Jones Nicola R, Hickman Matthew, Bharat Chrianna, Nielsen Suzanne, Larney Sarah, Ghouse Nimnaz Fathima, Lappin Julia, Degenhardt Louisa
National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.
SydneyMSK Research Flagship Centre, University of Sydney, Sydney, Australia.
Addiction. 2025 Oct;120(10):2044-2054. doi: 10.1111/add.70095. Epub 2025 May 25.
People with opioid use disorder are at increased risk of intentional self-harm and suicide. Although risk factors are well known, most tools for identifying individuals at highest risk of these behaviours have limited clinical value. We aimed to develop and internally validate models to predict intentional self-harm and suicide risk among people who have been in opioid agonist treatment (OAT).
Retrospective observational cohort study using linked administrative data.
New South Wales, Australia.
46 330 people prescribed OAT between January 2005 and November 2017.
Intentional self-harm and suicide prediction within a 30-day window using linked population datasets for OAT, hospitalisation, mental health care, incarceration and mortality. Machine learning algorithms, including neural networks and gradient boosting, assessed over 80 factors during the last 3, 6 and 12 months. Feature visualisation using SHapley Additive exPlanations.
Gradient boosting identified 30 important factors in predicting self-harm and/or suicide. These included the most recent frequency of emergency department presentations; hospital admissions involving mental disorders such as borderline personality, substance dependence, psychosis and depression/anxiety; and recent release from incarceration. The best fitting model had a Gini coefficient of 0.65 [area under the curve (AUC) = 0.82] and was applied to 2017 data to estimate the probability of self-harm and/or suicide. On average 46 people (0.16%) (from a total of 28 000 people in OAT) experienced intentional self-harm or suicide per month. Applying a 0.15% probability threshold, approximately 5167 people were classified as high risk, identifying 69% of all self-harm or suicide cases per month. This figure reduced to 450 per month after excluding people already identified in the previous month.
Among people in opioid agonist treatment, administrative linked data can be used with advanced machine learning algorithms to predict self-harm and/or suicide in a 30-day prediction window.
阿片类物质使用障碍患者故意自伤和自杀的风险增加。尽管风险因素众所周知，但大多数用于识别这些行为最高风险个体的工具临床价值有限。我们旨在开发并在内部验证模型，以预测接受阿片类激动剂治疗（OAT）的人群中故意自伤和自杀风险。
使用关联行政数据的回顾性观察队列研究。
澳大利亚新南威尔士州。
2005年1月至2017年11月期间接受OAT处方的46330人。
使用OAT、住院、精神卫生保健、监禁和死亡率的关联人群数据集，在30天窗口内预测故意自伤和自杀。机器学习算法，包括神经网络和梯度提升，在过去3、6和12个月评估了80多个因素。使用夏普利值附加解释进行特征可视化。
梯度提升在预测自伤和/或自杀方面识别出30个重要因素。这些因素包括急诊科就诊的最近频率；涉及边缘性人格、物质依赖、精神病和抑郁/焦虑等精神障碍的住院情况；以及最近从监禁中获释。最佳拟合模型的基尼系数为0.65[曲线下面积（AUC）=0.82]，并应用于2017年数据以估计自伤和/或自杀的概率。平均每月有46人（0.16%）（在接受OAT治疗的28000人中）经历故意自伤或自杀。应用0.15%的概率阈值，约5167人被归类为高风险，识别出每月所有自伤或自杀病例的69%。排除前一个月已识别的人员后，该数字降至每月450人。
在接受阿片类激动剂治疗的人群中，行政关联数据可与先进的机器学习算法一起用于在30天预测窗口内预测自伤和/或自杀。
