Suicidality in patients with post-traumatic stress disorder and its association with receipt of specific secondary mental healthcare treatments

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 in 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

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

Antipsychotic polypharmacy and adverse drug reactions among adults in a London mental health service, 2008-2018

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

Antipsychotic Polypharmacy and Adverse Drug Reactions Among Adults in a London Mental Health Service, 2008-2018

Background: Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions. 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 adverse drug reactions: 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. APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% C.I. 1.87-3.24) and of having a BMI > 25 (adjusted odds ratio 1.75; 95% C.I. 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

The side effect profile of Clozapine in real world data of three large mental hospitals

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. We compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER) where possible. 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) out chi-square tests show a significant association between most of the ADRs in smoking status and hospital admissions and some in gender and age groups. Further, the data was combined from three trusts, and chi-square tests were applied to estimate the average effect of ADRs in each monthly interval. Conclusion: A better understanding of how the drug works in the real world can complement clinical trials and precision medicine

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

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

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

Dementia severity at death: a register-based cohort study

Abstract 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