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Cited by 234Suicide and Self-Harm StudiesMental Health via WritingBipolar Disorder and Treatment

Suicide prediction models: a critical review of recent research with recommendations for the way forward

Open access
Kessler, Ronald C., Bossarte, Robert M., Luedtke, Alex, Zaslavsky, Alan M., Zubizarreta, Jose R.·2020·Molecular Psychiatry
DOI 10.1038/s41380-019-0531-0PMC PMC7489362
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Abstract

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.

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  • Digital phenotyping takes advantage of unprecedented types and amounts of data including movement, pulse rate, blood pressure, and sleep, meals and other lifestyle patterns, in addition to repeated self-report and neurocognitive tests.

    “Digital phenotyping, the 'moment-by-moment quantification of the individuallevel human phenotype in situ using data from personal digital devices' 636 takes advantage of unprecedented types and amounts of data including movement, pulse rate, blood pressure, and sleep, meals and other lifestyle patterns, in addition to repeated self-report and neurocognitive tests. 637”

    From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [637]

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Research works printed but not linked: 4

  1. [34]Simon R Improving suicide risk assessment with evidence-based psychiatry In: Pompili M, Taterelli R editors. Evidence-based practice in suicidology: a sourcebook. Cambridge MA: Hogrefe Publishing; 2011, p. 45– 54.
  2. [52]Self-harm in over 8s: long-term management. National Institute for Health and Care Excellence (NICE) 2011 Accessed 30 April 2019 31891461
  3. [66]United States. Public Health Service. Office of the Surgeon General. 2012 National strategy for suicide prevention: goals and objectives for action. National Action Alliance for Suicide Prevention. Washington, DC: National Action Alliance for Suicide Prevention; 2012 https://www.surgeongeneral.gov/library/reports/national-strategy-suicide-prevention/full-report.pdf. 23136686
  4. [97]Olson RS, Sipper M, La Cava W, Tartarone S, Vitale S, Fu W A system for accessible artificial intelligence. arXiv:1705.00594v2. 2017 Available from: https://arxiv.org/abs/1705.00594. · arXiv

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Authors
Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M., & Zubizarreta, J. R.
Affiliations
  • Harvard University (US)
  • West Virginia University (US)
  • University of Washington (US)
Corresponding author
Kessler, Ronald C.
ORCID
Venue
Molecular Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2019-09-30
Volume
25
Issue
1
Pages
168-179
ISSN
  • 1359-4184 (ISSN-L)
  • 1476-5578
DOI
10.1038/s41380-019-0531-0
PMC
PMC7489362
OpenAlex
W2975086838
Cited by
234
References
112
Citations by year
2026: 37 · 2025: 42 · 2024: 33 · 2023: 36 · 2022: 43 · 2021: 26 · 2020: 16 · 2019: 1
FWCI
3.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
Department of Veterans Affairs Center of Excellence for Suicide Prevention; The Precision Treatment of Mental Disorders Initiative
Topics
  • Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
Keywords
suicide risk prediction, suicide prevention, clinical utility, net benefit, positive predictive value, sensitivity, screening, individualized treatment rules, rare outcomes, preventive interventions
MeSH terms
Suicide Prevention, Forecasting, Humans, Suicide, Risk Assessment

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