyperprolactinaemia, and increased body weight [body mass index (BMI) ⩾ 25]., Methods: We extracted anonymised EHR data. Patients aged 16 + receiving antipsychotic medication at Camden & Islington NHS Foundation Trust between 1 January 2008 and 31 December 2018 were included. Multilevel mixed-effects logistic regression models were used to elucidate the relationship between APP and the subsequent presence of QT interval prolongation, hyperprolactinaemia, and/or increased BMI following a period of APP within 7, 30, or 180 days respectively., Results: We identified 35 409 observations of antipsychotic prescribing among 13 391 patients. Compared with antipsychotic monotherapy, APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% CI 1.87-3.24) and of registering a BMI > 25 (adjusted odds ratio 1.75; 95% CI 1.33-2.31) in the period following the APP prescribing., Conclusions: Our observations suggest that APP should be carefully managed with attention to hyperprolactinaemia and obesity.

Objectives: Evidence in mouse models has found that the antidepressant trazodone may be protective against neurodegeneration. We therefore aimed to compare cognitive decline of people with dementia taking trazodone with those taking other antidepressants., Methods: Three identical naturalistic cohort studies using UK clinical registers. We included all people with dementia assessed during 2008-16 who were recorded taking trazodone, citalopram or mirtazapine for at least 6 weeks. Linear mixed models examined age, time and sex-adjusted Mini-mental state examination (MMSE) change in people with all-cause dementia taking trazodone compared with those taking citalopram and mirtazapine. In secondary analyses, we examined those with non-vascular dementia; mild dementia; and adjusted results for neuropsychiatric symptoms. We combined results from the three study sites using random-effects meta-analysis., Results: We included 2,199 people with dementia, including 406 taking trazodone, with mean 2.2 years follow-up. There was no difference in adjusted cognitive decline in people with all-cause or non-vascular dementia taking trazodone, citalopram or mirtazapine in any of the three study sites. When data from the three sites were combined in meta-analysis, we found greater mean MMSE decline in people with all-cause dementia taking trazodone compared to those taking citalopram (0·26 points per successive MMSE measurement, 95% CI 0·03-0·49; p = 0·03). Results in sensitivity analyses were consistent with primary analyses., Conclusions: There was no evidence of cognitive benefit from trazodone compared to other antidepressants in people with dementia in three naturalistic cohort studies. Despite preclinical evidence, trazodone should not be advocated for cognition in dementia., (© 2021 The Authors. International Journal of Geriatric Psychiatry published by John Wiley & Sons Ltd.)

Background: Post-traumatic stress disorder (PTSD) is a risk factor for suicidality (suicidal ideation, and suicide attempt). This study described the prevalence of suicidality amongst a representative sample of individuals with PTSD and the association between suicidality and receipt of five PTSD treatments., Methods: We analysed deidentified data for patients being treated for PTSD at Camden and Islington NHS Foundation Trust between 2009 and 2017 obtained via the Clinical Record Interactive Search tool. We described the sample's sociodemographic and clinical characteristics and used stepwise logistic regression to investigate the association between suicidality and receipt of four, specific PTSD treatments: psychotherapy, antidepressant/antianxiety medication, antipsychotics, benzodiazepines. We used Cox proportional hazards regression to investigate the association between suicidality and hospital/crisis team admission., Results: Of 745 patients diagnosed with PTSD, 60% received psychotherapy and 66% received psychotropic medication. Those who reported suicidality (6%) were no more likely than those who did not to be prescribed antidepressant/antianxiety medication, but were more likely to receive antipsychotics (AOR = 2.27, 95% CI 1.15 - 4.47), benzodiazepines (AOR 2.28, 95% CI 1.17 - 4.44), psychotherapy (AOR 2.60, 95% CI 1.18 - 5.73) and to be admitted to hospital/crisis team (AOR 2.84, 95% 1.82 - 4.45)., Conclusion: In this sample, patients with PTSD and suicidality were more likely to receive psychiatric medication, psychotherapy and psychiatric admission than those who were not suicidal. Overall patients were more likely to receive psychotropic medication than psychotherapy. Adherence to clinical guidelines is important in this population to improve treatment outcomes and reduce the risk of suicide.KEY POINTSNICE guidelines recommend psychological therapy be first line treatment for PTSD, yet we identified that fewer people diagnosed with PTSD received therapy compared to psychotropic medication.Patients with suicidality were more likely to receive antipsychotics and benzodiazepines, yet not antidepressant/antianxiety medication although given that suicidality is characteristic of severe depression, it might be assumed from stepped care models that antidepressant/antianxiety medication be prescribed before antipsychotics.The high proportion of patients prescribed antipsychotics suggests a need for better understanding of psychosis symptoms among trauma-exposed populations.Identifying which combinations of symptoms are associated with suicidal thoughts could help tailor trauma-informed approaches to discussing therapy and medication.

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.

Introduction: Subclinical psychotic experiences (PEs) are far more prevalent than psychotic disorders, with an estimated prevalence of 7.2% (Linscott & Van Os. Psychol Med 2013;43(6) 1133-1149). PEs are particularly prevalent in late adolescence and young adulthood, when obtaining academic education is one of the main developmental tasks. University students are at the peak age of onset of mental disorders, and often experience high levels of social and academic stress that may contribute to the onset of psychopathology. Hence, estimating the prevalence and correlates of PEs among university students is particularly important. Objectives: To estimate the prevalence of PEs in a sample of Israeli students; assess whether rates of PEs differ by selected sociodemographic characteristics; and examine the association between PEs and non-specific psychological distress. Methods: 150 students from universities and colleges in Israel participated in a cross-sectional online survey. All students were over the age of 18 and were not diagnosed with psychotic disorders. Participants completed self-report questionnaires, including the Prodromal Questionnaire - Brief Version (PQ-B), Kessler Psychological Distress Scale (K10) and sociodemographic details. The PQ-B yields a score for the total number of items endorsed (range 0–21), and a total distress score (range 0–105). A cutoff of ≥8 distressing symptoms was used to identify participants at high-risk for psychosis. Results: 21 participants (14.0%) reported 8 or more distressing PEs. PEs were more common in males and among those with a psychiatric illness (Table 1). PEs were not associated with marital status, religiosity, or immigrant status. While a greater number of PEs was positively associated with non-specific psychological distress (r=0.589, p<.001), there was no association between distress caused by PEs and non-specific psychological distress (r=0.145, NS). Table 1. Sociodemographic characteristics by group PEs- PEs+ X2, p Sex M 29.5% 52.4% 4.32,.038 F 70.5% 47.6% Marital Status Married 17.1% 23.8% 0.56, NS Unmarried 82.9% 76.2% Immigrant No 89.9% 85.7% 0.34, NS Yes 10.1% 14.3% Religiosity Secular 74.4% 57.1% 2.67, NS Other 25.6% 42.9% Psychiatric illness No 87.6% 61.9% 8.87,.003 Yes 12.4% 38.1% Conclusions: The findings confirm that self-reported PEs are much more prevalent than clinically diagnosed psychotic disorders, particularly among young adults. As PEs were found to be associated with non-specific psychological distress, and as they are known forerunners for severe mental disorders, it is important to address mental health issues in school settings and promote prevention and early intervention programs. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

Objective: To use data from electronic health records (EHRs) to describe the demographic, clinical and functional correlates of childhood sexual abuse (CSA) in patients with severe mental illness (SMI), and compare their clinical outcomes (admissions and receipt of antipsychotic medications) to those of patients with no recorded history of CSA., Methods: We applied a string-matching technique to clinical text records of 7000 patients with SMI (non-organic psychotic disorders or bipolar disorder), identifying 619 (8.8%) patients with a recorded history of CSA. Data were extracted from both free-text and structured fields of patients' EHRs., Results: Comorbid diagnoses of major depressive disorder, post-traumatic stress disorder and personality disorders were more prevalent in patients with CSA. Positive psychotic symptoms, depressed mood, self-harm, substance use and aggression were also more prevalent in this group, as were problems with relationships and living conditions. The odds of inpatient admissions were higher in patients with CSA than in those without (adjusted OR = 1.95, 95% CI: 1.64-2.33), and they were more likely to have spent more than 10 days per year as inpatients (adjusted OR = 1.32, 95% CI: 1.07-1.62). Patients with CSA were more likely to be prescribed antipsychotic medications (adjusted OR = 2.48, 95% CI: 1.69-3.66) and be given over 75% of the maximum recommended daily dose (adjusted OR = 1.72, 95% CI: 1.44-2.04)., Conclusion: Data-driven approaches are a reliable, promising avenue for research on childhood trauma. Clinicians should be trained and skilled at identifying childhood adversity in patients with SMI, and addressing it as part of the care plan., (© 2021 The Authors. Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd.)

Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects., Material and Methods: We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER)., Results: Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs., Conclusion: A better understanding of how drugs work in the real world can complement clinical trials., Competing Interests: The authors have declared that no competing interests exist.

Background: Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical studies of their effectiveness due to the logistic challenges and costs of standard research methods. The Clinical Record Interactive Search (CRIS) tool, developed to de-identify and interrogate routinely recorded electronic health records, may provide an alternative to evaluate supported housing services., Methods: The feasibility of using the Camden and Islington NHS Foundation Trust CRIS database to identify a sample of users of mental health supported accommodation services. Two approaches to data interrogation and case identification were compared; using structured fields indicating individual's accommodation status, and iterative development of free text searches of clinical notes referencing supported housing. The data used were recorded over a 10-year-period (01-January-2008 to 31-December-2017)., Results: Both approaches were carried out by one full-time researcher over four weeks (150 hours). Two structured fields indicating accommodation status were found, 2,140 individuals had a value in at least one of the fields representative of supported accommodation. The free text search of clinical notes returned 21,103 records pertaining to 1,105 individuals. A manual review of 10% of the notes indicated an estimated 733 of these individuals had used a supported housing service, a positive predictive value of 66.4%. Over two-thirds of the individuals returned in the free text search (768/1,105, 69.5%) were identified via the structured fields approach. Although the estimated positive predictive value was relatively high, a substantial proportion of the individuals appearing only in the free text search (337/1,105, 30.5%) are likely to be false positives., Conclusions: It is feasible and requires minimal resources to use de-identified electronic health record search tools to identify large samples of users of mental health supported housing using structured and free text fields. Further work is needed to establish the availability and completion of variables relevant to specific clinical research questions in order to fully assess the utility of electronic health records in evaluating the effectiveness of these services., Competing Interests: The authors have declared that no competing interests exist.

Background: Characteristics and outcomes of patients presenting to Emergency Departments (EDs) have been under-examined. This paper describes the characteristics and risk of repeat suicidality amongst patients presenting to EDs with (1) suicidal ideation and (2) self-harm, compared to (3) controls in mental health crisis., Methods: The Clinical Record Interactive Search tool identified 2211 patients who presented to three London EDs with suicidal ideation or self-harm, and 1108 control patients. All patients received a full psychosocial assessment. Chi-squared tests examined group characteristics. Cox regression models assessed the risk of re-presentation with suicidal ideation or self-harm within one year., Results: There were a higher proportion of females and individuals under the age of 25 in the self-harm group. Patients presenting with suicidal ideation or self-harm were more likely to be white, live in more deprived areas, and less likely to receive a psychiatric diagnosis within one year compared to controls. Risk of repeat suicidality within one year was 3-4 times higher in those with baseline suicidal ideation (adjusted HR = 3.66, 95% CI 2.44-5.48) or self-harm (HR = 3.53 95% CI 2.47-5.04) compared to controls., Limitations: To be included patients needed to have a full psychosocial assessment. Incomplete records meant 21.4% of the sample was excluded. This will have introduced bias which might confound observed associations., Conclusion: Individuals presenting with either suicidal ideation or self-harm have similar risk for re-presentation within one year. Both groups would benefit from personalised risk management plans and active follow-up to reduce the risk of repeat suicidal behaviour., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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.

Background: The benefits of indicated primary prevention among individuals at Clinical High Risk for Psychosis (CHR-P) are limited by the difficulty in detecting these individuals. To overcome this problem, a transdiagnostic, clinically based, individualized risk calculator has recently been developed and subjected to a first external validation in 2 different catchment areas of the South London and Maudsley (SLaM) NHS Trust., Methods: Second external validation of real world, real-time electronic clinical register-based cohort study. All individuals who received a first ICD-10 index diagnosis of nonorganic and nonpsychotic mental disorder within the Camden and Islington (C&I) NHS Trust between 2009 and 2016 were included. The model previously validated included age, gender, ethnicity, age by gender, and ICD-10 index diagnosis to predict the development of any ICD-10 nonorganic psychosis. The model's performance was measured using Harrell's C-index., Results: This study included a total of 13702 patients with an average age of 40 (range 16-99), 52% were female, and most were of white ethnicity (64%). There were no CHR-P or child/adolescent services in the C&I Trust. The C&I and SLaM Trust samples also differed significantly in terms of age, gender, ethnicity, and distribution of index diagnosis. Despite these significant differences, the original model retained an acceptable predictive performance (Harrell's C of 0.73), which is comparable to that of CHR-P tools currently recommended for clinical use., Conclusions: This risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided., (© The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

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., (© 2019 John Wiley & Sons, Ltd.)

Introduction: Although important to cognitive neuropsychiatry and theories of delusions, Capgras delusion has largely been reported in single case studies. Bell et al. [2017. Uncovering Capgras delusion using a large scale medical records database. British Journal of Psychiatry Open, 3(4), 179-185] previously deployed computational and clinical case identification on a large-scale medical records database to report a case series of 84 individuals with Capgras delusion. We replicated this approach on a new database from a different mental health service provider while additionally examining instances of violence, given previous claims that Capgras is a forensic risk., Methods: We identified 34 additional cases of Capgras. Delusion phenomenology, clinical characteristics, and presence of lesions detected by neuroimaging were extracted., Results: Although most cases involved misidentification of family members or partners, a notable minority (20.6%) included the misidentification of others. Capgras typically did not present as a monothematic delusion. Few cases had identifiable lesions with no evidence of right-hemisphere bias. There was no evidence of physical violence associated with Capgras., Conclusions: Findings closely replicate Bell et al. (2017). The majority of Capgras delusion phenomenology conforms to the "dual route" model although a significant minority of cases cannot be explained by this framework.

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.

Background: One third of older people are estimated to die with dementia, which is a principal cause of death in developed countries. While it is assumed that people die with severe dementia this is not based on evidence., Methods: Cohort study using a large secondary mental healthcare database in North London, UK. We included people aged over 65 years, diagnosed with dementia between 2008 and 2016, who subsequently died. We estimated dementia severity using mini-mental state examination (MMSE) scores, adjusting for the time between last score and death using the average annual MMSE decline in the cohort (1.5 points/year). We explored the association of sociodemographic and clinical factors, including medication use, with estimated MMSE score at death using linear regression., Results: In 1400 people dying with dementia, mean estimated MMSE at death was 15.3 (standard deviation 7.0). Of the cohort, 22.2% (95% confidence interval 20.1, 24.5) died with mild dementia; 50.4% (47.8, 53.0) moderate; and 27.4% (25.1, 29.8) with severe dementia. In fully adjusted models, more severe dementia at death was observed in women, Black, Asian and other ethnic minorities, agitated individuals, and those taking antipsychotic medication., Conclusions: Only one quarter of people who die with dementia are at the severe stage of the illness. This finding informs clinical and public understanding of dementia prognosis. Provision of end-of-life services should account for this and healthcare professionals should be aware of high rates of mild and moderate dementia at end of life and consider how this affects clinical decision-making.

Background: Response to antidepressants in major depressive disorder is variable and determinants are not well understood or used to design clinical trials. We aimed to understand these determinants., Methods: Supported by Innovative Medicines Initiative, as part of a large public-private collaboration (NEWMEDS), we assembled the largest dataset of individual patient level information from industry sponsored randomized placebo-controlled trials of antidepressant drugs in adults with MDD. We examined patient and trial-design-related determinants of outcome as measured by change on Hamilton Depression Scale or Montgomery-Asberg Depression Rating Scale in 34 placebo-controlled trials (drug, n = 8260; placebo, n = 3957)., Results: While it is conventional for trials to be 6-8 weeks long, drug-placebo differences were nearly the same at week 4 as at week 6 and with lower dropout rates. At the multivariate level, having any of these attributes was significantly associated with greater drug vs. placebo differences on symptom improvement: female, increasing proportion of patients on placebo, centers located outside of North America, centers with low placebo response (regardless of active treatment response) and using randomized withdrawal designs., Limitations: Data on compounds that failed were not available to us. Findings may not be relevant for new mechanisms of action., Conclusions: Proof of concept trials can be shorter and efficiency improved by selecting enriched populations based on clinical and demographic variables, ensuring adequate balance of placebo patients, and carefully selecting and monitoring centers. In addition to improving drug discovery, patient exposure to placebo and experimental treatments can be reduced., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Potentially modifiable risk factors for developing dementia have been identified. However, risk factors for increased mortality in patients with diagnosed dementia are not well understood. Identifying factors that influence prognosis would help clinicians plan care and address unmet needs.AimsTo investigate diagnosed depression and sociodemographic factors as predictors of mortality in patients with dementia in UK secondary clinical care services., Method: We conducted a cohort study of patients with a dementia diagnosis in an electronic health records database in a UK National Health Service mental health trust., Results: In 3374 patients with 10 856 person-years of follow-up, comorbid depression was not associated with mortality (adjusted hazard ratio 0.94; 95% CI 0.71-1.24). Single patients had higher mortality than those who were married (adjusted hazard ratio 1.25; 95% CI 1.03-1.50). Patients of Asian ethnicity had lower mortality rates than White British patients (adjusted hazard ratio 0.50; 95% CI 0.34-0.73)., Conclusions: Clinically diagnosed depression does not increase mortality in patients with dementia. Patients who are single are a potential high-mortality risk group. Lower mortality rates in Asian patients with dementia that have been reported in the USA also apply in the UK.Declaration of interestNone.

Aims and method To identify temporal and demographic trends in referrals made to psychiatric liaison services. Routine clinical data from 16 105 individual referrals from three central London accident and emergency (A&E) departments to psychiatric liaison services from 2012 to 2014 were obtained and analysed using the Clinical Record Interactive Search (CRIS)., Results: Referrals from A&E to psychiatric liaison services increased 16% over the 3-year study period. There were fewer referrals to psychiatric liaison services in winter months compared with other seasons. There were fewer referrals to psychiatric liaison services over the weekend compared with weekdays (average 15.4 daily weekday referrals v. 13.2 weekend, z = 5.1, P < 0.001), and weekend referrals were slightly less likely to result in admission to psychiatric hospital (11.3% v. 12.8%, respectively, χ2 = 6.33, P = 0.01). Clinical implications Psychiatric staffing in A&E and inpatient psychiatric wards requires planning to meet temporal and regional variations in the pattern of demand. Declaration of interest None.