Predicting high-risk opioid prescriptions before they are given
Predicting high-risk opioid prescriptions before they are given
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DOI：
10.1073/pnas.1905355117
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发表时间：
2020-01-28
影响因子：
11.1
通讯作者：
Inman, Sarah E.
中科院分区：
文献类型：
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作者：
Hastings, Justine S.;Howison, Mark;Inman, Sarah E.
在美国，滥用处方阿片类药物是导致过早死亡的主要原因。我们使用州政府行政数据和机器学习方法来检查是否可以在初始阿片类药物处方之前预测未来阿片类药物依赖，滥用或中毒的风险。我们的模型准确地预测了这些结果，并确定了特定的先前非阿片类药物处方、病史、监禁和人口统计学作为强有力的预测因素。利用我们的估计，我们模拟了一个假设的政策，该政策将新的阿片类药物处方限制在那些预测风险较低的人。即使是对“高风险”的宽松定义，该政策的潜在收益也可能超过人口分组的成本。我们的研究结果提出了使用国家行政数据进行预防的新途径，这可以帮助提供者在权衡阿片类药物治疗的医疗益处与风险时做出更好的、有数据依据的决策。
Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.