Using a predicting Risky Opioid-Benzodiazepine Trajectory e-Care Tool (PROTeCT) to identify high-risk regions

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会增加用药过量的风险 以及其他不良后果，尤其是在老年人中。然而，人们对这些阈值知之甚少。 与药物过量风险和其他风险最相关的OPI-BZD使用特征的持续时间和剂量或模式 老年人的不良后果。先前的研究已经用任意阈值定义了OPI-BZD的使用（例如， ≥1天重叠供应），仅关注持续时间或剂量，而不是持续时间和剂量的组合 同时使用的剂量。在没有证据和验证的情况下对所有患者应用任意和宽泛的阈值 在定制临床护理方面提出了挑战，并导致涉及OPI的无效治疗和干预- 使用BZD。或者，可以使用高级基于组的轨迹模型（GBTM）来更好地表征 OPI-BZD在临床实践中的应用。GBTM能够说明动态药物使用情况， 随着时间的推移具有相似变化的亚组，并同时检查剂量和持续时间阈值或其他 与结果最相关的模式，以更好地帮助临床决策。 母体R21旨在开发一种创新的实时“预测风险阿片类药物-苯二氮卓轨迹” 电子护理工具（PROTeCT），用于有效识别和预测具有不同和 OPI-BZD使用的潜在不安全模式。因为医疗保险和医疗补助登记者正在经历一个 慢性疼痛状况、精神健康/物质使用障碍和处方OPI使用的数量增加， 医疗保险和医疗补助是开发PROTeCT工具的理想环境。使用国家医疗保险索赔和 亚利桑那州（AZ）和佛罗里达（FL）2013-2016年的医疗补助数据，目标1侧重于确定不同的轨迹 使用OPI-BZD。我们还将确定与特定轨迹或模式相关的预测因子。在目标2中， 将确定OPI-BZD使用的不同轨迹或模式，这些轨迹或模式与以下两种最密切相关： 单独的结果（即，过量;福尔斯和骨折）。最后，我们建议开发一个真实的原型- 时间PROTeCT平台能够前瞻性和迭代地预测具有不安全OPI模式的患者- 通过使用2017-2019年AZ和FL Medicaid数据前瞻性分析更近期的数据来使用BZD。为了 帮助博士洛-罗拉尼克在她最近的关键生活事件后成功完成这一R21，本补充要求 资金主要用于支持一名专门的项目协调员的工作，并包括两名额外的调查员， 目标3的卫生信息学专业知识。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。

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, especially among older adults. 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 to better aid clinical decision-making. The parent R21 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 with distinct and potentially unsafe patterns of OPI-BZD use. Because 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 prototype 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 using 2017-2019 AZ and FL Medicaid data. In order to help Dr. Lo-Ciganic to successfully complete this R21 after her recent critical life event, this supplement requests funds to mainly support a specialized program coordinator’s effort and include two additional investigators with health informatics expertise for Aim 3. 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.