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Predicting suicidal behaviours using clinical instruments: Systematic review and meta-analysis of positive predictive values for risk scales

  • Gregory Carter (a1), Allison Milner (a2), Katie McGill (a1), Jane Pirkis (a3), Nav Kapur (a4) and Matthew J. Spittal (a3)...



Prediction of suicidal behaviour is an aspirational goal for clinicians and policy makers; with patients classified as ‘high risk’ to be preferentially allocated treatment. Clinical usefulness requires an adequate positive predictive value (PPV).


To identify studies of predictive instruments and to calculate PPV estimates for suicidal behaviours.


A systematic review identified studies of predictive instruments. A series of meta-analyses produced pooled estimates of PPV for suicidal behaviours.


For all scales combined, the pooled PPVs were: suicide 5.5% (95% CI 3.9–7.9%), self-harm 26.3% (95% CI 21.8–31.3%) and self-harm plus suicide 35.9% (95% CI 25.8–47.4%). Subanalyses on self-harm found pooled PPVs of 16.1% (95% CI 11.3–22.3%) for high-quality studies, 32.5% (95% CI 26.1–39.6%) for hospital-treated self-harm and 26.8% (95% CI 19.5–35.6%) for psychiatric in-patients.


No ‘high-risk’ classification was clinically useful. Prevalence imposes a ceiling on PPV. Treatment should reduce exposure to modifiable risk factors and offer effective interventions for selected subpopulations and unselected clinical populations.

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Corresponding author

Gregory Carter, Locked Bag #7, Hunter Region Mail Centre, 2301 NSW, Australia. Email:


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See editorial, pp. 384–386, this issue.

Declaration of interest

N.K. chaired the NICE guidelines for the longer term management of self-harm in England but the views in this paper are the author's own and not those of NICE or the Department of Health (UK). G.C. chaired the Royal Australian and New Zealand College of Psychiatrists' (RANZCP's) Clinical Practice Guidelines for Deliberate Self Harm but the views in this paper are the author's own and not those of the RANZCP.



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Predicting suicidal behaviours using clinical instruments: Systematic review and meta-analysis of positive predictive values for risk scales

  • Gregory Carter (a1), Allison Milner (a2), Katie McGill (a1), Jane Pirkis (a3), Nav Kapur (a4) and Matthew J. Spittal (a3)...


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