SynthesisJAMA psychiatry2019
Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation.
Synthesis in JAMA psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 257 papers, 9 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
257 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability.JMIR mental health · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The developmental origins of suicide mortality: a systematic review of longitudinal studies.European child & adolescent psychiatry · 2024Pooled it
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Shared understanding of resilient practices in the context of inpatient suicide prevention: a narrative synthesis.BMC health services research · 2022Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Suicidal ideation trajectories among adults following psychiatric hospitalization.Behaviour research and therapy · 2026Trial
- Risk Model-Guided Clinical Decision Support for Suicide Screening: A Randomized Clinical Trial.JAMA network open · 2025Trial
- A Randomized Control Trial of a Digital Health Tool for Safer Firearm and Medication Storage for Patients with Suicide Risk.Prevention science : the official journal of the Society for Prevention Research · 2024Trial
- Improving risk prediction for target subpopulations: Predicting suicidal behaviors among multiple sclerosis patients.PloS one · 2023Trial
- Identification of risk factors for attempted suicide by self-poisoning and a nomogram to predict self-poisoning suicide.Frontiers in public health · 2023Trial
- Effect of Algoplaque Hydrocolloid Dressing Combined with Nanosilver Antibacterial Gel under Predictive Nursing in the Treatment of Medical Device-Related Pressure Injury.Computational and mathematical methods in medicine · 2022Trial
- Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026Article
- Nocturnal Autonomic Dysregulation and Admission-Window Clinical Suicide-Risk Assessment in Hospitalized Children and Adolescents.Journal of clinical medicine · 2026Article
- Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction models.Molecular psychiatry · 2026Article
- Distress Tolerance and Self-Injurious Thoughts and Behaviors: A systematic review and commentary on methodological challenges.Clinical psychology : a publication of the Division of Clinical Psychology of the American Psychological Association · 2026Article
- Judgments of predictability, preventability and causation of mental health homicides in England 2010 - 2023.Medicine, science, and the law · 2026Article
197 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Suicide prediction models have the potential to improve the identification of patients at heightened suicide risk by using predictive algorithms on large-scale data sources. Suicide prediction models are being developed for use across enterprise-level health care systems including the US Department of Defense, US Department of Veterans Affairs, and Kaiser Permanente. Objectives: To evaluate the diagnostic accuracy of suicide prediction models in predicting suicide and suicide attempts and to simulate the effects of implementing suicide prediction models using population-level estimates of suicide rates. Evidence Review: A systematic literature search was conducted in MEDLINE, PsycINFO, Embase, and the Cochrane Library to identify research evaluating the predictive accuracy of suicide prediction models in identifying patients at high risk for a suicide attempt or death by suicide. Each database was searched from inception to August 21, 2018. The search strategy included search terms for suicidal behavior, risk prediction, and predictive modeling. Reference lists of included studies were also screened. Two reviewers independently screened and evaluated eligible studies. Findings: From a total of 7306 abstracts reviewed, 17 cohort studies met the inclusion criteria, representing 64 unique prediction models across 5 countries with more than 14 million participants. The research quality of the included studies was generally high. Global classification accuracy was good (≥0.80 in most models), while the predictive validity associated with a positive result for suicide mortality was extremely low (≤0.01 in most models). Simulations of the results suggest very low positive predictive values across a variety of population assessment characteristics. Conclusions and Relevance: To date, suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near 0. Several critical concerns remain unaddressed, precluding their readiness for clinical applications across health systems.
Indexed as
Identifiers
30865249What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.