Identification of low risk of violent crime in severe mental illness with a clinical prediction tool (Oxford Mental Illness and Violence tool [OxMIV]): a derivation and validation study

Predictors of psychosocial functioning in people diagnosed with schizophrenia spectrum disorders that committed violent offences and in those that did not: Results of the Recoviwel study

Psychosocial functioning represents a core treatment target of Schizophrenia Spectrum Disorders (SSD), and several clinical and cognitive factors contribute to its impairment. However, determinants of psychosocial functioning in people living with SSD that committed violent offences remain to be more thoroughly explored. This study aims to separately assess and compare predictors of psychosocial functioning in people with SSD that did and that did not commit violent offences considering several clinical, cognitive and violence-related parameters. Fifty inmates convicted for violent crimes in a forensic psychiatry setting diagnosed with SSD (OP group) and fifty participants matched for age, gender, education, and diagnosis (Non-OP group) were included in the study. A higher risk of violent relapse as measured by HCR-20 clinical subscale scores (p < 0.002) and greater global clinical severity as measured by CGI-S scores (p = 0.023) emerged as individual predictors of worse psychosocial functioning, as measured by PSP scores, in the OP group. Greater global clinical severity (p < 0.001), worse performance in the processing speed domain as measured by the BACS Symbol Coding (p = 0.002) and TMT-A tests (p = 0.016) and higher levels of non-planning impulsivity as measured by BIS-11 scores (p < 0.001) emerged as individual predictors of worse psychosocial functioning in the Non-OP group. These results confirm that clinical severity impacts psychosocial functioning in all individuals diagnosed with SSD and suggest that while cognitive impairment clearly represents a determinant of worse functional outcomes in most patients, the risk of violent relapse is a specific predictor of worse psychosocial functioning in people with SSD that committed criminal offences.

Approaches and challenges to assessing risk of violence in first episode psychosis: A qualitative interview study of clinicians, patients and carers

AIM

Clinical services for early psychosis seek to improve prognosis for a range of adverse outcomes. For some individuals, perpetration of violence is an important potential outcome to reduce. How these clinical services currently assess this risk however is uncertain. This study aimed to address this gap by using qualitative methods to examine in depth current approaches, attitudes and challenges to assessing violence risk in this clinical setting, from the perspectives of multidisciplinary clinicians, patients and carers.

METHODS

Participants were recruited from two UK Early Intervention in Psychosis services. Semi-structured individual interviews were undertaken using a topic guide. In addition, clinical vignettes were presented to clinician participants as a probe to prompt discussion. Data were analysed using thematic analysis, informed by the constant comparative method.

RESULTS

We conducted 30 qualitative interviews, of 18 clinicians and 12 patients and carers. Themes developed from clinician interviews included key difficulties of low confidence, limited training, accessing collateral information and variation in how risk is appraised and communicated. Potential stigma and sensitivity of the topic of violence were perceived as barriers to its discussion. Patient and carer perspectives provided insight into how to address barriers, and highlighted the importance of an open approach, including with families.

CONCLUSIONS

We recommend developing contextually appropriate pathways to collaboratively assess violence risk and identify modifiable needs to reduce this risk, and for practical improvements in training and information-sharing.

Association of a History of Incarceration and Solitary Confinement with Suicide-Related Outcomes in a General Population Sample from Two U.S. Cities

OBJECTIVES

To evaluate whether a history of incarceration was associated with increased odds of suicidal ideation and suicide attempts, and to determine if this association was further strengthened when combined with a history of solitary confinement.

METHODS

We collected cross-sectional data from a general population sample in New York City and Baltimore in March 2017. Participants were categorized based on their history of incarceration and solitary confinement: (1) no incarceration, (2) incarceration-only, and (3) incarceration plus solitary confinement. We compared these three groups, utilizing hierarchical adjustments for sociodemographic factors and adverse childhood experiences. Missing data were accounted for utilizing multiple imputation via chained equation.

RESULTS

A total of 1221 individuals were analyzed. Those who experienced both incarceration and solitary confinement consistently had higher odds of suicidal ideation (OR, 2.80; 95% CI, 1.43 to 5.48) and suicide attempts (OR, 6.98; 95% CI, 2.77 to 17.61) than never incarcerated individuals. Those who experienced incarceration without solitary confinement had higher odds of suicide attempts (OR, 3.77; 95% CI, 1.35 to 10.56) than never incarcerated individuals, whereas this association was not evident for suicidal ideation. Solitary confinement increased the odds of suicidal ideation even compared to incarceration without solitary confinement (OR, 2.71; 95% CI, 1.09 to 6.74).

CONCLUSIONS

Our findings support the need to address the higher likelihood of suicide-related outcomes among those in contact with the criminal justice system, and to consider alternatives to solitary confinement.

Cognitive and clinical characteristics of offenders and non-offenders diagnosed with schizophrenia spectrum disorders: results of the Recoviwel observational study

The association between schizophrenia spectrum disorders (SSD) and violent behavior is complex and requires further research. The cognitive correlates of violent behavior, in particular, remain to be further investigated. Aims of the present study were to comprehensively assess the cognitive and clinical profile of SSD violent offenders and evaluate individual predictors of violent behavior. Fifty inmates convicted for violent crimes in a forensic psychiatry setting and diagnosed with SSD were compared to fifty non-offender patients matched for age, gender, education, and diagnosis. Offender and non-offender participants were compared based on socio-demographic, clinical, and cognitive variables using non-parametric testing to select potential predictors of violent behavior. Multivariate logistic regressions were then performed to identify individual predictors of violent behavior. Offender participants showed more school failures, higher prevalence of substance use, higher Clinical Global Impression Severity Scale (CGI-S) and Positive and Negative Syndrome Scale Excited Component (PANSS-EC) scores, worse working memory and better attention performance, higher Historical Clinical and Risk Management scale 20 (HCR-20) and Hare Psychopathy Checklist (PCL-R) scores in all subdomains and factors. School failures, higher PANSS-EC scores, worse working memory and processing speed, better attention performance, higher scores in HCR-20 Management subscale and the PCL-R "Callous" factor emerged as predictors of violent behavior. Better attentional performance was correlated with higher PCL-R "Callous" factor scores, worse cognitive performance in several domains with higher PCL-R "Unstable" factor scores. In conclusion, the present study highlights the importance of carefully assessing SSD patients with violent behavior in all clinical, cognitive, and behavioral aspects.

Development and validation of a prediction model for rehospitalization among people with schizophrenia discharged from acute inpatient care

Objective

Relapses and rehospitalization prevent the recovery of individuals with schizophrenia or related psychoses. We aimed to build a model to predict the risk of rehospitalization among people with schizophrenia or related psychoses, including those with multiple episodes.

Methods

This retrospective cohort study included individuals aged 18 years or older, with schizophrenia or related psychoses, and discharged between January 2014 and December 2018 from one of three Japanese psychiatric hospital acute inpatient care ward. We collected nine predictors at the time of recruitment, followed up with the participants for 12 months, and observed whether psychotic relapse had occurred. Next, we applied the Cox regression model and used an elastic net to avoid overfitting. Then, we examined discrimination using bootstrapping, Steyerberg's method, and "leave-one-hospital-out" cross-validation. We also constructed a bias-corrected calibration plot.

Results

Data from a total of 805 individuals were analyzed. The significant predictors were the number of previous hospitalizations (HR 1.42, 95% CI 1.22-1.64) and the current length of stay in days (HR 1.31, 95% CI 1.04-1.64). In model development for relapse, Harrell's c-index was 0.59 (95% CI 0.55-0.63). The internal and internal-external validation for rehospitalization showed Harrell's c-index to be 0.64 (95% CI 0.59-0.69) and 0.66 (95% CI 0.57-0.74), respectively. The calibration plot was found to be adequate.

Conclusion

The model showed moderate discrimination of readmission after discharge. Carefully defining a research question by seeking needs among the population with chronic schizophrenia with multiple episodes may be key to building a useful model.

Autism Spectrum Disorders in forensic psychiatric investigations-patterns of comorbidity and criminality

Background

There are contradictory research findings regarding whether individuals with Autism Spectrum Disorders (ASDs) are more or less likely to commit crimes. The aims of the current study were to: (1) Describe psychiatric and crime-related characteristics of a large group of offenders with ASD who had undergone a Forensic Psychiatric Investigation (FPI). (2) Identify clinical subgroups among this group of offenders. (3) Investigate associations between the identified clinical subgroups and (a) psychiatric comorbidity (b) types of crimes and (c) criminal responsibility.

Methods

The study cohort consists of all subjects (n = 831) who received an ASD-diagnosis at an FPI between 2002 and 2018 in Sweden. Descriptive and clinical, as well as crime related variables were obtained from the FPIs. Non-parametric (Pearson χ2, Fisher's exact and Mann-Whitney U-test) inferential statistics were used for analyses of between-group differences and effect sizes were reported. A Latent Class Analysis was used to identify homogeneous subgroups (or classes) from categorical characteristics.

Results

The cohort consisted of 708 men and 123 women, aged 18 to 74 yrs. Two-thirds (66.7%) of the cohort had at least one other psychiatric diagnosis, the most prevalent was substance use disorder (SUD). A severe mental disorder, equivalent to lack of criminal responsibility, was most often reported among offenders with a comorbid diagnosis of schizophrenia spectrum disorder. The most common type of crime was violent crime. Three person-oriented clinical subgroups were identified; (1) ASD with few other diagnoses; (2) ASD and very high levels of SUDs, plus moderate levels of other externalizing disorders and psychotic psychopathology and (3) ASD and moderate to high levels of personality disorders (other than ASPD) and SUDs.

Conclusion

Our results highlight the importance of all parts of the CJS to be prepared to handle offenders with ASD, often with high levels of additional psychiatric problems. Traditional approaches in treatment or other psychosocial interventions for ASD may need to be adapted to at least three general clinical profiles- one with mainly neurodevelopmental problems, one with a spectrum of externalizing problems and one with complex personality related difficulties.

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.

Assessing violence risk in first-episode psychosis: external validation, updating and net benefit of a prediction tool (OxMIV)

BACKGROUND

Violence perpetration is a key outcome to prevent for an important subgroup of individuals presenting to mental health services, including early intervention in psychosis (EIP) services. Needs and risks are typically assessed without structured methods, which could facilitate consistency and accuracy. Prediction tools, such as OxMIV (Oxford Mental Illness and Violence tool), could provide a structured risk stratification approach, but require external validation in clinical settings.

OBJECTIVES

We aimed to validate and update OxMIV in first-episode psychosis and consider its benefit as a complement to clinical assessment.

METHODS

A retrospective cohort of individuals assessed in two UK EIP services was included. Electronic health records were used to extract predictors and risk judgements made by assessing clinicians. Outcome data involved police and healthcare records for violence perpetration in the 12 months post-assessment.

FINDINGS

Of 1145 individuals presenting to EIP services, 131 (11%) perpetrated violence during the 12 month follow-up. OxMIV showed good discrimination (area under the curve 0.75, 95% CI 0.71 to 0.80). Calibration-in-the-large was also good after updating the model constant. Using a 10% cut-off, sensitivity was 71% (95% CI 63% to 80%), specificity 66% (63% to 69%), positive predictive value 22% (19% to 24%) and negative predictive value 95% (93% to 96%). In contrast, clinical judgement sensitivity was 40% and specificity 89%. Decision curve analysis showed net benefit of OxMIV over comparison approaches.

CONCLUSIONS

OxMIV performed well in this real-world validation, with improved sensitivity compared with unstructured assessments.

CLINICAL IMPLICATIONS

Structured tools to assess violence risk, such as OxMIV, have potential in first-episode psychosis to support a stratified approach to allocating non-harmful interventions to individuals who may benefit from the largest absolute risk reduction.

Characteristics and associated factors of violence in male patients with schizophrenia in China

Objective

To investigate the characteristics and associated factors of violence in male patients with schizophrenia in China.

Methods

A total of 507 male patients with schizophrenia were recruited, including 386 non-violent and 121 violent patients. The socio-demographic information and medical history of the patients were collected. Psychopathological characteristics, personality traits psychopathology, and factors related to risk management were assessed using the Brief Psychiatric Rating Scale (BPRS), the History of Violence, Clinical, Risk Assessment Scale (HCR-20), and the Psychopathy Checklist-Revised (PCL-R), as appropriate. Differences in these factors were compared between the violent and non-violent patients, and logistic regression analysis was performed to explore the risk factors for violence in male patients with schizophrenia.

Results

The results showed that the violent group had a lower level of education, longer duration of illness, as well as a higher rate of hospitalization, history of suicidal attempts, and history of alcohol compared with the non-violent group. The violent group scored higher in items of symptoms in BPRS, personality traits and psychopathy in PCL-R, and risk management in HCR-20. The regression analysis showed that previous suicidal behavior (OR = 2.07,95% CI [1.06-4.05], P = 0.033), antisocial tendency in PCL-R (OR = 1.21, 95% CI [1.01-1.45], P = 0.038), H2: young age at violent incident (OR = 6.39, 95% CI [4.16-9.84], P &lt; 0.001), C4: impulsivity (OR = 1.76, 95% CI [1.20-2.59], P = 0.004), and H3: relationship instability (OR = 1.60, 95% CI [1.08-2.37], P = 0.019) in HCR-20 were risk factors of violence among male patients with schizophrenia.

Conclusion

The present study found significant differences in socio-demographic information, history of treatment, and psychopathy characteristics between male patients with schizophrenia who had engaged in violent behaviors and their non-violent counterparts in China. Our findings suggested the necessity of individualized treatment for male patients with schizophrenia who had engaged in violent behaviors as well as the use of both HCR-20 and PCL-R for their assessment.

Modifiable risk factors for inpatient violence in psychiatric hospital: prospective study and prediction model

BACKGROUND

Violence perpetrated by psychiatric inpatients is associated with modifiable factors. Current structured approaches to assess inpatient violence risk lack predictive validity and linkage to interventions.

METHODS

Adult psychiatric inpatients on forensic and general wards in three psychiatric hospitals were recruited and followed up prospectively for 6 months. Information on modifiable (dynamic) risk factors were collected every 1-4 weeks, and baseline background factors. Data were transferred to a web-based monitoring system (FOxWeb) to calculate a total dynamic risk score. Outcomes were extracted from an incident-reporting system recording aggression and interpersonal violence. The association between total dynamic score and violent incidents was assessed by multilevel logistic regression and compared with dynamic score excluded.

RESULTS

We recruited 89 patients and conducted 624 separate assessments (median 5/patient). Mean age was 39 (s.d. 12.5) years with 20% (n = 18) female. Common diagnoses were schizophrenia-spectrum disorders (70%, n = 62) and personality disorders (20%, n = 18). There were 93 violent incidents. Factors contributing to violence risk were a total dynamic score of ⩾1 (OR 3.39, 95% CI 1.25-9.20), 10-year increase in age (OR 0.67, 0.47-0.96), and female sex (OR 2.78, 1.04-7.40). Non-significant associations with schizophrenia-spectrum disorder were found (OR 0.50, 0.20-1.21). In a fixed-effect model using all covariates, AUC was 0.77 (0.72-0.82) and 0.75 (0.70-0.80) when the dynamic score was excluded.

CONCLUSIONS

In predicting violence risk in individuals with psychiatric disorders, modifiable factors added little incremental value beyond static ones in a psychiatric inpatient setting. Future work should make a clear distinction between risk factors that assist in prediction and those linked to needs.

The age of violence: Mapping brain age in psychosis and psychopathy

Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.

Influencing factors of multiple adverse outcomes among schizophrenia patients using count regression models: a cross-sectional study

BACKGROUND

Schizophrenia patients have increased risks of adverse outcomes, including violent crime, aggressiveness, and suicide. However, studies of different adverse outcomes in schizophrenia patients are limited and the influencing factors for these outcomes need clarification by appropriate models. This study aimed to identify influencing factors of these adverse outcomes by examining and comparing different count regression models.

METHODS

This study included schizophrenia patients who had at least one follow-up record in the Guangdong Mental Health Center Network Medical System during 2020. Three types of adverse outcomes were included: a) aggressiveness with police dispatch or violent crime, b) aggressiveness without police dispatch, and c) self-harm or suicide attempts. The incidence density of these adverse outcomes was investigated using the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models, accordingly. The best model was chosen based on goodness-of-fit tests. We further analyzed associations between the number of occurrences of adverse outcomes and sociodemographic, clinical factors with the best model.

RESULTS

A total of 130,474 schizophrenia patients were enrolled. Adverse outcomes rates were reported to be less than 1% for schizophrenia patients in 2020, in Guangdong. The NB model performed the best in terms of goodness-of-fit and interpretation when fitting for the number of occurrences of aggressiveness without police dispatch, whereas the ZINB models performed better for the other two outcomes. Age, sex, and history of adverse outcomes were influencing factors shared across these adverse outcomes. Higher education and employment were protective factors for aggressive and violent behaviors. Disease onset aged ≥ 18 years served as a significant risk factor for aggressiveness without police dispatch, and self-harm or suicide attempts. Family history of mental diseases was a risk factor for self-harm or suicide attempts individually.

CONCLUSIONS

NB and ZINB models were selected for fitting the number of occurrences of adverse outcomes among schizophrenia patients in our studies. Influencing factors for the incidence density of adverse outcomes included both those shared across different types and those individual to specific types. Therefore, comprehensive and customized tools in risk assessment and intervention might be necessary.

Association of Schizophrenia Spectrum Disorders and Violence Perpetration in Adults and Adolescents From 15 Countries: A Systematic Review and Meta-analysis

IMPORTANCE

Violence perpetration outcomes in individuals with schizophrenia spectrum disorders contribute to morbidity and mortality at a population level, disrupt care, and lead to stigma.

OBJECTIVE

To conduct a systematic review and meta-analysis of the risk of perpetrating interpersonal violence in individuals with schizophrenia spectrum disorders compared with general population control individuals.

DATA SOURCES

Multiple databases were searched for studies in any language from January 1970 to March 2021 using the terms violen* or homicid* and psychosis or psychoses or psychotic or schizophren* or schizoaffective or delusional and terms for mental disorders. Bibliographies of included articles were hand searched.

STUDY SELECTION

The study included case-control and cohort studies that allowed risks of interpersonal violence perpetration and/or violent criminality in individuals with schizophrenia spectrum disorders to be compared with a general population group without these disorders.

DATA EXTRACTION AND SYNTHESIS

The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Meta-analyses of Observational Studies in Epidemiology (MOOSE) proposal. Two reviewers extracted data. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Data were pooled using a random-effects model.

MAIN OUTCOMES AND MEASURES

The main outcome was violence to others obtained either through official records, self-report and/or collateral-report, or medical file review and included any physical assault, robbery, sexual offenses, illegal threats or intimidation, and arson.

RESULTS

The meta-analysis included 24 studies of violence perpetration outcomes in 15 countries over 4 decades (N = 51 309 individuals with schizophrenia spectrum disorders; reported mean age of 21 to 54 years at follow-up; of those studies that reported outcomes separately by sex, there were 19 976 male individuals and 14 275 female individuals). There was an increase in risk of violence perpetration in men with schizophrenia and other psychoses (pooled odds ratio [OR], 4.5; 95% CI, 3.6-5.6) with substantial heterogeneity (I2 = 85%; 95% CI, 77-91). The risk was also elevated in women (pooled OR, 10.2; 95% CI, 7.1-14.6), with substantial heterogeneity (I2 = 66%; 95% CI, 31-83). Odds of perpetrating sexual offenses (OR, 5.1; 95% CI, 3.8-6.8) and homicide (OR, 17.7; 95% CI, 13.9-22.6) were also investigated. Three studies found increased relative risks of arson but data were not pooled for this analysis owing to heterogeneity of outcomes. Absolute risks of violence perpetration in register-based studies were less than 1 in 20 in women with schizophrenia spectrum disorders and less than 1 in 4 in men over a 35-year period.

CONCLUSIONS AND RELEVANCE

This systematic review and meta-analysis found that the risk of perpetrating violent outcomes was increased in individuals with schizophrenia spectrum disorders compared with community control individuals, which has been confirmed in new population-based longitudinal studies and sibling comparison designs.

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.

Risk factors for violent behaviors in patients with schizophrenia: 2-year follow-up study in primary mental health care in China

OBJECTIVE

The consequences and impact of violent behavior in schizophrenia are often serious, and identification of risk factors is of great importance to achieve early identification and effective management.

METHODS

This follow-up study sampled adult patients with schizophrenia in primary mental health care in a rural area of southern China, in which 491 participants completed a comprehensive questionnaire at baseline and the 2-year follow-up. Sociodemographic, clinical and psychological assessment data were collected from all participants. Paired sample T-Tests and the McNemar Test were performed to examine changes over the follow-up period. Generalized Estimating Equations (GEE) were used to analyze the risk factors for violent behavior.

RESULTS

The results showed that about two in five community-dwelling patients with schizophrenia reported violent behavior in the past year. At follow-up, participants were significantly less employed, had more times of hospitalization, more psychotropic medication, and severer depressive symptoms, but had better health-related quality of life than at baseline. Use of clozapine and better insight into medication decreased the possibility of violent behavior, while more severe positive symptoms, insomnia, as well as use of second-generation antipsychotics other than clozapine, antidepressants and mood stabilizers increased the possibility of violent behavior.

CONCLUSIONS

Risk evaluation, prevention and management of violence in patients with schizophrenia are demanded in primary mental health care.

Identification of violent patients with schizophrenia using a hybrid machine learning approach at the individual level

Despite numerous risk factors associated with violence in patients with schizophrenia, predicting and preventing violent behavior is still a challenge. At present, machine learning (ML) has become a promising strategy for guiding individualized assessment. To build an effective model to predict the risk of violence in patients with schizophrenia, we proposed a hybrid ML method to improve the prediction capability in 42 violent offenders with schizophrenia and 33 non-violent patients with schizophrenia. The results revealed that the final model, which combined multimodal data, achieved the highest prediction performance with an accuracy of 90.67%. Specifically, the model, which fused three modalities of neuroimaging data, achieved a better accuracy than other fused models. In addition, the msot discriminative neuroimaging features involved in the prefrontal-temporal cognitive circuit and striatum reward system, indicating that dysfunction in cortical-subcortical circuits might be associated with high risk of violence in patients with schizophrenia. This study provides the first evidence supporting that the combination of specific multimodal neuroimaging and clinical data in ML analysis can effectively identify violent patients with schizophrenia. Furthermore, this work is crucial for the development of neuro-prediction models that could facilitate individualized treatment and interventions for violent behaviors in patients with schizophrenia.

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.

A guide to outcome measurement in psychiatry

Measuring outcomes is becoming an increasingly standard (and highly complex) part of what mental health services are expected to do. Practising psychiatrists will need to have a good understanding of approaches to outcome measurement: used well, they have the potential to amplify the patient voice, promote good-quality services and facilitate research. We discuss what constitutes an outcome measure, the different ways that such measures can be obtained and the mechanisms for assessing the quality and appropriateness of an outcome measure. We outline the rapidly evolving research and policy context regarding outcome measurement, with particular reference to the UK's National Health Service. We also consider the potential pitfalls to outcome measurement, such as added clinical burden, inappropriate incentivisation of behaviour and incorrect interpretation of results. We discuss ways that such difficulties can be avoided or their effects mitigated.

Digital Technologies and Coercion in Psychiatry

Psychiatry has a contentious history of coercion in the care of patients with mental illness, and legal frameworks often govern use of coercive interventions, such as involuntary hospitalization, physical restraints, and medication over objection. Research also suggests that informal coercion, including subtle inducements, leverage, or threats, is prevalent and influential in psychiatric settings. Digital technologies bring promise for expanding access to psychiatric care and improving delivery of these services; however, use and misuse of digital technologies, such as electronic medical record flags, surveillance cameras, videoconferencing, and risk assessment tools, could lead to unexpected coercion of patients with mental illness. Using several composite case examples, the author proposes that the integration of digital technologies into psychiatric care can influence patients' experiences of coercion and provides recommendations for studying and addressing these effects.

Violence and mental disorders: a structured review of associations by individual diagnoses, risk factors, and risk assessment

In this Review, we summarise evidence on the association between different mental disorders and violence, with emphasis on high quality designs and replicated findings. Relative risks are typically increased for all violent outcomes in most diagnosed psychiatric disorders compared with people without psychiatric disorders, with increased odds in the range of 2-4 after adjustment for familial and other sources of confounding. Absolute rates of violent crime over 5-10 years are typically below 5% in people with mental illness (excluding personality disorders, schizophrenia, and substance misuse), which increases to 6-10% in personality disorders and schizophrenia spectrum disorders, and to more than 10% in substance misuse. Past criminality and comorbid substance misuse are strongly predictive of future violence in many individual disorders. We reviewed national clinical practice guidelines, which vary in content and require updating to reflect the present epidemiological evidence. Standardised and clinically feasible approaches to the assessment and management of violence risk in general psychiatric settings need to be developed.

Mental illness and the risk of self- and other-directed aggression: Results from the National Survey on Drug Use and Health

Aggression and mental illness have been classically interlinked, often causing controversy and debate. Previous studies have shown that mental illness can be a risk factor to self- and other-directed aggression. However, these associations have rarely been simultaneously studied within the same population. Therefore, we aimed to study whether psychiatric disorders differentially increase the likelihood of one subtype of aggression over the other. We used the publicly available data of the National Survey on Drug Use and Health (NSDUH) from 2008 through 2014, for a total sample of 270,227 adult respondents. We designed our independent variable according to three categories: no mental illness (NMI), low or moderate (LMMI) and serious (SMI). We constructed regression models to estimate the odds ratios for those having a mental illness committing (a) a subtype of aggression over the past year compared with no aggression and (b) other-directed compared to self-directed aggression. We found that most respondents with mental illness reported no past-year aggression of any type. However, respondents with mental illness had higher odds of perpetrating all subtypes of aggression. Additionally, respondents with LMMI and SMI were respectively 1.7 and 3 times more likely to engage in self- rather than other-directed aggression. Future research should focus on identifying accurate and reliable predictors of self- and other-directed aggression among individuals with mental illness.

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

The real-world impact of psychosis prevention is reliant on effective strategies for identifying individuals at risk. A transdiagnostic, individualized, clinically-based risk calculator to improve this has been developed and externally validated twice in two different UK healthcare trusts with convincing results. The prognostic performance of this risk calculator outside the UK is unknown. All individuals who accessed primary or secondary health care services belonging to the IBM® MarketScan® Commercial Database between January 2015 and December 2017, and received a first ICD-10 index diagnosis of nonorganic/nonpsychotic mental disorder, were included. According to the risk calculator, age, gender, ethnicity, age-by-gender, and ICD-10 cluster diagnosis at index date were used to predict development of any ICD-10 nonorganic psychotic disorder. Because patient-level ethnicity data were not available city-level ethnicity proportions were used as proxy. The study included 2,430,333 patients with a mean follow-up of 15.36 months and cumulative incidence of psychosis at two years of 1.43%. There were profound differences compared to the original development UK database in terms of case-mix, psychosis incidence, distribution of baseline predictors (ICD-10 cluster diagnoses), availability of patient-level ethnicity data, follow-up time and availability of specialized clinical services for at-risk individuals. Despite these important differences, the model retained accuracy significantly above chance (Harrell's C = 0.676, 95% CI: 0.672-0.679). To date, this is the largest international external replication of an individualized prognostic model in the field of psychiatry. This risk calculator is transportable on an international scale to improve the automatic detection of individuals at risk of psychosis.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Psychosis in German prisoners: Comparison of the clinical appearance of psychotic disorder of an imprisoned population with a not detained community group

Surveys confirm risk factors for the incarceration of patients with psychosis including homelessness and comorbidity. There is also agreement that severe psychosis can lead to violence. Data describing prisoners with psychosis in Germany are scarce. We aimed to compare patients with psychosis in a prison hospital and patients with psychosis in a community hospital. Demographic data were collected, as well as comorbidity in the form of substance dependence and a psychiatric assessment using the German version of the 18-item Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS). In the prison hospital group more patients were homeless (17 versus 2%) and non-German (36 versus 4%). There were also more patients with substance dependence or abuse in the prison hospital group. The total scores of BPRS and PANSS were lower in the prison hospital group (BPRS, 43.8 versus 51.2; PANSS, 71.5 versus 83.7). We assume that social disintegration for mentally disturbed offenders prior to incarceration hindered effective treatment. To avoid further social disintegration and possible further deterioration of mental health status of released offenders, which may lead to reoffending after imprisonment, discharge management after release from prison should be improved.

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.

Violent outcomes in first-episode psychosis: A clinical cohort study

AIM

Violence risk is an important part of a comprehensive clinical assessment in first-episode psychosis. This study addresses limitations of previous violent outcome research in first-episode psychosis, which has typically investigated selected cohorts or been restricted to violence occurring prior to service contact, with limited use of police data.

METHODS

For individuals consecutively assessed by Early Intervention in Psychosis (EIP) services in two UK regions (n = 177), violent outcomes in the subsequent 12-months were collected using electronic patient records, supplemented by police data.

RESULTS

Of individuals accepted by EIP services (n = 109), electronic medical records indicated around 1 in 4 (n = 28, 25.7%) perpetrated any physical violence, and 1 in 10 (n = 10, 9.2%) were arrested or charged for violent offences in the 12-months after first contact. Police data on all individuals assessed (n = 177) reported 1 in 7 (n = 26, 14.7%) were arrested or charged for violent offences in the 12-months after first contact.

CONCLUSIONS

EIP services should consider integrating multi-agency sources of data to evaluate violent outcomes. The potential role of violence risk management should be further examined.

Delusion, excitement, violence, and suicide history are risk factors for aggressive behavior in general inpatients with serious mental illnesses: A multicenter study in China

Little is known about the risk factors for aggression in general clinical settings in China. The aim of this study is to explore potential risk factors for inpatients with serious mental illness. The study was conducted from 15 March to 14 April 2013 and involved 16 general psychiatric institutions in China. A standardized data collection form was used to collect demographic and clinical characteristics data, including information on current hallucinations, delusions, depression, excitement, aboulia, apathy, and adherence to treatment. Information on lifetime history of violence and suicidality was also collected. The Modified Overt Aggression Scale (MOAS) was also administered to indicate recent (past week) aggression. A total of 511 inpatients were enrolled. On the basis of a score of five or greater on the MOAS, 245 inpatients were assigned to aggressive group and 266 were assigned to non-aggressive group. A lifetime history of violent behaviour (OR = 3.1, 95% CI = 1.95-5.11), suicide (OR = 3.0, 95% CI = 1.49-6.10), as well as current delusions (OR = 1.92, 95% CI = 1.24-2.97), and excitement (OR = 2.63, 95% CI = 1.57-4.39) were associated with aggression. The study suggested violent history, suicide history, current delusions, and excitement are the risk factors for aggression among general psychiatric inpatients with serious mental illnesses.

Prediction of violent reoffending in prisoners and individuals on probation: a Dutch validation study (OxRec)

Scalable and transparent methods for risk assessment are increasingly required in criminal justice to inform decisions about sentencing, release, parole, and probation. However, few such approaches exist and their validation in external settings is typically lacking. A total national sample of all offenders (9072 released from prisoners and 6329 individuals on probation) from 2011–2012 in the Netherlands were followed up for violent and any reoffending over 2 years. The sample was mostly male (n = 574 [6%] were female prisoners and n = 784 [12%] were female probationers), and median ages were 30 in the prison sample and 34 in those on probation. Predictors for a scalable risk assessment tool (OxRec) were extracted from a routinely collected dataset used by criminal justice agencies, and outcomes from official criminal registers. OxRec’s predictive performance in terms of discrimination and calibration was tested. Reoffending rates in the Dutch prisoner cohort were 16% for 2-year violent reoffending and 44% for 2-year any reoffending, with lower rates in the probation sample. Discrimination as measured by the c-index was moderate, at 0.68 (95% CI: 0.66–0.70) for 2-year violent reoffending in prisoners and between 0.65 and 0.68 for other outcomes and the probation sample. The model required recalibration, after which calibration performance was adequate (e.g. calibration in the large was 1.0 for all scenarios). A recalibrated model for OxRec can be used in the Netherlands for individuals released from prison and individuals on probation to stratify their risk of future violent and any reoffending. The approach that we outline can be considered for external validations of criminal justice and clinical risk models.

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

Background: Although there is evidence that individuals who suffer from severe mental disorders are at higher risk for aggressive behavior, only a minority eventually become violent. In 2017, Fazel et al. developed a risk calculator (Oxford Mental Illness and Violence tool, OxMIV) to identify the risk of violent crime in patients with mental disorders. For the first time, we tested the predictive validity of the OxMIV in the department of psychiatry at the prison hospital in Berlin, Germany, and presented findings from our internal validation. Materials and Methods: We designed a cohort study with 474 patients aged 16-65 years old who met the inclusion criteria of schizophrenia-spectrum or bipolar disorder and classified the patients into two groups: a violent group with 191 patients and a nonviolent group with 283 patients. Violence was defined as the aggressive behavior of a patient with the necessity of special observation. We obtained all the required information retrospectively through patient files, applied the OxMIV tool on each subject, and compared the results of both groups. Sensitivity, specificity, and positive/negative predictive values were determined. We used logistic regression including variable selection and internal validation to identify relevant predictors of aggressive behavior in our cohort. Results: The OxMIV score was significantly higher in the violent group [median 4.21%; Interquartile range (IQR) 8.51%] compared to the nonviolent group (median 1.77%; IQR 2.01%; p < 0.0001). For the risk of violent behavior, using the 5% cutoff for "increased risk," the sensitivity was 44%, and the specificity was 89%, with a positive predictive value of 72% and a negative predictive value of 70%. Applying logistic regression, four items were statistically significant in predicting violent behavior: previous violent crime (adjusted odds ratio 5.29 [95% CI 3.10-9.05], p < 0.0001), previous drug abuse (1.80 [1.08-3.02], p = 0.025), and previous alcohol abuse (1.89 [1.21-2.95], p = 0.005). The item recent antidepressant treatment (0.28 [0.17-0.47]. p < 0.0001) had a statistically significant risk reduction effect. Conclusions: In our opinion, the risk assessment tool OxMIV succeeded in predicting violent behavior in imprisoned psychiatric patients. As a result, it may be applicable for identification of patients with special needs in a prison environment and, thus, improving prison safety.

A Clinical Feasibility Study of the Forensic Psychiatry and Violence Oxford (FoVOx) Tool

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.

The Science of Prognosis in Psychiatry: A Review

IMPORTANCE

Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.

OBSERVATIONS

The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.

CONCLUSIONS AND RELEVANCE

Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

Prediction of Violence, Suicide Behaviors and Suicide Ideation in a Sample of Institutionalized Offenders With Schizophrenia and Other Psychosis

This study examined the predictive validity of the Spanish version of the Suicide Risk Assessment Manual (S-RAMM) and the Historical-Clinical-Risk Management-20 (HCR-20) in a sample of violent offenders with schizophrenia and other psychosis, who had committed violent crimes and had been sentenced to compulsory psychiatric treatment by the criminal justice system. Patients were prospectively monitored within the institution for 18 months. During the follow-up period, 25% of offenders were involved in any suicidal behavior including acts of self-harm, suicidal ideation and suicide attempts and 34% were physically or verbally violent. The S-RAMM and HCR-20 risk assessment tools were strongly correlated and were able to predict suicidal behavior and violence with a moderate-large effect size (AUCs = 0.81-0.85; AUCs = 0.78-0.80 respectively). Patients scoring above the mean on the S-RAMM (>20-point cut-off) had a five times increased risk of suicide related events (OR = 5.05, 95% CI = 2.6-9.7) and sevenfold risk of violence in the HCR-20 (>21-point cut-off) (OR = 7.13, 95% CI = 2.0-21.2) than those scoring below the mean. Offenders at high risk for suicide and violence had significantly more suicide attempts (p < 0.001) and more prior sentences for violent crimes (p < 0.001). These results support the use of the S-RAMM and HCR-20 for clinical practice by providing evidence of the utility of these measures for predicting risk for suicidal and violent behavior in mentally disordered offenders.

Use of risk assessment instruments to predict violence in forensic psychiatric hospitals: a systematic review and meta-analysis

Background and Aims

Violent behaviour by forensic psychiatric inpatients is common. We aimed to systematically review the performance of structured risk assessment tools for violence in these settings.

Methods

The nine most commonly used violence risk assessment instruments used in psychiatric hospitals were examined. A systematic search of five databases (CINAHL, Embase, Global Health, PsycINFO and PubMed) was conducted to identify studies examining the predictive accuracy of these tools in forensic psychiatric inpatient settings. Risk assessment instruments were separated into those designed for imminent (within 24 hours) violence prediction and those designed for longer-term prediction. A range of accuracy measures and descriptive variables were extracted. A quality assessment was performed for each eligible study using the QUADAS-2. Summary performance measures (sensitivity, specificity, positive and negative predictive values, diagnostic odds ratio, and area under the curve value) and HSROC curves were produced. In addition, meta-regression analyses investigated study and sample effects on tool performance.

Results

Fifty-two eligible publications were identified, of which 43 provided information on tool accuracy in the form of AUC statistics. These provided data on 78 individual samples, with information on 6,840 patients. Of these, 35 samples (3,306 patients from 19 publications) provided data on all performance measures. The median AUC value for the wider group of 78 samples was higher for imminent tools (AUC 0.83; IQR: 0.71–0.85) compared with longer-term tools (AUC 0.68; IQR: 0.62-0.75). Other performance measures indicated variable accuracy for imminent and longer-term tools. Meta-regression indicated that no study or sample-related characteristics were associated with between-study differences in AUCs.

Interpretation

The performance of current tools in predicting risk of violence beyond the first few days is variable, and the selection of which tool to use in clinical practice should consider accuracy estimates. For more imminent violence, however, there is evidence in support of brief scalable assessment tools.

Predicting Violent Behavior: What Can Neuroscience Add?

The ability to accurately predict violence and other forms of serious antisocial behavior would provide important societal benefits, and there is substantial enthusiasm for the potential predictive accuracy of neuroimaging techniques. Here, we review the current status of violence prediction using actuarial and clinical methods, and assess the current state of neuroprediction. We then outline several questions that need to be addressed by future studies of neuroprediction if neuroimaging and other neuroscientific markers are to be successfully translated into public policy.

Selecting a risk assessment tool to use in practice:a 10-point guide

With the increase in the number of risk assessment tools and clinical algorithms in many areas of science and medicine, this Perspective article provides an overview of research findings that can assist in informing the choice of an instrument for practical use. We take the example of violence risk assessment tools in criminal justice and forensic psychiatry, where there are more than 200 such instruments and their use is typically mandated. We outline 10 key questions that researchers, clinicians and other professionals should ask when deciding what tool to use, which are also relevant for public policy and commissioners of services. These questions are based on two elements: research underpinning the external validation, and derivation or development of a particular instrument. We also recommend some guidelines for reporting drawn from consensus guidelines for research in prognostic models.