Psychological Health Center of Excellence, Defense Health Agency, Silver Spring, Maryland.
Uniformed Services University of the Health Sciences, Bethesda, Maryland.
JAMA Psychiatry. 2019 Jun 1;76(6):642-651. doi: 10.1001/jamapsychiatry.2019.0174.
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.
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.
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.
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.
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.
自杀预测模型有可能通过使用大规模数据源上的预测算法来提高识别高危自杀患者的能力。自杀预测模型正在为包括美国国防部、美国退伍军人事务部和 Kaiser Permanente 在内的企业级医疗保健系统开发和使用。
评估自杀预测模型预测自杀和自杀未遂的诊断准确性，并使用自杀率的人群水平估计值模拟实施自杀预测模型的效果。
系统地在 MEDLINE、PsycINFO、Embase 和 Cochrane 图书馆中进行文献检索，以确定评估自杀预测模型在识别高自杀风险患者或自杀死亡风险患者的预测准确性的研究。每个数据库都从成立到 2018 年 8 月 21 日进行了搜索。搜索策略包括自杀行为、风险预测和预测建模的搜索词。还对纳入研究的参考文献进行了筛选。两位审查员独立筛选和评估了合格的研究。
从总共审查的 7306 篇摘要中，有 17 项队列研究符合纳入标准，代表来自 5 个国家的 64 个独特预测模型，涉及超过 1400 万名参与者。纳入研究的研究质量通常较高。总体分类准确性较好（大多数模型≥0.80），而与自杀死亡率阳性结果相关的预测有效性极低（大多数模型≤0.01）。结果模拟表明，在各种人群评估特征下，阳性预测值非常低。
迄今为止，自杀预测模型产生了准确的总体分类模型，但它们预测未来事件的准确性接近 0。有几个关键问题仍未得到解决，这限制了它们在整个卫生系统中临床应用的准备情况。
