Pan Fenglian, Zhou You, Vivas-Valencia Carolina, Kong Nan, Ott Carol, Jalali Mohammad S, Liu Jian
Department of Systems and Industrial Engineering, University of Arizona, Tucson, Arizona, United States of America.
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States of America.
PLoS Comput Biol. 2025 May 5;21(5):e1012889. doi: 10.1371/journal.pcbi.1012889. eCollection 2025 May.
Substance use disorder, particularly opioid-related, is a serious public health challenge in the U.S. Accurately predicting opioid overdose events and stratifying the risk of having such an event are critical for healthcare providers to deliver effective interventions in patients with opioid overdose. Despite a large body of literature investigating various risk factors for the prediction, the existing research to date has not explicitly investigated and quantitatively modeled how an individual's past opioid overdose events affect future occurrences. In this paper, we proposed a covariate-adjusted triggering point process to simultaneously model the effect of various risk factors on opioid overdose events and the triggering mechanism among opioid overdose events. The prediction performance was assessed by the U.S. state-wise Medicaid reimbursement claims data. Compared with commonly used prediction models, the proposed model achieved the lowest Mean Absolute Errors and Mean Absolute Percentage Errors on 30-, 60-, 90, 120-, 150-, and 180-day-ahead predictions. In addition, our results showed the statistical significance of considering the triggering mechanism for recurrent opioid overdose events prediction. On average, around 47% of the event recurrence were explained by the triggering mechanism.
物质使用障碍，尤其是与阿片类药物相关的障碍，是美国面临的一项严峻的公共卫生挑战。准确预测阿片类药物过量事件并对发生此类事件的风险进行分层，对于医疗保健提供者为阿片类药物过量患者提供有效的干预措施至关重要。尽管有大量文献研究了各种预测风险因素，但迄今为止，现有研究尚未明确调查并定量建模个体过去的阿片类药物过量事件如何影响未来事件的发生。在本文中，我们提出了一种协变量调整触发点过程，以同时对各种风险因素对阿片类药物过量事件的影响以及阿片类药物过量事件之间的触发机制进行建模。通过美国各州的医疗补助报销申请数据评估预测性能。与常用的预测模型相比，所提出的模型在提前30天、60天、90天、120天、150天和180天的预测中实现了最低的平均绝对误差和平均绝对百分比误差。此外，我们的结果显示了考虑复发性阿片类药物过量事件预测触发机制的统计学意义。平均而言，约47%的事件复发可由触发机制解释。
