Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital

A clinical risk prediction tool for identifying the risk of violent offending in severe mental illness: A retrospective case-control study

BACKGROUND

Individuals with severe mental illness are at a higher risk of violence than the general population. However, there is a lack of available and simple tools to screen for the risk of violent offending in clinical settings. We aimed to develop an easy-to-use predictive tool to assist clinicians' decision-making to identify risk of violent offences in China.

METHODS

We identified 1157 patients with severe mental illness who committed violent offending and 1304 patients who were not suspected of violent offending in the matched living areas. We used stepwise regression and Lasso's method to screen for predictors, built a multivariate logistic regression model, and performed internal validation with the 10- fold cross-validation to develop the final prediction model.

RESULTS

The risk prediction model for violence in severe mental illness included age (beta coefficient (b) = 0.05), male sex (b = 2.03), education (b = 1.14), living in rural areas (b = 1.21), history of homeless (b = 0.62), history of previous aggression (b = 1.56), parental history of mental illness (b = 0.69), diagnosis of schizophrenia (b = 1.36), episodes (b = -2.23), duration of illness (b = 0.01). The area under curve for the predictive model for the risk of violence in severe mental illness was 0.93 (95% CI: 0.92-0.94).

CONCLUSIONS

In this study, we developed a predictive tool for violent offending in severe mental illness, containing 10 items that can be easily used by healthcare practitioners. The model was internally validated and has the potential for assessing the risk of violence in patients with severe mental illness in community routine care, although external validation is necessary.

The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies

Although risk assessment tools have been widely used to inform sentencing decisions, there is uncertainty about the extent and quality of evidence of their predictive performance when validated in new samples. Following PRISMA guidelines, we conducted a systematic review of validation studies of 11 commonly used risk assessment tools for sentencing. We identified 36 studies with 597,665 participants, among which were 27 independent validation studies with 177,711 individuals. Overall, the predictive performance of the included risk assessment tools was mixed, and ranged from poor to moderate. Tool performance was typically overestimated in studies with smaller sample sizes or studies in which tool developers were co-authors. Most studies only reported area under the curve (AUC), which ranged from 0.57 to 0.75 in independent studies with more than 500 participants. The majority did not report key performance measures, such as calibration and rates of false positives and negatives. In addition, most validation studies had a high risk of bias, partly due to inappropriate analytical approach used. We conclude that the research priority is for future investigations to address the key methodological shortcomings identified in this review, and policy makers should enable this research. More sufficiently powered independent validation studies are necessary.

Validation and recalibration of OxMIV in predicting violent behaviour in patients with schizophrenia spectrum disorders

Oxford Mental Illness and Violence (OxMIV) addresses the need in mental health services for a scalable, transparent and valid tool to predict violent behaviour in patients with severe mental illness. However, external validations are lacking. Therefore, we have used a Dutch sample of general psychiatric patients with schizophrenia spectrum disorders (N = 637) to evaluate the performance of OxMIV in predicting interpersonal violence over 3 years. The predictors and outcome were measured with standardized instruments and multiple sources of information. Patients were mostly male (n = 493, 77%) and, on average, 27 (SD = 7) years old. The outcome rate was 9% (n = 59). Discrimination, as measured by the area under the curve, was moderate at 0.67 (95% confidence interval 0.61–0.73). Calibration-in-the-large was adequate, with a ratio between predicted and observed events of 1.2 and a Brier score of 0.09. At the individual level, risks were systematically underestimated in the original model, which was remedied by recalibrating the intercept and slope of the model. Probability scores generated by the recalibrated model can be used as an adjunct to clinical decision-making in Dutch mental health services.

Evaluating the Risk of Suicide and Violence in Severe Mental Illness: A Feasibility Study of Two Risk Assessment Tools (OxMIS and OxMIV) in General Psychiatric Settings

BACKGROUND

Two 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.

METHOD

We 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).

RESULTS

Median 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).

CONCLUSION

Both 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.

Validation of the youth mood recurrences risk calculator in an adult sample with bipolar disorder

BACKGROUND

The ability to predict an individual's risk of mood episode recurrence can facilitate personalized medicine in bipolar disorder (BD). We sought to externally validate, in an adult sample, a risk calculator of mood episode recurrence developed in youth/young adults with BD from the Course and Outcome of Bipolar Youth (COBY) study.

METHODS

Adult participants from the National Institute of Mental Health Collaborative Depression Study (CDS; N=258; mean(SD) age=35.5(12.0) years; mean follow-up=24.9 years) were utilized as a sample to validate the youth COBY risk calculator for onset of depressive, manic, or any mood episodes.

RESULTS

In this older validation sample, the risk calculator predicted recurrence of any episode over 1, 2, 3, or 5-year follow-up intervals, with Area Under the Curves (AUCs) approximating 0.77. The AUC for prediction of depressive episodes was about 0.81 for each of the time windows, which was higher than for manic or hypomanic episodes (AUC=0.72). While the risk calculator was well-calibrated across the range of risk scores, it systematically underestimated risk in the CDS sample by about 20%. The length of current remission was a highly significant predictor of recurrence risk in the CDS sample.

LIMITATIONS

Predominantly self-reported White samples may limit generalizability; the risk calculator does not assess more proximal risk (e.g., 1 month).

CONCLUSIONS

Risk of mood episode recurrence can be predicted with good accuracy in youth and adults with BD in remission. The risk calculators may help identify higher risk BD subgroups for treatment and research.

Candidate symptomatic markers for predicting violence in schizophrenia: A cross-sectional study of 7711 patients in a Chinese population

OBJECTIVE

Violent behaviour is an alarming problem among schizophrenia patients. The effects of historical, clinical, and pathological risk factors for violence have been investigated by multiple studies, but consensus has not been achieved. As psychotic symptoms are more direct and intuitive indicators for violence, identifying robustly associated symptoms is a crucial part of the future prediction and precise management of violent patients in clinics. This study aims to identify the psychotic symptoms correlated with violence among schizophrenia patients in a Chinese population.

METHODS

In this cross-sectional study, the medical records of 7711 schizophrenia patients (4711 in the discovery set and 3000 in the validation set) were collected from 1998 to 2010. Their psychotic symptoms were extracted, and the patients were divided into violent and non-violent groups. Multivariate logistic analysis was applied to identify symptoms associated with violence in the discovery set.

RESULTS

Eight psychotic symptoms were found to be significantly correlated with violence in schizophrenia. "Destruction of property", "verbal aggression" and "insomnia" increased the risk of violence, while "flat affect", "delusion of persecution", "auditory hallucination", "vagueness of thought" and "poverty of thought" decreased the risk of violence. The regression model was evaluated by receiver operating characteristic (ROC) analysis for its discriminatory performance, achieving area under curve (AUC) values of 0.887 in the discovery sample set and 0.824 in the validation sample set.

CONCLUSIONS

The correlated symptoms identified by this study can serve as future candidate predictors for violence in schizophrenia, paving the way for precise management of schizophrenia patients in clinics.

Applicability of two violence risk assessment tools in a psychiatric prison hospital population

The risk of violent behavior is known to be higher for patients who suffer from a severe mental disorder. However, specific prediction tools for clinical work in prison psychiatry are lacking. In this single-center study, two violence risk assessment tools (Forensic Psychiatry and Violence Tool, "FoVOx," and Mental Illness and Violence Tool, "OxMIV") were applied to a prison hospital population with a primary psychotic or bipolar disorder and subsequently compared. The required information on all items of both tools was obtained retrospectively for a total of 339 patients by evaluation of available patient files. We obtained the median and inter-quartile range for both FoVOx and OxMIV, and their rank correlation coefficient along with 95% confidence intervals (CIs)-for the full cohort, as well as for cohort subgroups. The two risk assessment tools were strongly positively correlated (Spearman correlation = 0.83; 95% CI = 0.80-0.86). Such a high correlation was independent of nationality, country of origin, type of detention, schizophrenia-spectrum disorder, previous violent crime and alcohol use disorder, where correlations were above 0.8. A lower correlation was seen with patients who were 30 years old or more, married, with affective disorder and with self-harm behavior, and also in patients without aggressive behavior and without drug use disorder. Both risk assessment tools are applicable as an adjunct to clinical decision making in prison psychiatry.