oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Prediction of violent crime on discharge from secure psychiatric hospitals: A clinical prediction rule (FoVOx)
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Oxford Health NHS Foundation Trust, Oxford, UK.
Show others and affiliations
2018 (English)In: European psychiatry, ISSN 0924-9338, E-ISSN 1778-3585, Vol. 47, p. 88-93Article in journal (Refereed) Published
Abstract [en]

Background: 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.

Methods: We 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).

Results: We 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).

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. Further evaluation in other countries is needed.

Funding: Wellcome 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.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 47, p. 88-93
Keywords [en]
Forensic psychiatry, Violence, Psychometry and assessments in psychiatry, Risk assessment, Crime, Secure hospital, Clinical prediction
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:oru:diva-64527DOI: 10.1016/j.eurpsy.2017.07.011ISI: 000419527700013PubMedID: 29161680Scopus ID: 2-s2.0-85034666587OAI: oai:DiVA.org:oru-64527DiVA, id: diva2:1177458
Funder
Wellcome trust, 202836/Z/16/ZSwedish Research CouncilAvailable from: 2018-01-25 Created: 2018-01-25 Last updated: 2018-01-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Larsson, Henrik

Search in DiVA

By author/editor
Larsson, Henrik
By organisation
School of Medical Sciences
In the same journal
European psychiatry
Psychiatry

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 61 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf