Development and Applications of the Veterans Health Administration's Stratification Tool for Opioid Risk Mitigation (STORM) to Improve Opioid Safety and Prevent Overdose and Suicide
Development and Applications of the Veterans Health Administration's Stratification Tool for Opioid Risk Mitigation (STORM) to Improve Opioid Safety and Prevent Overdose and Suicide
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DOI：
10.1037/ser0000099
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发表时间：
2017-02-01
影响因子：
2.3
通讯作者：
Trafton, Jodie A.
中科院分区：
文献类型：
--
作者：
Oliva, Elizabeth M.;Bowe, Thomas;Trafton, Jodie A.
对阿片类药物相关不良事件（包括过量）的担忧促使退伍军人健康管理局（VHA）启动了阿片类药物安全倡议和过量教育和纳洛酮分发计划。为了减轻与阿片类药物处方相关的风险，一种考虑到两种风险因素（例如，剂量，物质使用障碍）和风险缓解干预措施（例如，尿液药物筛查、心理社会治疗）。本文介绍了阿片类药物风险缓解分层工具（STORM），这是VHA开发的一种工具，反映了这种整体方法，有助于患者识别和监测。STORM根据用药过量/自杀相关事件的建模风险，优先考虑患者进行审查和干预，并显示从VHA电子病历（EMR）-数据摘录中获得的风险因素和风险缓解干预措施。患者的估计风险基于使用2010财年（FY 2010：2009年10月1日-2010年9月30日）EMR开发的预测风险模型-在1，135，601例VHA患者中使用阿片类镇痛药处方的数据提取和死亡率数据，以预测2011财年过量/自杀相关事件的风险（2.1%发生事件）。交叉验证用于验证模型，训练和测试数据集的接收器操作特征曲线表现良好（曲线下面积> 0.80）。预测风险模型根据药物过量/自杀相关不良事件的风险对患者进行区分，允许识别高风险患者并丰富具有更大安全性问题的患者目标人群，以进行主动监测和应用风险缓解干预措施。结果表明，临床信息学可以利用EMR提取的数据来识别有过量/自杀相关事件风险的患者，并为临床医生提供可操作的信息以降低风险。
Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g.,dose, substance use disorders) and risk mitigation interventions (e.g.,urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation ( STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 ( FY2010: 10/1/2009-9/30/2010) EMR- data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 ( 2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well ( >.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/ suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk.