Violence risk assessment instruments in forensic psychiatric populations: a systematic review and meta-analysis

Abstract

Although structured tools have been widely used to predict violence risk in specialist mental health settings, there is uncertainty about the extent and quality of evidence of their predictive performance. We aimed to systematically review the predictive performance of tools used to assess violence risk in forensic mental health, where they are routinely administered. In our systematic review and meta-analysis, we followed PRISMA guidelines and searched four databases (PsycINFO, Embase, Medline, and Global Health) from database inception to Nov 1, 2022, to identify studies examining the predictive performance of risk assessment tools in people discharged from forensic (secure) mental health hospitals. Systematic and narrative reviews were excluded from the review. Performance measures and descriptive statistics were extracted from published reports. A quality assessment was performed for each study using the Prediction Model Risk of Bias Assessment Tool. Meta-analysis was conducted on the performance of instruments that were independently externally validated with a sample size greater than 100. The study was registered with PROSPERO, CRD42022304716. We conducted a systematic review of 50 eligible publications, assessing the predictive performance of 36 tools, providing data for 10 460 participants (88% men, 12% women; median age [from 47 studies] was 35 years, IQR 33-38) from 12 different countries. Post-discharge interpersonal violence and crime was most often measured by new criminal offences or recidivism (47 [94%] of 50 studies); only three studies used informant or self-report data on physical aggression or violent behaviour. Overall, the predictive performance of risk assessment tools was mixed. Most studies reported one discrimination metric, the area under the receiver operating characteristic curve (AUC); other key performance measures such as calibration, sensitivity, and specificity were not presented. Most studies had a high risk of bias (49 [98%] of 50), partly due to poor analytical approaches. A meta-analysis was conducted for violent recidivism on 29 independent external validations from 19 studies with at least 100 patients. Pooled AUCs for predicting violent outcomes ranged from 0·72 (0·65-0·79; I2=0%) for H10, to 0·69 for the Historical Clinical Risk Management-20 version 2 (95% CI 0·65-0·72; I2=0%) and Violence Risk Appraisal Guide (0·63-0·75; I2=0%), to 0·64 for the Static-99 (0·53-0·73; I2=45%). Current violence risk assessment tools in forensic mental health have mixed evidence of predictive performance. Forensic mental health services should review their use of current risk assessment tools and consider implementing those with higher-quality evidence in support. Wellcome Trust.