Compacted doctrines: Empson and the meanings of words.

This chapter describes the account of word meaning advanced by William Empson in 'The Structure of Complex Words' (1951). Exposition is supported by detailed historical analysis of the word wit, chosen to illustrate the possibilities, as well as difficulties, of the framework Empson devised to investigate meaning ‘equations’ that his selected words are capable of entering into. Noting the apparent likeness between 'Complex Words' and Raymond Williams’s slightly later 'Keywords' (1976/1983), including use by both authors of the term ‘keyword’, the chapter examines important differences of approach between the two authors (differences revealed especially in a review Empson published of Williams’s 'Keywords', discussed in the chapter). In conclusion, it is suggested that despite differences between them some similar implications regarding meaning follow from the work of both authors. These include the idea that, rather than merely describing distinct word meanings, or even meanings attributed to words by individual speakers, historical analyses of meaning should focus on social practices that accompany language use, including practices which find their existence and articulation in institutions. In this more social view of meaning, it is suggested, meaning and social identity are kinds of effect, or produced relation, rather than stable elements outside language with which to begin an analysis

Do schizophrenic patients who managed to get to university have a non-neurodevelopmental form of illness?

Background. Many people who develop schizophrenia have impairments in intellectual and social functioning that are detectable from early childhood. However, some patients do not exhibit such deficits, and this suggests that they may have suffered less neurodevelopmental damage. We hypothesized that the aetiology and form of schizophrenia may differ in such patients. We therefore studied a group of schizophrenic patients who were functioning well enough to enter university prior to illness onset. Methods. The casenotes of 46 university-educated patients and 48 non-university-educated patients were rated on several schedules including the OPCRIT checklist, and the two groups were compared using univariate statistical techniques. Principal components analysis was then performed using data from all patients, and the factor scores for each principal component were compared between groups. Results. Univariate analyses showed the university-educated patients had an excess of depressive symptoms, and a paucity of core schizophrenic symptoms. Four principal components emerged in the principal components analysis: mania, biological depression, schizophrenic symptoms, and a reactive depression. University-educated patients scored significantly higher on the reactive depression principal component, and lower on the schizophrenic symptoms principal component, than the non-university-educated patients. Conclusions. University-educated patients may have a non-developmental subtype of schizophrenia.link_to_subscribed_fulltex

Validation of an algorithm-based definition of treatment resistance in patients with schizophrenia

Large-scale pharmacoepidemiological research on treatment resistance relies on accurate identification of people with treatment-resistant schizophrenia (TRS) based on data that are retrievable from administrative registers. This is usually approached by operationalising clinical treatment guidelines by using prescription and hospital admission information. We examined the accuracy of an algorithm-based definition of TRS based on clozapine prescription and/or meeting algorithm-based eligibility criteria for clozapine against a gold standard definition using case notes. We additionally validated a definition entirely based on clozapine prescription. 139 schizophrenia patients aged 18–65 years were followed for a mean of 5 years after first presentation to psychiatric services in South-London, UK. The diagnostic accuracy of the algorithm-based measure against the gold standard was measured with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A total of 45 (32.4%) schizophrenia patients met the criteria for the gold standard definition of TRS; applying the algorithm-based definition to the same cohort led to 44 (31.7%) patients fulfilling criteria for TRS with sensitivity, specificity, PPV and NPV of 62.2%, 83.0%, 63.6% and 82.1%, respectively. The definition based on lifetime clozapine prescription had sensitivity, specificity, PPV and NPV of 40.0%, 94.7%, 78.3% and 76.7%, respectively. Although a perfect definition of TRS cannot be derived from available prescription and hospital registers, these results indicate that researchers can confidently use registries to identify individuals with TRS for research and clinical practices

Clozapine use in childhood and adolescent schizophrenia: A nationwide population-based study

Early onset schizophrenia (EOS) begins in childhood or adolescence. EOS is associated with poor treatment response and may benefit from timely use of clozapine. This study aimed to identify the predictors of clozapine use in EOS and characterize the clinical profile and outcome of clozapine-treated youths with schizophrenia. We conducted a nationwide population-based study using linked data from Danish medical registries. We examined all incident cases of EOS (i.e., cases diagnosed prior to their 18th birthday) between December 31st 1994 and December 31st 2006 and characterized their demographic, clinical and treatment profiles. We then used multivariable cox proportional hazard models to identify predictors of clozapine treatment in this patient population. We identified 662 EOS cases (1.9% of all schizophrenia cases), of whom 108 (17.6%) had commenced clozapine by December 31st 2008. Patients had on average 3 antipsychotic trials prior to clozapine initiation. The mean interval between first antipsychotic treatment and clozapine initiation was 3.2 (2.9) years. Older age at diagnosis of schizophrenia [HR=1.2, 95% CI (1.05-1.4), p=0.01], family history of schizophrenia [HR=2.1, 95% CI (1.1-3.04), p=0.02] and attempted suicide [HR=1.8, 95% CI (1.1-3.04), p=0.02] emerged as significant predictors of clozapine use. The majority of patients (n=96, 88.8%) prescribed clozapine appeared to have a favorable clinical response as indicated by continued prescription redemption and improved occupational outcomes. Our findings support current recommendations for the timely use of clozapine in EOS

Scholastic achievement at age 16 and risk of schizophrenia and other psychoses: a national cohort study

Background: There is abundant evidence that schizophrenia is associated with cognitive deficits in childhood. However, previous studies investigating school performance have been inconclusive. Furthermore, there are several biological and social factors that could confound the association. We investigated whether school performance at age 16 is associated with risk of adult schizophrenia and other psychoses in a large national cohort, while controlling for multiple confounders. Method: Using a national sample of 907 011 individuals born in Sweden between 1973 and 1983, we used Cox regression to assess whether scholastic achievement at age 15-16 predicted hospital admission for psychosis between ages 17 and 31, adjusting for potential confounders. Results: Poor school performance was associated with increased rates of schizophrenia [hazard ratio (HR) 3.9, 95% confidence interval (CI) 2.8-5.3], schizo-affective disorder (HR 4.2, 95% CI 1.9-9.1) and other psychoses (HR 3.0, 95% CI 2.3-4.0). Receiving the lowest (E) grade was significantly associated with risk for schizophrenia and other psychoses in every school subject. There was no evidence of confounding by migrant status, low birthweight, hypoxia, parental education level or socio-economic group. Conclusions: Poor school performance across all domains is strongly associated with risk for schizophrenia and other psychoses. Copyright © 2007 Cambridge University Press.link_to_subscribed_fulltex

Extracting antipsychotic polypharmacy data from electronic health records: developing and evaluating a novel process

Background Antipsychotic prescription information is commonly derived from structured fields in clinical health records. However, utilising diverse and comprehensive sources of information is especially important when investigating less frequent patterns of medication prescribing such as antipsychotic polypharmacy (APP). This study describes and evaluates a novel method of extracting APP data from both structured and free-text fields in electronic health records (EHRs), and its use for research purposes. Methods Using anonymised EHRs, we identified a cohort of patients with serious mental illness (SMI) who were treated in South London and Maudsley NHS Foundation Trust mental health care services between 1 January and 30 June 2012. Information about antipsychotic co-prescribing was extracted using a combination of natural language processing and a bespoke algorithm. The validity of the data derived through this process was assessed against a manually coded gold standard to establish precision and recall. Lastly, we estimated the prevalence and patterns of antipsychotic polypharmacy. Results Individual instances of antipsychotic prescribing were detected with high precision (0.94 to 0.97) and moderate recall (0.57-0.77). We detected baseline APP (two or more antipsychotics prescribed in any 6-week window) with 0.92 precision and 0.74 recall and long-term APP (antipsychotic co-prescribing for 6 months) with 0.94 precision and 0.60 recall. Of the 7,201 SMI patients receiving active care during the observation period, 338 (4.7 %; 95 % CI 4.2-5.2) were identified as receiving long-term APP. Two second generation antipsychotics (64.8 %); and first -second generation antipsychotics were most commonly co-prescribed (32.5 %). Conclusions These results suggest that this is a potentially practical tool for identifying polypharmacy from mental health EHRs on a large scale. Furthermore, extracted data can be used to allow researchers to characterize patterns of polypharmacy over time including different drug combinations, trends in polypharmacy prescribing, predictors of polypharmacy prescribing and the impact of polypharmacy on patient outcomes

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

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

This is the author accepted manuscript. The final version is available from OUP via the DOI in this recordThe prevalence of negative symptoms (NS) at first episode of early-onset psychosis (EOP), and their effect on psychosis prognosis is unclear. In a sample of 638 children with EOP (aged 10-17 y, 51% male), we assessed (1) the prevalence of NS at first presentation to mental health services and (2) whether NS predicted eventual development of multiple treatment failure (MTF) prior to the age of 18 (defined by initiation of a third trial of novel antipsychotic due to prior insufficient response, intolerable adverse-effects or non-adherence). Data were extracted from the electronic health records held by child inpatient and community-based services in South London, United Kingdom. Natural Language Processing tools were used to measure the presence of Marder Factor NS and antipsychotic use. The association between presenting with ≥2 NS and the development of MTF over a 5-year period was modeled using Cox regression. Out of the 638 children, 37.5% showed ≥2 NS at first presentation, and 124 (19.3%) developed MTF prior to the age of 18. The presence of NS at first episode was significantly associated with MTF (adjusted hazard ratio 1.62, 95% CI 1.07-2.46; P = .02) after controlling for a number of potential confounders including psychosis diagnostic classification, positive symptoms, comorbid depression, and family history of psychosis. Other factors associated with MTF included comorbid autism spectrum disorder, older age at first presentation, Black ethnicity, and family history of psychosis. In EOP, NS at first episode are prevalent and may help identify a subset of children at higher risk of responding poorly to antipsychotics.J.D. received supported by a Medical Research Council (MRC) Clinical Research Training Fellowship (MR/L017105/1) and Psychiatry Research Trust Peggy Pollak Research Fellowship in Developmental Psychiatry. H.D. and S.L. have received salary support from the Foundation of Professional Services to Adolescents, UK. R.D.H. was funded by an MRC Fellowship (MR/J01219X/1). R.P. was funded by an MRC CRTF (MR/K002813/1). C.A., L.P-C., and C.M.D-C. have held grants from the Spanish Ministry of Economy, Industry and Competitiveness. Instituto de Salud Carlos III, co-financed by ERDF Funds from the European Commission, “A way of making Europe,” CIBERSAM, Madrid Regional Government (S2010/BMD-2422 AGES), European Union Structural Funds and European Union Seventh Framework Program under grant agreements FP7-HEALTH-2009-2.2.1-2-241909 (EU-GEI), FP7-HEALTH-2009-2.2.1-3-242114 (OPTiMISE), FP7-HEALTH-2013-2.2.1-2-603196 (PSYSCAN)and FP7- HEALTH-2013-2.2.1-2-602478 (METSY); European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No-115916; PRISM); Fundación Alicia Koplowitz and Fundación Mutua Madrileña. M.H., J.H.M. and H.S. receive salary support from the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a Remote Monitoring Platform

Background: There is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis. Objective: This study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes. Methods: A total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater. Results: Overall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001). Conclusions: Extended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse