Developing a Real-Time Trajectory Tool to Identify Potentially Unsafe Concurrent Opioid and Benzodiazepine Use among Older Adults

项目总结/摘要 处方阿片类药物过量死亡在过去二十年中显着增加，其中三分之一 涉及同时使用苯二氮卓类（BZD）的死亡。尽管有临床指南和FDA的黑盒 警告不要同时使用阿片类药物（OPI）和BZD（以下简称OPI-BZD）， 2002年至2014年间，使用OPI-BZD的患者增加了41%。使用OPI-BZD会增加以下风险 过量和其他不良结果（例如，福尔斯、骨折、认知功能障碍），尤其是老年人 成年人了 OPI-BZD使用的剂量和持续时间因患者特征和合并症而异。然而，在这方面， 关于持续时间和剂量的阈值或最相关的OPI-BZD使用模式， 在老年人中存在过量服用和其他不良后果的风险。既往研究定义了OPI-BZD 使用任意阈值（例如，≥1天重叠供应），仅关注持续时间或剂量 而不是同时使用的持续时间和剂量的组合。适用任意和宽泛的阈值 如果没有证据和对所有患者的验证，将给定制临床护理带来挑战，并导致 涉及OPI-BZD使用的无效治疗和干预。或者，高级基于组的轨迹 模型（GBTM）可用于更好地表征OPI-BZD在临床实践中的使用。GBTM有能力 考虑动态药物使用，识别随时间变化相似的亚组，同时 检查剂量和持续时间阈值或与结果最相关的其他模式，以便更好地帮助临床 决策的 这项拟议的研究旨在开发一种创新的、实时的“预测危险的阿片类药物-苯二氮卓类药物”。 轨迹电子护理工具（PROTeCT）”，用于有效识别和预测老年人（年龄 ≥50岁），具有明显和潜在不安全的OPI-BZD使用模式。因为医疗保险和医疗补助 登记者正在经历越来越多的慢性疼痛状况，心理健康/物质使用 疾病和处方OPI使用，医疗保险和医疗补助是开发PROTeCT的理想环境 工具.使用2013-2016年的国家医疗保险索赔和亚利桑那州（AZ）和佛罗里达（FL）医疗补助数据，目标1 重点是确定OPI-BZD使用的不同轨迹。我们还将确定与以下因素相关的预测因素： 特定的轨迹或模式。在目标2中，我们将确定OPI-BZD使用的不同轨迹或模式 与两个独立的结果最密切相关的（即，过量;福尔斯和骨折）。最后， 我们建议开发一个实时PROTeCT平台的测试版，该平台能够前瞻性地和迭代地 通过前瞻性分析来自 2017-2019年亚利桑那州和佛罗里达州的医疗补助。我们的研究结果和工具的基础设施可以推广到 其他州的医疗保险、医疗补助计划或具有类似数据结构的其他医疗保健数据系统。

Project Summary/Abstract Prescription opioid overdose deaths have increased markedly in the past two decades, with a third of these fatalities involving concurrent benzodiazepine (BZD) use. Despite clinical guidelines and FDA black-box warnings cautioning against concurrent opioid (OPI) and BZD use (hereafter OPI-BZD use), the number of patients using OPI-BZD increased by 41% between 2002 and 2014. OPI-BZD use increases the risk of overdose and other adverse outcomes (e.g., falls, fractures, cognitive dysfunction), especially among older adults. The dose and duration of OPI-BZD use varies by patient characteristics and comorbid conditions. However, little is known about the thresholds of duration and dose or patterns of OPI-BZD use profiles most associated with the risk of overdose and other adverse outcomes among older adults. Prior studies have defined OPI-BZD use with arbitrary thresholds (e.g., ≥1 day overlapped supply) and focused only on duration or dose alone rather than combinations of duration and dose of concurrent use. Applying arbitrary and broad thresholds without evidence and validation to all patients imposes challenges in tailoring clinical care, and leads to ineffective therapies and interventions involving OPI-BZD use. Alternatively, advanced group-based trajectory models (GBTMs) can be used to better characterize OPI-BZD use in clinical practice. GBTMs have the ability to account for dynamic medication use, identify subgroups with similar changes over time, and simultaneously examine dose and duration thresholds or other patterns most relevant to outcomes in order to better aid clinical decision-making. The proposed study aims at developing an innovative, real-time “Predicting Risky Opioid-Benzodiazepine Trajectory e-Care Tool (PROTeCT)” for efficiently identifying and predicting subgroups of older adults (aged ≥50 years) with distinct and potentially unsafe patterns of OPI-BZD use. Because both Medicare and Medicaid enrollees are experiencing an increased number of chronic pain conditions, mental health/substance use disorders, and prescription OPI use, Medicare and Medicaid are ideal settings for developing the PROTeCT tool. Using national Medicare claims and Arizona (AZ) and Florida (FL) Medicaid data from 2013-2016, Aim 1 focuses on identifying distinct trajectories of OPI-BZD use. We will also identify predictors associated with specific trajectories or patterns. In Aim 2, we will identify the distinct trajectories or patterns of OPI-BZD use that are the most closely associated with two separate outcomes (i.e., overdoses; falls and fractures). Finally, we propose to develop a beta-version of a real-time PROTeCT platform capable of prospectively and iteratively predicting patients with unsafe patterns of OPI-BZD use by prospectively analyzing more recent data from the 2017-2019 Medicaid in AZ and FL. The infrastructure of our findings and tool may be generalizable to Medicare, Medicaid programs in other states or other healthcare data systems with similar data structures.