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Can we predict dangerousness in our patients?

Can we predict dangerousness in our patients?. Seena Fazel Wellcome Senior Research Fellow, University of Oxford & Honorary Consultant Forensic Psychiatrist, Oxford Health. Background and context What is the association between severe mental illness and violence?

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Can we predict dangerousness in our patients?

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  1. Can we predict dangerousness in our patients? Seena Fazel Wellcome Senior Research Fellow, University of Oxford & Honorary Consultant Forensic Psychiatrist, Oxford Health

  2. Background and context • What is the association between severe mental illness and violence? • How can we predict violence in patients with severe mental illness?

  3. Public health impact of interpersonal violence • Mortality and morbidity • Increasing numbers of secure hospital beds and prisoners

  4. Deaths by cause, estimates for 2004 (total deaths, % total)

  5. Disability Adjusted Life Years (DALYs)

  6. Prison population England & Wales

  7. Reinstitutionalization Priebe, 2008

  8. Khiroya, 2009

  9. Cost of mental health services in 2009/10

  10. Figure 2b - Risk estimates with substance abuse comorbidity

  11. Figure 6a – Risk estimates for violence in men with schizophrenia comorbid with substance abuse compared with risk in men with substance abuse (without psychosis) reported in the same study

  12. The problem of risk assessment • Current approaches are expensive, resource-intensive, and not scalable • Risk assessment has mixed evidence for predictive ability • Guru-like system of occasional training • 120+ structured instruments

  13. National Confidential Inquiry data- homicides by psychiatric patients

  14. Risk assessment tools – research questions How do these measures compare with other medical technologies? Is there an authorship effect? Which are the best ones? Are they more useful for some people than others? Does their predictive validity change using different study designs?

  15. Design-related biases? Lijmer, JAMA 1999

  16. Authorship effects? Bekelman, JAMA 2003

  17. New review and meta-analysis • 81 samples involving 26,426 individuals • Replication studies from 1 January 1995 to 1 January 2011 • Diagnostic odds ratio (DOR), sensitivity, specificity, area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), and the number needed to detain (NND) to prevent one offence were calculated

  18. Other prognostic tools • AUCs from cardiovascular prognostic tools similar: Framingham 0.57-0.86, SCORE 0.65-0.85, QRISK 0.76-0.79 • DORs for diagnostic tests considerably higher

  19. Summary • Violent risk prediction cannot be done accurately • Cannot be used as sole determinants of sentencing, release or discharge • Violence risk assessment instruments have moderate PPVs, higher NPVs

  20. Implications • Screen out those at low risk • Mixture of clinical judgement and evidence-based clinical prediction rules • Risk management • We cannot predict on individual patient basis

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