Abstract
Background and AimsViolent behaviour by forensic psychiatric inpatients is common. We aimed to systematically review the performance of structured risk assessment tools for violence in these settings. MethodsThe 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. ResultsFifty-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. InterpretationThe 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.
Highlights
Violence in inpatient psychiatric wards is a major problem for health services, with effects on patient and staff psychiatric morbidity [1], wider implications on stigma for patients and recruitment in psychiatric hospitals, alongside costs associated with injury, staff sickness, and potential litigation by victims
Current guidelines from the National Institute for Health and Care Excellence (NICE) [3] in England recommend the use of the Brøset Violence Checklist (BVC) [4,5] or the Dynamic Appraisal of Situational Aggression (DASA) [6] for the prediction of inpatient violence, US and Australasian guidelines do not appear to recommend any such tools for acute management of schizophrenia inpatients [7,8]
We developed a bivariate random-effects model that jointly analyzed pairs of sensitivities and specificities, taking into account their correlation with one another [33]. This model is a different parameterisation of the hierarchical summary receiver operating characteristic (HSROC) model [34]
Summary
Violence in inpatient psychiatric wards is a major problem for health services, with effects on patient and staff psychiatric morbidity [1], wider implications on stigma for patients and recruitment in psychiatric hospitals, alongside costs associated with injury, staff sickness, and potential litigation by victims. We aimed to systematically review the performance of structured risk assessment tools for violence in these settings. 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. 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. 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.
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