Suicide risk assessment among psychiatric inpatients: a systematic review and meta-analysis of high-risk categories
Abstract
Background: The clinical care of psychiatric patients is often guided by perceptions of suicide risk. The aim of this study was to examine the methods and results of studies reporting high-risk models for inpatient suicide. Methods: We conducted a registered meta-analysis according to PRISMA guidelines. We searched for relevant peer-reviewed cohort and controlled studies indexed in Medline, EMBASE and PsychINFO. Results: The pooled odds ratio (OR) among 18 studies reporting high-risk models for inpatient suicide was 7.1 [95% confidence interval (CI) 4.2–12.2]. Between-study heterogeneity in ORs was very high (range 0–94.8, first quartile 3.4, median 8.8, third quartile 26.1, prediction interval 0.80–63.1,I2= 88.1%). The meta-analytically derived sensitivity was 53.1% (95% CI 38.2–67.5%,I2= 95.9%) and specificity was 84.2% (95% CI 71.6–91.9%,I2= 99.9%) with an associated meta-analytic area under the curve of 0.83. The positive predictive value of risk categorization among six cohort studies was 0.43% (95% CI 0.014–1.3%,I2= 95.9%). A history of suicidal behavior and depressive symptoms or affective disorder was included in the majority of high-risk models. Conclusions: Despite the strength of the pooled association between high-risk categorization and suicide, the very high degree of observed heterogeneity indicates uncertainty about our ability to meaningfully distinguish inpatients according to suicide risk. The limited sensitivity and low positive predictive value of risk categorization suggest that suicide risk models are not a suitable basis for clinical decisions in inpatient settings.
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Digital technologies for workforce training and monitoring of care include natural language processing of the services as they are delivered, and automatic feedback on adherence to quality indicators or indicators of individual improvement.
“Digital technologies for workforce training 631 and monitoring of care include natural language processing of the services as they are delivered, and automatic feedback on adherence to quality indicators or indicators of individual improvement.”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [631]
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References 49 · Cited by 76
- Authors
- Large, M., Myles, N., Myles, H., Corderoy, A., Weiser, M., Davidson, M., & Ryan, C. J.
- Affiliations
- UNSW Sydney (AU)
- Queen Elizabeth Hospital (AU)
- Adelaide University (AU)
- The University of Notre Dame Australia (AU)
- Tel Aviv University (IL)
- University of Nicosia (CY)
- The University of Sydney (AU)
- Corresponding author
- Large, M.
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2017-09-06
- Volume
- 48
- Issue
- 7
- Pages
- 1119-1127
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W2752429071
- Cited by
- 76
- References
- 49
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 9 · 2023: 12 · 2022: 9 · 2021: 6 · 2020: 12 · 2019: 10 · 2018: 5
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Keywords
- suicide risk assessment, psychiatric inpatients, inpatient suicide, risk classification, depressive symptoms, mood disorders, sensitivity and specificity, positive predictive value, clinical decision-making
- MeSH terms
- Depression, Humans, Inpatients, Risk Factors, Suicide, Risk Assessment, Mood Disorders
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