Dataset Information

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).

ABSTRACT: Background:Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records. Methods:In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure). Results:In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61). Conclusions:It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

SUBMITTER: Senior M

PROVIDER: S-EPMC7175991 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).

Senior Morwenna M Burghart Matthias M Yu Rongqin R Kormilitzin Andrey A Liu Qiang Q Vaci Nemanja N Nevado-Holgado Alejo A Pandit Smita S Zlodre Jakov J Fazel Seena S

Frontiers in psychiatry 20200415

<h4>Background</h4>Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, the ...[more]

PMID: 32351413

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Project description:BackgroundTwo OxRisk risk assessment tools, the Oxford Mental Illness and Suicide (OxMIS) and the Oxford Mental Illness and Violence (OxMIV), were developed and validated using national linked registries in Sweden, to assess suicide and violence risk in individuals with severe mental illness (schizophrenia-spectrum disorders and bipolar disorders). In this study, we aim to examine the feasibility and acceptability of the tools in three different clinical services.MethodWe employed a two-step mixed-methods approach, by combining quantitative analyses of risk scores of 147 individual patients, and thematic analyses of qualitative data. First, 38 clinicians were asked to use OxMIS and OxMIV when conducting their routine risk assessments in patients with severe mental illness. The risk scores for each patient (which provide a probability of the outcome over 12 months) were then compared to the unstructured clinical risk assessment made by the treating clinician. Second, we carried out semi-structured interviews with the clinicians on the acceptability and utility of the tools. Thematic analysis was conducted on the qualitative data to identify common themes, in terms of the utility, accuracy, and acceptability of the tools. The investigations were undertaken in three general adult psychiatric clinics located in the cities of Barcelona and Sevilla (Spain), and Changsha (China).ResultsMedian risk probabilities over 12 months for OxMIS were 1.0% in the Spanish patient sample and 1.9% in the Chinese sample. For OxMIV, they were 0.7% (Spanish) and 0.8% (Chinese). In the thematic analysis, clinicians described the tools as easy to use, and thought that the risk score improved risk management. Potential additions to predictors were suggested, including family history and the patient's support network. Concordance rates of risk estimates between the tools and clinicians was high for violence (94.4%; 68/72) and moderate for suicide (50.0%; 36/72).ConclusionBoth OxMIS and OxMIV are feasible and practical in different general adult psychiatric settings. Clinicians interviewed found that both tools provide a useful structured approach to estimate the risk of suicide and violence. Risk scores from OxMIS and OxMIV can also be used to assist clinical decision-making for future management.

Project description:BackgroundCurrent approaches to stratify patients with psychiatric disorders into groups on the basis of violence risk are limited by inconsistency, variable accuracy, and unscalability. To address the need for a scalable and valid tool to assess violence risk in patients with schizophrenia spectrum or bipolar disorder, we describe the derivation of a score based on routinely collected factors and present findings from external validation.MethodsOn the basis of a national cohort of 75 158 Swedish individuals aged 15-65 years with a diagnosis of severe mental illness (schizophrenia spectrum or bipolar disorder) with 574 018 patient episodes between Jan 1, 2001, and Dec 31, 2008, we developed predictive models for violent offending (primary outcome) within 1 year of hospital discharge for inpatients or clinical contact with psychiatric services for outpatients (patient episode) through linkage of population-based registers. We developed a derivation model to determine the relative influence of prespecified criminal history and sociodemographic and clinical risk factors, which are mostly routinely collected, and then tested it in an external validation. We measured discrimination and calibration for prediction of violent offending at 1 year using specified risk cutoffs.FindingsOf the cohort of 75 158 patients with schizophrenia spectrum or bipolar disorder, we assigned 58 771 (78%) to the derivation sample and 16 387 (22%) to the validation sample. In the derivation sample, 830 (1%) individuals committed a violent offence within 12 months of their patient episode. We developed a 16-item model. The strongest predictors of violent offending within 12 months were conviction for previous violent crime (adjusted odds ratio 5·03 [95% CI 4·23-5·98]; p<0·0001), male sex (2·32 [1·91-2·81]; p<0·0001), and age (0·63 per 10 years of age [0·58-0·67]; p<0·0001). In external validation, the model showed good measures of discrimination (c-index 0·89 [0·85-0·93]) and calibration. For risk of violent offending at 1 year, with a 5% cutoff, sensitivity was 62% (95% CI 55-68) and specificity was 94% (93-94). The positive predictive value was 11% and the negative predictive value was more than 99%. We used the model to generate a simple web-based risk calculator (Oxford Mental Illness and Violence tool [OxMIV]).InterpretationWe have developed a prediction score in a national cohort of patients with schizophrenia spectrum or bipolar disorder, which can be used as an adjunct to decision making in clinical practice by identifying those who are at low risk of violent offending. The low positive predictive value suggests that further clinical assessment in individuals at high risk of violent offending is required to establish who might benefit from additional risk management. Further validation in other countries is needed.FundingWellcome Trust and Swedish Research Council.

Project description:Background: Risk assessment informs decisions around admission to and discharge from secure psychiatric hospital and contributes to treatment and supervision. There are advantages to using brief, scalable, free online tools with similar accuracy to instruments currently used. We undertook a study of one such risk assessment, the Forensic Psychiatry and Violence Oxford (FoVOx) tool, examining its acceptability, feasibility, and practicality. Methods: We completed the FoVOx tool on all discharges from six secure psychiatric hospitals in one region in England over two years. We interviewed 11 senior forensic psychiatrists regarding each discharge using a standardized questionnaire. Their patient's FoVOx score was compared to clinical risk assessment, and the senior clinicians were asked if they considered FoVOx scores accurate and useful. A modified thematic analysis was conducted, and clinicians were surveyed about current risk assessment practice on discharge. Results: Of 90 consecutive discharges, 84 were included in the final analysis. The median FoVOx probability score was 11% risk of violent recidivism in two years after discharge. We estimated that 12 (14%) individuals reoffended since discharge; all were in the medium or high risk FoVOx categories. Clinical assessment of risk agreed with the FoVOx categories in around half the cases. Clinicians were more likely to provide lower risk categories compared with FoVOx ones. FoVOx was considered to be an accurate representation of risk in 67% of cases; clinicians revised their view on some patient's risk assessment after being informed of their FoVOx scores. Completing FoVOx was reported to be helpful in the majority of cases. Reasons included improved communication with other agencies, reassurance to clinical teams, and identifying additional factors not fully considered. 10 of the 11 respondents reported that FoVOx was practical, and seven of 11 reported that they would use it in the future, highlighting its brevity and speed of use compared to existing risk assessment tools. Conclusions: Senior clinicians in this regional forensic psychiatric service found the FoVOx risk assessment tool feasible, practical, and easy to use. Its use addressed a lack of consistency around risk assessment at the point of discharge and, if used routinely, could assist in clinical decision-making.

Project description:Stigma and discrimination toward individuals experiencing homelessness and mental disorders remain pervasive across societies. However, there are few longitudinal studies of stigma and discrimination among homeless adults with mental illness. This study aimed to identify the two-year group trajectories of stigma and discrimination and examine the predictive role of mental health characteristics among 414 homeless adults with mental illness participating in the extended follow-up phase of the Toronto At Home/Chez Soi (AH/CS) randomized trial site. Mental health-related perceived stigma and discrimination were measured at baseline, one, and two years using validated scales. Group-based-trajectory modelling was used to identify stigma and discrimination group trajectory memberships and the effect of the Housing First treatment (rent supplements and mental health support services) vs treatment as usual on these trajectories. The associations between mental health-related characteristics and trajectory group memberships were also assessed using multinomial logistic regression. Over two-years, three group trajectories of stigma and discrimination were identified. For discrimination, participants followed a low, moderate, or increasingly high discrimination group trajectory, while for stigma, participants followed a low, moderate or high stigma group trajectory. The Housing First treatment had no significant effect on discrimination or stigma trajectories groups. For the discrimination trajectories, major depressive episode, mood disorder with psychotic features, alcohol abuse, suicidality, severity of mental health symptoms, and substance use severity in the previous year were predictors of moderate and increasingly high discrimination trajectories. History of discrimination within healthcare setting was also positively associated with following a moderate or high discrimination trajectory. For the stigma trajectories, substance dependence, high mental health symptoms severity, substance use severity, and discrimination experiences within healthcare settings were the main predictors for the moderate trajectory group; while substance dependence, suicidality, mental health symptom severity, substance use severity and discrimination experiences within health care setting were also positive predictors for the high stigma trajectory group. Ethno-racial status modified the association between having a major depression episode, alcohol dependence, and the likelihood of being a member of the high stigma trajectory group. This study showed that adults experiencing mental illness and homelessness followed distinct stigma and discrimination group trajectories based on their mental health-problems. There is an urgent need to increase focus on strategies and policies to reduce stigma and discrimination in this population.

Project description:INTRODUCTION:Successful competitive employment has been found to be related to enhanced self-esteem, higher quality of life and reduced mental health service use for individuals living with serious mental illnesses (SMIs) including schizophrenia, bipolar disorder and major depression. The effectiveness of the individual placement and support model has been demonstrated in multiple randomised controlled trials in many countries. The management of stress, depression and anxiety in the workplace may be effectively enhanced through digital mental health interventions. The WorkingWell mobile support tool ('app') is specifically designed to meet the need for illness management support for individuals with SMI in the workplace, as an adjunct to professional treatment. METHODS AND ANALYSIS:The WorkingWell app, grounded in evidence-based supported employment, is informed by user experience design. It will be tested in a pre-post design, mixed-methods pilot study to explore issues of feasibility, acceptability and usefulness, and to provide preliminary data on the impact of use. Putative mediators of improved job tenure and psychological well-being, including postintervention changes in social support, self-efficacy and work-related motivation, will be investigated. Forty individuals at least 18 years of age, meeting the eligibility requirements for supported employment services (ie, diagnosed with a mental illness meeting the criteria for severity, duration and treatment), working a minimum of 10 hours per week at study enrolment, and speaking, reading and writing in English will be recruited for the pilot study. Research staff will recruit individuals at community-based mental health agencies; provide orientation to the study, the study smartphones and the WorkingWell app; conduct research interviews including standardised measures as well as semistructured items; and provide technical assistance in telephone calls and inperson meetings. A sample of 10 agency staff will be recruited to obtain further information on the feasibility, acceptability and usefulness of WorkingWell. ETHICS AND DISSEMINATION:The study design and procedures are approved by the Dartmouth-Hitchcock Medical Center Committee for the Protection of Human Subjects, the Massachusetts Department of Mental Health Central Office Research Review Committee and the Vermont Agency of Human Services Institutional Review Board. Study findings will be disseminated to agency partners, state agencies and funders, and to the research and technology development communities. Findings from the study will inform the design, data collection procedures and protocol for future full-scale randomised controlled trial testing of the effectiveness of the WorkingWell app, as well as investigations of work-related variables as mediators of psychological well-being and quality of life for individuals with SMI.

Dataset Information

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).

Publications

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).

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