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

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.

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.

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.