Defining severity of personality disorder using electronic health records: short report.

Severity of personality disorder is an important determinant of future health. However, this key prognostic variable is not captured in routine clinical practice. Using a large clinical data-set, we explored the predictive validity of items from the Health of Nation Outcome Scales (HoNOS) as potential indicators of personality disorder severity. For 6912 patients with a personality disorder diagnosis, we examined associations between HoNOS items relating to core personality disorder symptoms (self-harm, difficulty in interpersonal relationships, performance of occupational and social roles, and agitation and aggression) and future health service use. Compared with those with no self-harm problem, the total healthcare cost was 2.74 times higher (95% CI 1.66-4.52; P < 0.001) for individuals with severe to very severe self-harm problems. Other HoNOS items did not demonstrate clear patterns of association with service costs. Self-harm may be a robust indicator of the severity of personality disorder, but further replication work is required.

Adverse outcomes associated with recorded victimization in mental health electronic records during the first UK COVID-19 lockdown.

PURPOSE: The impact of COVID-19 pandemic policies on vulnerable groups such as people with mental health problems who experience violence remains unknown. This study aimed to investigate the prevalence of victimization recorded in mental healthcare records during the first UK lockdown, and associations with subsequent adverse outcomes. METHODS: Using a large mental healthcare database, we identified all adult patients receiving services between 16.12.2019 and 15.06.2020 and extracted records of victimisation between 16.03.2020 and 15.06.2020 (first UK COVID-19 lockdown). We investigated adverse outcomes including acute care, emergency department referrals and all-cause mortality in the year following the lockdown (16.06.2020- 01.11.2021). Multivariable Cox regressions models were constructed, adjusting for socio-demographic, socioeconomic, clinical, and service use factors. RESULTS: Of 21,037 adults receiving mental healthcare over the observation period, 3,610 (17.2%) had victimisation mentioned between 16.03.2020 and 15.06.2020 (first UK COVID-19 lockdown). Service users with mentions of victimisation in their records had an elevated risk for all outcomes: acute care (adjusted HR: 2.1; 95%CI 1.9-2.3, p < 0.001), emergency department referrals (aHR: 2.0; 95%CI 1.8-2.2; p < 0.001), and all-cause mortality (aHR: 1.5; 95%CI 1.1-1.9; p = 0.003), when compared to service users with no recorded victimisation. We did not observe a statistically significant interaction with gender; however, after adjusting for possible confounders, men had slightly higher hazard ratios for all-cause mortality and emergency department referrals than women. CONCLUSION: Patients with documented victimisation during the first UK lockdown were at increased risk for acute care, emergency department referrals and all-cause mortality. Further research is needed into mediating mechanisms.

Clinical correlates of early onset antipsychotic treatment resistance.

BACKGROUND: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies. AIM: This study investigates sociodemographic and clinical correlates of early onset of TRS. METHOD: Employing a retrospective cohort design, we do a secondary analysis of data from a cohort of people with TRS attending the South London and Maudsley. Regression analyses were conducted to identify the correlates of the length of treatment to TRS. Predictors included the following: gender, age, ethnicity, problems with positive symptoms, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics. RESULTS: In a cohort of 164 people with TRS (60% were men), the median length of treatment to TRS was 3 years and 8 months. We observed no cut-off on the length of treatment until TRS presentation differentiating between early and late TRS (i.e. no bimodal distribution). Having mild to very severe problems with hallucinations and delusions at the treatment start was associated with earlier TRS (~19 months earlier). In sensitivity analyses, including only complete cases (subject to selection bias), treatment with a long-acting injectable antipsychotic was additionally associated with later TRS (~15 months later). CONCLUSION: Our findings do not support a clear separation between early and late TRS but rather a continuum of the length of treatment before TRS onset. Having mild to very severe problems with positive symptoms at treatment start predicts earlier onset of TRS.

A predictor model of treatment resistance in schizophrenia using data from electronic health records.

OBJECTIVES: To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. METHODS: We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. RESULTS: We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel's C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. CONCLUSIONS: Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.

Mental healthcare utilisation by patients before and after receiving paliperidone palmitate treatment: mirror image analyses.

OBJECTIVES: To compare mental healthcare use and healthcare professional (HCP) contacts for patients before and after initiation of paliperidone palmitate. SETTING: The South London and Maudsley NHS Foundation Trust (SLAM) Biomedical Research Centre Clinical Record Interactive Search. PARTICIPANTS: We identified all adults with a diagnosis of schizophrenia (International Classification of Diseases 10th Revision: F20.x), who had received paliperidone palmitate prescription for at least 365 days and had at least 1 year of recorded treatment from SLAM, prior to the first recorded receipt of paliperidone palmitate. PRIMARY AND SECONDARY OUTCOME MEASURES: Inpatient and community mental healthcare service use, such as inpatient bed days, number of active days in the service, face-to-face and telephone HCP use in the 12 months before and after paliperidone palmitate initiation. RESULTS: We identified 664 patients initiated on paliperidone palmitate. Following initiation, inpatient bed days were lower, although patients remained active on the service case load longer for both mirror approach 1 (mean difference of inpatient bed days -10.48 (95% CI -15.75 to -5.22); days active 40.67 (95% CI 33.39 to 47.95)) and mirror approach 2 (mean difference of inpatient bed days -23.96 (95% CI -30.01 to -17.92); mean difference of days active 40.69 (95% CI 33.39 to 47.94)). The postinitiation period was further characterised by fewer face-to-face and telephone contacts with medical and social work HCPs, and an increased contact with clinical psychologists. CONCLUSIONS: Our findings indicate a change in the profile of HCP use, consistent with a transition from treatment to possible rehabilitation.

Ethnic inequalities in clozapine use among people with treatment-resistant schizophrenia: a retrospective cohort study using data from electronic clinical records.

PURPOSE: Clozapine is the most effective intervention for treatment-resistant schizophrenia (TRS). Several studies report ethnic disparities in clozapine treatment. However, few studies restrict analyses to TRS cohorts alone or address confounding by benign ethnic neutropenia. This study investigates ethnic equity in access to clozapine treatment for people with treatment-resistant schizophrenia spectrum disorder. METHODS: A retrospective cohort study, using information from 11 years of clinical records (2007-2017) from the South London and Maudsley NHS Trust. We identified a cohort of service-users with TRS using a validated algorithm. We investigated associations between ethnicity and clozapine treatment, adjusting for sociodemographic factors, psychiatric multi-morbidity, substance misuse, neutropenia, and service-use. RESULTS: Among 2239 cases of TRS, Black service-users were less likely to be receive clozapine compared with White British service-users after adjusting for confounders (Black African aOR = 0.49, 95% CI [0.33, 0.74], p = 0.001; Black Caribbean aOR = 0.64, 95% CI [0.43, 0.93], p = 0.019; Black British aOR = 0.61, 95% CI [0.41, 0.91], p = 0.016). It was additionally observed that neutropenia was not related to treatment with clozapine. Also, a detention under the Mental Health Act was negatively associated clozapine receipt, suggesting people with TRS who were detained are less likely to be treated with clozapine. CONCLUSION: Black service-users with TRS were less likely to receive clozapine than White British service-users. Considering the protective effect of treatment with clozapine, these inequities may place Black service-users at higher risk for hospital admissions and mortality.

Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine.

BACKGROUND: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised approaches to treatment. METHODS: This retrospective cohort study used data from the electronic health records of the South London and Maudsley (SLaM) hospital between 2007 and 2011. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression statistical learning approach, we examined 35 sociodemographic and clinical factors' predictive ability of response to clozapine at 3 months of treatment. Response was assessed by the level of change in the severity of the symptoms using the Clinical Global Impression (CGI) scale. RESULTS: We identified 242 service-users with a treatment-resistant psychotic disorder who had their first trial of clozapine and continued the treatment for at least 3 months. The LASSO regression identified three predictors of response to clozapine: higher severity of illness at baseline, female gender and having a comorbid mood disorder. These factors are estimated to explain 18% of the variance in clozapine response. The model's optimism-corrected calibration slope was 1.37, suggesting that the model will underfit when applied to new data. CONCLUSIONS: These findings suggest that women, people with a comorbid mood disorder and those who are most ill at baseline respond better to clozapine. However, the accuracy of the internally validated and recalibrated model was low. Therefore, future research should indicate whether a prediction model developed by including routinely collected data, in combination with biological information, presents adequate predictive ability to be applied in clinical settings.

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.

OBJECTIVE: This paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal violence using electronic health records (EHRs) from a large mental healthcare provider. DESIGN: A multidisciplinary team iteratively developed guidelines for annotating clinical text referring to violence. Keywords were used to generate a dataset which was annotated (ie, classified as affirmed, negated or irrelevant) for: presence of violence, patient status (ie, as perpetrator, witness and/or victim of violence) and violence type (domestic, physical and/or sexual). An NLP approach using a pretrained transformer model, BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) was fine-tuned on the annotated dataset and evaluated using 10-fold cross-validation. SETTING: We used the Clinical Records Interactive Search (CRIS) database, comprising over 500 000 de-identified EHRs of patients within the South London and Maudsley NHS Foundation Trust, a specialist mental healthcare provider serving an urban catchment area. PARTICIPANTS: Searches of CRIS were carried out based on 17 predefined keywords. Randomly selected text fragments were taken from the results for each keyword, amounting to 3771 text fragments from the records of 2832 patients. OUTCOME MEASURES: We estimated precision, recall and F1 score for each NLP model. We examined sociodemographic and clinical variables in patients giving rise to the text data, and frequencies for each annotated violence characteristic. RESULTS: Binary classification models were developed for six labels (violence presence, perpetrator, victim, domestic, physical and sexual). Among annotations affirmed for the presence of any violence, 78% (1724) referred to physical violence, 61% (1350) referred to patients as perpetrator and 33% (731) to domestic violence. NLP models' precision ranged from 89% (perpetrator) to 98% (sexual); recall ranged from 89% (victim, perpetrator) to 97% (sexual). CONCLUSIONS: State of the art NLP models can extract and classify clinical text on violence from EHRs at acceptable levels of scale, efficiency and accuracy.

Gender disparities in clozapine prescription in a cohort of treatment-resistant schizophrenia in the South London and Maudsley case register.

BACKGROUND: Gender disparities in treatment are apparent across many areas of healthcare. There has been little research into whether clozapine prescription, the first-line treatment for treatment-resistant schizophrenia (TRS), is affected by patient gender. METHODS: This retrospective cohort study identified 2244 patients with TRS within the South London and Maudsley NHS Trust, by using a bespoke method validated against a gold-standard, manually coded, dataset of TRS cases. The outcome and exposures were identified from the free-text using natural language processing applications (including machine learning and rules-based approaches) and from information entered in structured fields. Multivariable logistic regression was carried out to calculate the odds ratios for clozapine prescription according to patients' gender, and adjusting for numerous potential confounders including sociodemographic, clinical (e.g., psychiatric comorbidities and substance use), neutropenia, functional factors (e.g., problems with occupation), and clinical monitoring. RESULTS: Clozapine was prescribed to 77% of the women and 85% of the men with TRS. Women had reduced odds of being prescribed clozapine as compared to men after adjusting for all factors included in the present study (adjusted OR: 0.66; 95% CI 0.44-0.97; p = 0.037). CONCLUSION: Women with TRS are less likely to be prescribed clozapine than men with TRS, even when considering the effects of multiple clinical and functional factors. This finding suggests there could be gender bias in clozapine prescription, which carries ramifications for the relatively poorer care of women with TRS regarding many outcomes such as increased hospitalisation, mortality, and poorer quality of life.

Comparing psychotropic medication prescribing in personality disorder between general mental health and psychological services: retrospective cohort study.

BACKGROUND: Although no drugs are licensed for the treatment of personality disorder, pharmacological treatment in clinical practice remains common. AIMS: This study aimed to estimate the prevalence of psychotropic drug use and associations with psychological service use among people with personality disorder. METHOD: Using data from a large, anonymised mental healthcare database, we identified all adult patients with a diagnosis of personality disorder and ascertained psychotropic medication use between 1 August 2015 and 1 February 2016. Multivariable logistic regression models were constructed, adjusting for sociodemographic, clinical and service use factors, to examine the association between psychological services use and psychotropic medication prescribing. RESULTS: Of 3366 identified patients, 2029 (60.3%) were prescribed some form of psychotropic medication. Patients using psychological services were significantly less likely to be prescribed psychotropic medication (adjusted odds ratio 0.48, 95% CI 0.39-0.59, P<0.001) such as antipsychotics, benzodiazepines and antidepressants. This effect was maintained following several sensitivity analyses. We found no difference in the risk for mood stabiliser (adjusted odds ratio 0.79, 95% CI 0.57-1.10, P = 0.169) and multi-class psychotropic use (adjusted odds ratio 0.80, 95% CI 0.60-1.07, P = 0.133) between patients who did and did not use psychological services. CONCLUSIONS: Psychotropic medication prescribing is common in patients with personality disorder, but significantly less likely in those who have used psychological services. This does not appear to be explained by differences in demographic, clinical and service use characteristics. There is a need to develop clear prescribing guidelines and conduct research in clinical settings to examine medication effectiveness for this population.