Abstract
BackgroundCurrent approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling. This study seeks to develop scalable predictive models for violent offending following discharge from secure psychiatric hospitals. MethodsWe identified all patients discharged from secure hospitals in Sweden between January 1, 1992 and December 31, 2013. Using multiple Cox regression, pre-specified criminal, sociodemographic, and clinical risk factors were included in a model that was tested for discrimination and calibration in the prediction of violent crime at 12 and 24 months post-discharge. Risk cut-offs were pre-specified at 5% (low vs. medium) and 20% (medium vs. high). ResultsWe identified 2248 patients with 2933 discharges into community settings. We developed a 12-item model with good measures of calibration and discrimination (area under the curve=0.77 at 12 and 24 months). At 24 months post-discharge, using the 5% cut-off, sensitivity was 96% and specificity was 21%. Positive and negative predictive values were 19% and 97%, respectively. Using the 20% cut-off, sensitivity was 55%, specificity 83% and the positive and negative predictive values were 37% and 91%, respectively. The model was used to develop a free online tool (FoVOx). InterpretationWe have developed a prediction score in a Swedish cohort of patients discharged from secure hospitals that can assist in clinical decision-making. Scalable predictive models for violence risk are possible in specific patient groups and can free up clinical time for treatment and management. Further evaluation in other countries is needed. FundingWellcome Trust (202836/Z/16/Z) and the Swedish Research Council. The funding sources had no involvement in writing of the manuscript or decision to submit or in data collection, analysis or interpretation or any aspect pertinent to the study.
Highlights
Current approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling
We identified a cohort of 2248 forensic psychiatric patients with 2933 discharges into community settings between 1 January 1992 and 31 December 2013, with 155 (6.9%) patients with violent offences within 12 months, and 244 (10.9%) within 24 months; 34 (1.5%) committed a serious violent crime within 24 months (Appendix Table 1 for types of crime pre- and post-discharge)
Previous serious violent crime was associated with a lower risk than nonserious violent crime, but a doubling compared to no violent crime
Summary
Current approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling. This study seeks to develop scalable predictive models for violent offending following discharge from secure psychiatric hospitals. Interpretation: We have developed a prediction score in a Swedish cohort of patients discharged from secure hospitals that can assist in clinical decision-making. Scalable predictive models for violence risk are possible in specific patient groups and can free up clinical time for treatment and management. A recent systematic review found studies from three European countries, showing high rates of violent offending following discharge from secure hospitals in England & Wales (7 studies; 1589 to 8403 per 100,000 person–years) [6], Sweden (3 studies; 1041 to 3019 per 100,000 person–years), and Norway (one study; 486 per 100,000 person–years).
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