Clinical Record Interactive Search Research Articles

Obsessive-compulsive symptoms relating to psychosocial functioning for people with schizophrenia, schizoaffective disorder, or bipolar disorder.

To assess the psychosocial functioning concerning obsessive-compulsive symptoms (OCS) and/or obsessive-compulsive disorder (OCD) comorbidity in people with schizophrenia, schizoaffective disorder, or bipolar disorder diagnosed in a large case register database in Southeast London. Data were retrieved from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) register using Clinical Record Interactive Search (CRIS) system, a platform allowing research on full but de-identified electronic health records for secondary and tertiary mental healthcare services. Information of schizophrenia, schizoaffective disorder, bipolar disorder diagnosis and OCS/OCD status was ascertained from structural or free-text fields through natural language processing (NLP) algorithms based on artificial intelligence techniques during the observation window of January 2007 to December 2016. Associations between comorbid OCS/OCD and recorded Health of the Nation Outcome Scales (HoNOS) for problems with activities of daily living (ADLs), living conditions, occupational and recreational activities, and relationships were estimated by logistic regression with socio-demographic confounders controlled. Of 15,412 subjects diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, 2,358 (15.3%) experienced OCS without OCD, and 2,586 (16.8%) had OCD recorded. The presence of OCS/OCD was associated with more problems with relationships (adj.OR = 1.34, 95% CI: 1.25-1.44), ADLs (adj.OR = 1.31, 95%CI: 1.22-1.41), and living conditions (adj.OR = 1.31, 95% CI: 1.22-1.41). Sensitivity analysis revealed similar outcomes. Comorbid OCS/OCD was associated with poorer psychosocial functioning in people with schizophrenia, schizoaffective disorder, or bipolar disorder. This finding highlights the importance of identification and treatment of comorbid OCS among this vulnerable patient group.

Lay review of the South London and Maudsley NHS Foundation Trust’s (SLaM) Clinical Records Interactive Search (CRIS) system website to better meet UK Heath Data Research Alliance Transparency Standards.

Lay review of the South London and Maudsley NHS Foundation Trust’s (SLaM) Clinical Records Interactive Search (CRIS) system website to better meet UK Heath Data Research Alliance Transparency Standards.

Impact of inconsistent ethnicity recordings on estimates of inequality in child health and education data: a data linkage study of Child and Adolescent Mental Health Services in South London

ObjectivesEthnicity data are critical for identifying inequalities, but previous studies suggest that ethnicity is not consistently recorded between different administrative datasets. With researchers increasingly leveraging cross-domain data linkages, we investigated...

Improving our understanding of the social determinants of mental health: a data linkage study of mental health records and the 2011 UK census

ObjectivesTo address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider....

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.

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

Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach.

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

Sociodemographic and clinical predictors of delay to and length of stay with early intervention for psychosis service: findings from the CRIS-FEP study

PurposeWe investigated the influence of sociodemographic and clinical characteristics on delay to early intervention service (EIS) and the length of stay (LOS) with EIS.MethodsWe used incidence data linked to the Clinical Record Interactive Search—First Episode Psychosis (CRIS-FEP) study. We followed the patients from May 2010 to March 2016. We performed multivariable Cox regression to estimate hazard ratios of delay to EIS. Negative binomial regression was used to determine LOS with EIS by sociodemographic and clinical characteristics, controlling for confounders.Results343 patients were eligible for an EIS, 34.1% of whom did not receive the service. Overall, the median delay to EIS was 120 days (IQR; 15–1668); and the median LOS was 130.5 days (IQR 0–663). We found that women (adj.HR 0.58; 95%C I 0.42–0.78), living alone (adj.HR: 0.63; 95% CI 0.43–0.92) and ethnicity (‘Other’: adj.HR 0.47; 95% CI 0.23–0.98) were associated with prolonged delay to EIS. However, family involvement in help-seeking for psychosis (adj.HR 1.37; 95% CI 1.01–1.85) was strongly associated with a shorter delay to EIS. Patients who have used mental health services previously also experienced long delays to EIS.ConclusionsOur analyses highlight the link between sociodemographic status, help-seeking behaviours, and delay to EIS. Our findings also show the vulnerability faced by those with a previous mental health problem who later develop psychosis in receiving specialist treatment for psychosis. Initiatives that ameliorate indicators of social disadvantage are urgently needed to reduce health inequalities and improve clinical outcomes.

Antidepressant drug prescription and incidence of COVID-19 in mental health outpatients: a retrospective cohort study

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

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

ObjectivesTo assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SettingThe Clinical Records...

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

IntroductionPeople often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers...

Association between depression diagnosis and educational attainment trajectories: an historical cohort study using linked data.

Depression symptoms are thought to be associated with lower educational attainment, but patterns of change in attainment among those who receive a clinical diagnosis of depression at any point during childhood and adolescence remain unclear. We conducted a secondary analysis of an existing data linkage between a national educational dataset (National Pupil Database) and pseudonymised electronic health records (Clinical Record Interactive Search) from a large mental healthcare provider in London, United Kingdom (2007 to 2013). A cohort of 222,027 pupils were included. We used Growth Mixture Modelling (GMM) and stakeholder input to estimate trajectories of standardised educational attainment over School Years 2, 6 and 11. Multinomial logistic regression analyses were then used to investigate the association between resulting educational attainment trajectory membership (outcome) and depression diagnosis any time before age 18 (exposure). A five-trajectory GMM solution for attainment was derived: (1) average/high-stable, (2) average-modest declining, (3) average-steep declining, (4) low-improving and (5) low-stable. After adjusting for clinical and sociodemographic covariates, having a depression diagnosis before age 18 was associated with occupying the average-modest declining trajectory (RRR=2.80, 95% CI 2.36-3.32, p < .001) or the average-steep declining trajectory (RRR=3.54, 95% CI 3.10-4.04, p < .001), as compared to the average/high-stable trajectory. Receiving a diagnosis of depression before age 18 was associated with a relative decline in attainment throughout school. While these findings cannot support a causal direction, they nonetheless suggest a need for timely mental health and educational support among pupils struggling with depression.

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 POINTS NICE 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.

Evaluating physical urban features in several mental illnesses using electronic health record data.

ObjectivesUnderstanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses.Materials and MethodsPhysical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually.ResultsThe prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data.DiscussionThe urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments.ConclusionUrban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.

COVID-19 ethnic inequalities in mental health and multimorbidities: protocol for the COVEIMM study

PurposeThe COVID-19 pandemic may have exacerbated ethnic health inequalities, particularly in people with multiple long-term health conditions, the interplay with mental health is unclear. This study investigates the impact of the pandemic on the association of ethnicity and multimorbidity with mortality/service use among adults, in people living with severe mental illnesses (SMI).MethodsThis study will utilise secondary mental healthcare records via the Clinical Record Interactive Search (CRIS) and nationally representative primary care records through the Clinical Practice Interactive Research Database (CPRD). Quasi-experimental designs will be employed to quantify the impact of COVID-19 on mental health service use and excess mortality by ethnicity, in people living with severe mental health conditions. Up to 50 qualitative interviews will also be conducted, co-produced with peer researchers; findings will be synthesised with quantitative insights to provide in-depth understanding of observed associations.Results81,483 people in CRIS with schizophrenia spectrum, bipolar or affective disorder diagnoses, were alive from 1st January 2019. Psychiatric multimorbidities in the CRIS sample were comorbid somatoform disorders (30%), substance use disorders (14%) and personality disorders (12%). In CPRD, of 678,842 individuals with a prior probable diagnosis of COVID-19, 1.1% (N = 7493) had an SMI diagnosis. People in the SMI group were more likely to die (9% versus 2% in the non-SMI sample) and were more likely to have mental and physical multimorbidities.ConclusionThe effect of COVID-19 on people from minority ethnic backgrounds with SMI and multimorbidities remains under-studied. The present mixed methods study aims to address this gap.

Natural Language Processing of Electronic Patient Records to Predict Psychiatric Inpatients at Risk of Early Readmission to Hospital Using Predictive Models Derived Through Machine Learning

AimsPsychiatric readmissions cause a burden on the healthcare system, incur a monetary cost and cause additional distress to acutely unwell patients. This project explores the use of the free-text of electronic patient records to predict inpatients in psychiatric hospitals at risk of readmission using predictive models generated by machine learning.MethodsFree-text was extracted from the electronic patient records of patients admitted to hospitals in Birmingham and Solihull Mental Health Foundation Trust (BSMHFT) during the five years 2015–2019 inclusive. The anonymised records were obtained via the CRIS (Clinical Record Interactive Search) database. A total of 17208 records were extracted.The free-text entered by clinicians during an admission was extracted and processed using techniques of natural language processing to generate input vectors suitable to be used with machine learning algorithms. tf-idf (term frequency-inverse document frequency) vectors were used.A selection of algorithms were used to train predictive models. Two-thirds of the records were used as training data with the remainder as test data. Baseline model performance was assessed and then best-performing candidates underwent hyperparameter optimisation using five-fold cross-validation to improve performance. Bayesian optimisation was used to automate hyperparameter tuning during cross-validation. Hyperparameters were optimised on the log loss function. As the dataset was imbalanced with negative instances outnumbering positive instances to a significant degree, various techniques such as random undersampling of negative instances in the training data were used to deal with class imbalance throughout this process. Following cross-validation, the best-performing models underwent performance analysis.Models were used to make predictions on the test data. Performance was assessed using F1-measures, precision-recall curves and the average precision metric (equivalent to area under the precision-recall curve). These metrics were chosen due to their suitability in assessing models trained on imbalanced datasets.ResultsThe best F1 score obtained was 0.233 using a Random Forest model trained using unigram tf-idf vectors of 500 token dimension.The best average precision obtained was 0.157 using a Support Vector Machine trained using unigram tf-idf vectors of 2000 token dimension.Both the above results required the use of random oversampling of positive instances to improve performance on the imbalanced dataset.ConclusionThe performance indicates that the models generated are unlikely to have significant practical utility. Nevertheless, this exploratory project has produced a processed dataset with knowledge about its characteristics. This could be used for the further development of models using more complex techniques such as language modelling using neural networks.

The Impact of the COVID-19 Pandemic on the Incidence of First Episode Psychosis in South London

AimsTransmission of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to a global pandemic. Many studies are underway to ascertain the mental health impact of this seismic event, however no study has investigated its effect on psychosis incidence. We hypothesise that the overall crude incidence rates of first episode psychosis (FEP) will be higher during the pandemic when comparing the same area of South London in defined pre-pandemic and pandemic time periods.MethodsClinical records for all patients aged 18 to 64 years presenting to early intervention in psychosis services in the London boroughs of Southwark and Lambeth between July 1st 2019 to December 31st 2019 (pre-pandemic period) and July 1st 2020 to December 31st 2020 (pandemic period) were extracted from the Clinical Record Interactive Search (CRiS), an online database containing anonymised patient records. All patients were manually screened using the Screening Schedule for Psychosis to confirm FEP, with 104/235 cases meeting criteria for FEP in the pre-pandemic period compared with 158/376 in the pandemic period. Crude, age-standardised, and sex-standardised incidence rates and ratios were calculated for interpretation.ResultsThe crude incidence rate of FEP was significantly higher in the pandemic period (68.3, 95% CI:[57.6 ; 78.9]) than the pre-pandemic period (44.9, 95% CI:[36.3 ; 53.6]). The crude incidence ratio was 1.52 (95% CI:[1.28 ; 1.77]), indicating that the overall crude incidence of FEP in the pandemic period was significantly higher (52%) than in the pre-pandemic period. The directionality and statistical significance of this ratio was unperturbed by standardisation for age (SIR = 1.45, 95% CI[1.23 ; 1.70]) and sex (SIR = 1.56, 95% CI[1.33 ; 1.83]).ConclusionOur results suggest that the COVID-19 pandemic has had a significant impact on the incidence of FEP in the South London boroughs of Southwark and Lambeth. Further research is required to elucidate the factors contributing to this increase to inform targeted interventions and prevent deterioration in at-risk patients.

Associated Mortality Risk of Atypical Antipsychotic Medication in Individuals With Dementia (AMRAAD): A Clinical Cohort Study

AimsAntipsychotic medications such as risperidone, olanzapine and aripiprazole are used to treat psychological and behavioural symptoms among dementia patients. Current evidence indicate prescription rates for antipsychotics vary and wider consensus to evaluate clinical epidemiological outcomes is limited. This study aims to investigate the potential impact of atypical antipsychotics on the mortality of patients with dementia.MethodsA retrospective clinical cohort study was developed to review United Kingdom Clinical Record Interactive Search system based data between January 1, 2013 to December 31, 2017. A descriptive statistical method was used to analyse the data. Mini Mental State Examination (MMSE) scores were used to assess the severity and stage of disease progression. A study specific cox proportional hazards model was developed to evaluate the relationship between survival following diagnosis and other variables.ResultsA total sample size of 1692 patients were identified using natural language processing of which, 587 were prescribed olanzapine, quetiapine, or risperidone (common group) whilst 893 (control group) were not prescribed any antipsychotics. Patients prescribed olanzapine and Risperidone showed similar risk of death [hazard ratio (HR) = 1.32; 95% confidence interval (CI): 1.08–1.60; P &lt; 0.01], (HR = 1.35; 95%CI: 1.18–1.54; P &lt; 0.001). Patients prescribed Quetiapine showed no significant association (HR = 1.09; 95%CI: 0.90–1.34; P = 0.38). Factors associated with a lower risk of death were elevated MMSE score at diagnosis (HR = 0.72; 95%CI: 0.62–0.83; P &lt; 0.001) along with other demographic factors such as women (HR = 0.73; 95%CI: 0.64–0.82; P &lt; 0.001) and being of a Caucasian British group (HR = 0.82; 95%CI: 0.72–0.94; P &lt; 0.01).ConclusionA significant mortality risk was identified among those prescribed olanzapine and risperidone which contradicts previous findings although the study designs used were different. Comprehensive research should be conducted to better assess clinical epidemiological outcomes associated with diagnosis and therapies to improve clinical management of these patients.

Sociodemographic characteristics associated with parenthood amongst patients with a psychotic diagnosis: a cross-sectional study using patient clinical records

PurposeEstimates of parenthood in individuals with psychosis range from 27 to 63%. This number has likely increased due to the introduction of newer anti-psychotics and shorter hospital stays. The problems of psychosis can affect patients’ capacity to offer the consistent, responsive care required for healthy child development. The following research questions were assessed: (1) what proportion of these patients have their children correctly recorded in their clinical notes, (2) what proportion of patients in secondary care with a psychotic diagnosis have children, and (3) what sociodemographic characteristics are associated with parenthood in this population.MethodsThis study used CRIS (Clinical Record Interactive Search) to search for patients with a diagnosis of non-affective or affective psychosis (F20–29, F31.2 or F31.5) within a UK NHS Trust. A binomial regression model was fitted to identify the variables associated with parenthood.ResultsFewer than half of the parents in the sample had their children recorded in the correct field in their clinical notes. Of 5173 patients with psychosis, 2006 (38.8%) were parents. Characteristics associated with parenthood included being female, older age, higher socioeconomic status, renting or owning, having ever been married, being unemployed, not being White (British) and not having a diagnosis of schizophrenia.ConclusionOver one-third of patients with psychosis were parents, and the study indicates that not all NHS Trusts are recording dependants accurately. Many variables were strongly associated with parenthood and these findings may help target interventions for this population.

Associations of presenting symptoms and subsequent adverse clinical outcomes in people with unipolar depression: a prospective natural language processing (NLP), transdiagnostic, network analysis of electronic health record (EHR) data

ObjectiveTo investigate the associations of symptoms of mania and depression with clinical outcomes in people with unipolar depression.DesignA natural language processing electronic health record study. We used network analysis to...

Associated mortality risk of atypical antipsychotic medication in individuals with dementia.

BACKGROUNDAntipsychotic medications such as risperidone, olanzapine and aripiprazole are used to treat psychological and behavioural symptoms among dementia patients. Current evidence indicate prescription rates for antipsychotics vary and wider consensus to evaluate clinical epidemiological outcomes is limited. AIMTo investigate the potential impact of atypical antipsychotics on the mortality of patients with dementia.METHODSA retrospective clinical cohort study was developed to review United Kingdom Clinical Record Interactive Search system based data between January 1, 2013 to December 31, 2017. A descriptive statistical method was used to analyse the data. Mini Mental State Examination (MMSE) scores were used to assess the severity and stage of disease progression. A cox proportional hazards model was developed to evaluate the relationship between survival following diagnosis and other variables.RESULTSA total of 1692 patients were identified using natural language processing of which, 587 were prescribed olanzapine, quetiapine or risperidone (common group) whilst 893 (control group) were not prescribed any antipsychotics. Patients prescribed olanzapine showed an increased risk of death [hazard ratio (HR) = 1.32; 95% confidence interval (CI): 1.08-1.60; P < 0.01], as did those with risperidone (HR = 1.35; 95%CI: 1.18-1.54; P < 0.001). Patients prescribed quetiapine showed no significant association (HR = 1.09; 95%CI: 0.90-1.34; P = 0.38). Factors associated with a lower risk of death were: High MMSE score at diagnosis (HR = 0.72; 95%CI: 0.62-0.83; P < 0.001), identifying as female (HR = 0.73; 95%CI: 0.64-0.82; P < 0.001), and being of a White-British ethnic group (HR = 0.82; 95%CI: 0.72-0.94; P < 0.01).CONCLUSIONA significant mortality risk was identified among those prescribed olanzapine and risperidone which contradicts previous findings although the study designs used were different. Comprehensive research should be conducted to better assess clinical epidemiological outcomes associated with diagnosis and therapies to improve clinical management of these patients.