Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis.
Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis.
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DOI：
10.1111/add.15878
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发表时间：
2022-08
期刊：
影响因子：
6
通讯作者：
Lo-Ciganic, Wei-Hsuan
中科院分区：
文献类型：
--
作者：
Guo, Jingchuan;Gellad, Walid F.;Yang, Qingnan;Weiss, Jeremy C.;Donohue, Julie M.;Cochran, Gerald;Gordon, Adam J.;Malone, Daniel C.;Kwoh, C. Kent;Kuza, Courtney C.;Wilson, Debbie L.;Lo-Ciganic, Wei-Hsuan
获取卫生保健数据时遇到的时间滞后是在实践中实施阿片类药物过量预测措施的一大障碍。对于一个人的阿片类药物过量风险随着时间的推移是如何变化的，人们知之甚少。我们的目标是确定在阿片类药物处方开始后，医疗补助受益人中个人预测的过量风险的纵向模式。一项回溯性队列研究。美国宾夕法尼亚州。宾夕法尼亚州医疗补助受益人年龄在18岁至之间，在2017年7月至2018年9月期间开始开阿片类药物处方(318,585名符合条件的受益人(平均年龄=39±12岁，女性=66%，白人=62%，黑人=25%)，我们首先应用先前开发和验证的机器学习算法，以获取每个开始阿片类药物治疗的受益人每3个月一次的阿片类药物过量急诊室或住院风险评分，直到退出医疗补助、死亡或观察结束(2018年12月)。我们进行了基于群体的轨迹建模，以确定这些预测的过量风险分数随时间的轨迹。在符合条件的受益人中，0.61%的患者在15个月的中位随访期内出现了≥1阿片类药物过量。我们确定了五条独特的阿片类药物过量风险轨迹：三条轨迹(占队列的92%)随着时间的推移具有一致的过量风险，包括一致的低风险(63%)、一致的中等风险(25%)和一致的高风险(4%)；另外两条轨迹(占8%)的过量风险随着时间的推移发生了很大的变化，包括一组从高风险过渡到中等风险(3%)和另一组随着时间的推移从中等风险增加到高风险(5%)。在美国宾夕法尼亚州，超过90%的拥有一张或多张阿片类药物处方的医疗补助受益人在15个月内有一致的、预测的阿片类药物过量风险。对于绝大多数个人来说，应用根据历史数据开发的阿片类药物预测算法可能不是在实践中实施的主要障碍。
The time lag encountered when accessing health care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one’s opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions. A retrospective cohort study. Pennsylvania, USA. Pennsylvania Medicaid beneficiaries aged 18 to 64 years who initiated opioid prescriptions between July 2017 and September 2018 (318,585 eligible beneficiaries (mean age=39±12 years, female=66%, White=62% and Black=25%), We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death, or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time. Among eligible beneficiaries, 0.61% had ≥1 occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time including consistent-low risk (63%), consistent-medium risk (25%), and consistent-high risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high to medium risk (3%) and another group that increased from medium to high risk over time (5%). Over 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.
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