Rashmi Patel

Association of cannabis use with hospital admission and antipsychotic treatment failure in first episode psychosis: an observational study

Objective To investigate whether cannabis use is associated with increased risk of relapse, as indexed by number of hospital admissions, and whether antipsychotic treatment failure, as indexed by number of unique antipsychotics prescribed, may mediate this effect in a large data set of patients with first episode psychosis (FEP). Design Observational study with exploratory mediation analysis. Setting Anonymised electronic mental health record data from the South London and Maudsley NHS Foundation Trust. Participants 2026 people presenting to early intervention services with FEP. Exposure Cannabis use at presentation, identified using natural language processing. Main outcome measures admission to psychiatric hospital and clozapine prescription up to 5 years following presentation. Mediator Number of unique antipsychotics prescribed. Results Cannabis use was present in 46.3% of the sample at first presentation and was particularly common in patients who were 16–25, male and single. It was associated with increased frequency of hospital admission (incidence rate ratio 1.50, 95% CI 1.25 to 1.80), increased likelihood of compulsory admission (OR 1.55, 1.16 to 2.08) and greater number of days spent in hospital (β coefficient 35.1 days, 12.1 to 58.1). The number of unique antipsychotics prescribed, mediated increased frequency of hospital admission (natural indirect effect 1.09, 95% CI 1.01 to 1.18; total effect 1.50, 1.21 to 1.87), increased likelihood of compulsory admission (natural indirect effect (NIE) 1.27, 1.03 to 1.58; total effect (TE) 1.76, 0.81 to 3.84) and greater number of days spent in hospital (NIE 17.9, 2.4 to 33.4; TE 34.8, 11.6 to 58.1). Conclusions Cannabis use in patients with FEP was associated with an increased likelihood of hospital admission. This was linked to the prescription of several different antipsychotic drugs, indicating clinical judgement of antipsychotic treatment failure. Together, this suggests that cannabis use might be associated with worse clinical outcomes in psychosis by contributing towards failure of antipsychotic treatment.

Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes

Objectives Mood instability is a clinically important phenomenon but has received relatively little research attention. The objective of this study was to assess the impact of mood instability on clinical outcomes in a large sample of people receiving secondary mental healthcare. Design Observational study using an anonymised electronic health record case register. Setting South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK. Participants 27 704 adults presenting to SLaM between April 2006 and March 2013 with a psychotic, affective or personality disorder. Exposure The presence of mood instability within 1 month of presentation, identified using natural language processing (NLP). Main outcome measures The number of days spent in hospital, frequency of hospital admission, compulsory hospital admission and prescription of antipsychotics or non-antipsychotic mood stabilisers over a 5-year follow-up period. Results Mood instability was documented in 12.1% of people presenting to mental healthcare services. It was most frequently documented in people with bipolar disorder (22.6%), but was common in people with personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (β coefficient 18.5, 95% CI 12.1 to 24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75 to 2.17), greater likelihood of compulsory admission (OR 2.73, 2.34 to 3.19) and an increased likelihood of prescription of antipsychotics (2.03, 1.75 to 2.35) or non-antipsychotic mood stabilisers (2.07, 1.77 to 2.41). Conclusions Mood instability occurs in a wide range of mental disorders and is not limited to affective disorders. It is generally associated with relatively poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also suggest that targeted interventions for mood instability may be useful in patients who do not have a formal affective disorder.

Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method

Objectives To identify negative symptoms in the clinical records of a large sample of patients with schizophrenia using natural language processing and assess their relationship with clinical outcomes. Design Observational study using an anonymised electronic health record case register. Setting South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK. Participants 7678 patients with schizophrenia receiving care during 2011. Main outcome measures Hospital admission, readmission and duration of admission. Results 10 different negative symptoms were ascertained with precision statistics above 0.80. 41% of patients had 2 or more negative symptoms. Negative symptoms were associated with younger age, male gender and single marital status, and with increased likelihood of hospital admission (OR 1.24, 95% CI 1.10 to 1.39), longer duration of admission (β-coefficient 20.5 days, 7.6–33.5), and increased likelihood of readmission following discharge (OR 1.58, 1.28 to 1.95). Conclusions Negative symptoms were common and associated with adverse clinical outcomes, consistent with evidence that these symptoms account for much of the disability associated with schizophrenia. Natural language processing provides a means of conducting research in large representative samples of patients, using data recorded during routine clinical practice.

Services for people at high risk improve outcomes in patients with first episode psychosis

About one‐third of patients referred to services for people at high risk for psychosis may have already developed a first episode of psychosis (FEP). We compared clinical outcomes in FEP patients who presented to either high risk or conventional mental health services.

Do antidepressants increase the risk of mania and bipolar disorder in people with depression? A retrospective electronic case register cohort study

Objectives To investigate the association between antidepressant therapy and the later onset of mania/bipolar disorder. Design Retrospective cohort study using an anonymised electronic health record case register. Setting South London and Maudsley National Health Service (NHS) Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK. Participants 21 012 adults presenting to SLaM between 1 April 2006 and 31 March 2013 with unipolar depression. Exposure Prior antidepressant therapy recorded in electronic health records. Main outcome measure Time to subsequent diagnosis of mania or bipolar disorder from date of diagnosis of unipolar depression, censored at 31 March 2014. Methods Multivariable Cox regression analysis with age and gender as covariates. Results The overall incidence rate of mania/bipolar disorder was 10.9 per 1000 person-years. The peak incidence of mania/bipolar disorder incidence was seen in patients aged between 26 and 35 years (12.3 per 1000 person-years). Prior antidepressant treatment was associated with an increased incidence of mania/bipolar disorder ranging from 13.1 to 19.1 per 1000 person-years. Multivariable analysis indicated a significant association with selective serotonin reuptake inhibitors (HR 1.34, 95% CI 1.18 to 1.52) and venlafaxine (1.35, 1.07 to 1.70). Conclusions In people with unipolar depression, antidepressant treatment is associated with an increased risk of subsequent mania/bipolar disorder. These findings highlight the importance of considering risk factors for mania when treating people with depression.

Delays before Diagnosis and Initiation of Treatment in Patients Presenting to Mental Health Services with Bipolar Disorder

Background Bipolar disorder is a significant cause of morbidity and mortality. Although existing treatments are effective, there is often a substantial delay before diagnosis and treatment initiation. We sought to investigate factors associated with the delay before diagnosis of bipolar disorder and the onset of treatment in secondary mental healthcare. Method Retrospective cohort study using anonymised electronic mental health record data from the South London and Maudsley NHS Foundation Trust (SLaM) Biomedical Research Centre (BRC) Case Register on 1364 adults diagnosed with bipolar disorder between 2007 and 2012. The following predictor variables were analysed in a multivariable Cox regression analysis: age, gender, ethnicity, compulsory admission to hospital under the UK Mental Health Act, marital status and other diagnoses prior to bipolar disorder. The outcomes were time to recorded diagnosis from first presentation to specialist mental health services (the diagnostic delay), and time to the start of appropriate therapy (treatment delay). Results The median diagnostic delay was 62 days (interquartile range: 17–243) and median treatment delay was 31 days (4–122). Compulsory hospital admission was associated with a significant reduction in both diagnostic delay (hazard ratio 2.58, 95% CI 2.18–3.06) and treatment delay (4.40, 3.63–5.62). Prior diagnoses of other psychiatric disorders were associated with increased diagnostic delay, particularly alcohol (0.48, 0.33–0.41) and substance misuse disorders (0.44, 0.31–0.61). Prior diagnosis of schizophrenia and psychotic depression were associated with reduced treatment delay. Conclusions Some individuals experience a significant delay in diagnosis and treatment of bipolar disorder after initiation of specialist mental healthcare, particularly those who have prior diagnoses of alcohol and substance misuse disorders. These findings highlight a need for further study on strategies to better identify underlying symptoms and offer appropriate treatment sooner in order to facilitate improved clinical outcomes, such as developing specialist early intervention services to identify and treat people with bipolar disorder.

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

Objectives We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research. Design Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries. Setting Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK. Participants The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. Outcome measures Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database. Results We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis. Conclusions This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.

Investigation of negative symptoms in schizophrenia with a machine learning text-mining approach

Abstract Background Negative symptoms account for the greatest burden of illness among individuals with schizophrenia. These symptoms are an increasingly important target for therapy, especially since their presence predicts poor long-term clinical outcomes. However, development of practicable methods for their assessment has been difficult. In this study, we present a novel support vector machine learning method to see whether we could identify the presence of negative symptoms in electronic health records. Methods We used routinely collected clinical data from the Biomedical Research Council Case Register, South London and Maudsley NHS Trust. Data were obtained from the case records of 7678 adults with schizophrenia receiving care in 2011, of whom 1590 were inpatients. A training dataset of around 200 case records from this sample was analysed with the General Architecture for Text Engineering Machine Learning software package to develop a text-mining tool that was subsequently used to estimate the prevalence of negative symptoms in the whole sample. Multivariable logistic and multiple linear regression analyses were done to investigate the association of negative symptomatology with age, sex, relationship status, impairment of activities of daily living, and (for inpatients) length of hospital stay. Findings 4269 patients (55·7%) had at least one negative symptom documented. Negative symptoms were particularly associated with patients who were aged 20–29 years (all other age groups odds ratio [OR] and upper 95% CI limit Interpretation Using a machine learning approach, we were able to identify the presence of negative symptoms in electronic health records. The data suggest that negative symptoms are evident in most patients with schizophrenia and are associated with poor clinical outcomes. These findings highlight the need for the development of new treatments that can alleviate negative symptoms. Furthermore, the increasing use of electronic health records highlights an opportunity to adopt support vector machine learning text-mining approaches to obtain data for research and clinical decision support in other areas of medicine. Funding UK Medical Research Council, National Institute for Health Research, Roche.

Clinical outcomes and mortality associated with weekend admission to psychiatric hospital.

Background Studies indicate that risk of mortality is higher for patients admitted to acute hospitals at the weekend. However, less is known about clinical outcomes among patients admitted to psychiatric hospitals. Aims To investigate whether weekend admission to a psychiatric hospital is associated with worse clinical outcomes. Method Data were obtained from 45 264 consecutive psychiatric hospital admissions. The association of weekend admission with in-patient mortality, duration of hospital admission and risk of readmission was investigated using multivariable regression analyses. Secondary analyses were performed to investigate the distribution of admissions, discharges, in-patient mortality, episodes of seclusion and violent incidents on different days of the week. Results There were 7303 weekend admissions (16.1%). Patients who were aged between 26 and 35 years, female or from a minority ethnic group were more likely to be admitted at the weekend. Patients admitted at the weekend were more likely to present via acute hospital services, other psychiatric hospitals and the criminal justice system than to be admitted directly from their own home. Weekend admission was associated with a shorter duration of admission (B coefficient −21.1 days, 95% CI −24.6 to −17.6, P<0.001) and an increased risk of readmission in the 12 months following index admission (incidence rate ratio 1.13, 95% CI 1.08 to 1.18, P<0.001), but in-patient mortality (odds ratio (OR) = 0.79, 95% CI 0.51 to 1.23, P = 0.30) was not greater than for weekday admission. Fewer episodes of seclusion occurred at the weekend but there was no significant variation in deaths during hospital admission or violent incidents on different days of the week. Conclusions Being admitted at the weekend was not associated with an increased risk of in-patient mortality. However, patients admitted at the weekend had shorter admissions and were more likely to be readmitted, suggesting that they may represent a different clinical population to those admitted during the week. This is an important consideration if mental healthcare services are to be implemented across a 7-day week.

Diagnostic and Prognostic Significance of DSM-5 Attenuated Psychosis Syndrome in Services for Individuals at Ultra High Risk for Psychosis

Background The diagnostic and prognostic significance of the DSM-5-defined Attenuated Psychosis Syndrome (DSM-5-APS) in individuals undergoing an ultra high risk (UHR) clinical assessment for suspicion of psychosis risk is unknown. Methods Prospective cohort study including all consecutive help-seeking individuals undergoing both a DSM-5-APS and a Comprehensive Assessment of At Risk Mental States (CAARMS 12/2006) assessment for psychosis risk at the Outreach and Support in South London (OASIS) UHR service (March 2013-April 2014). The diagnostic significance of DSM-5-APS was assessed with percent overall agreement, prevalence bias adjusted kappa, Bowkers test, Stuart-Maxwell test, residual analysis; the prognostic significance with Cox regression, Kaplan-Meier failure function, time-dependent area under the curve (AUC) and net benefits analysis. The impact of specific revisions of the DSM-5-APS was further tested. Result In 203 help-seeking individuals undergoing UHR assessment, the agreement between the DSM-5-APS and the CAARMS 12/2006 was only moderate (kappa 0.59). Among 142 nonpsychotic cases, those meeting DSM-5-APS criteria had a 5-fold probability (HR = 5.379) of developing psychosis compared to those not meeting DSM-5-APS criteria, with a 21-month cumulative risk of psychosis of 28.17% vs 6.49%, respectively. The DSM-5-APS prognostic accuracy was acceptable (AUC 0.76 at 24 months) and similar to the CAARMS 12/2006. The DSM-5-APS designation may be clinically useful to guide the provision of indicated interventions within a 7%-35% (2-year) range of psychosis risk. The removal of the criterion E or C of the DSM-5-APS may improve its prognostic performance and transdiagnostic value. Conclusions The DSM-5-APS designation may be clinically useful in individuals accessing clinical services for psychosis prevention.