Cross-state validation of a novel prescription-based model to predict new long-term opioid use

项目摘要/摘要 在美国，阿片类药物使用障碍和过量使用仍然是严重的公共卫生问题。美国疾病控制与预防中心 已经确定减少之前阿片类药物天真的患者过渡到长期治疗(&gt；90天)的数量 阿片类药物使用，这是与过量和阿片类药物使用障碍有关的结果，作为治疗 防止这些与阿片类药物相关的伤害。预测患者过渡到长期使用风险的临床工具可以 成为这一战略的重要组成部分。处方药监测计划(PDMP)是理想的 实施平台，因为它们包含：a)全州范围内、人口层面的受控物质 处方数据，b)大多数州的临床医生在开处方前需要检查的Web界面 阿片类药物，以及c)临床决策的基本支持(例如，确定接受处方的患者 来自多个处方者)。为PDMP配备这些工具的关键障碍是各州人口的不同 和PDMP独立运行；从头开始构建50个不同的预测模型效率低下，而且 不切实际。各州需要准确和可推广的基于PDMP的预测模型，这些模型可以转化为 帮助临床医生避免不适当地过渡到长期使用阿片类药物的临床工具，进而， 防止与阿片类药物相关的伤害。该项目的目标是产生一个科学透明的、可推广的、 和临床上有用的预测模型，PDMP管理员可以将其用作未来工具的基础 发展。使用2016-2018年加州PDMP数据，我们开发并验证了一种新的风险 预测阿片类药物幼稚患者过渡到长期使用阿片类药物的可能性的预测模型 高分辨率(一致性统计=0.91)。我们提议的项目有两个目标。第一，评估 加州的风险预测模型如何推广到肯塔基州，肯塔基州与 通过更新我们现有的人口统计数据和阿片类药物处方和过量服药的比率高于加州 预测肯塔基州事件长期使用的2018模型。第二，比较更新后的表格的准确性 现有的加州模型与使用肯塔基州PDMP数据开发的新的州特定模型的对比。 该项目将为PDMP管理员提供有关使用以下产品之间的权衡的信息 这一建议作为一种“基础性模式”，而不是投入资源来制定自己的国家特色 模特们。研究结果将为未来在项目管理计划中实施预测工具奠定基础 并向其他感兴趣的州的PDMP机构提供关键信息 在实施自己的预测工具方面。拟议的研究是研究计划中的必要步骤，以 建立和实施基于PDMP的工具，促进安全的阿片类药物处方，减少 在美国，阿片类药物过量、阿片使用障碍和其他与阿片类药物相关的危害。

PROJECT SUMMARY/ABSTRACT Opioid use disorder and overdose remain significant public health concerns in the United States. The CDC has identified reducing the number of previously opioid-naïve patients who transition to long-term (>90 days) opioid use, an outcome associated with both overdose and opioid use disorder, as a key strategy for preventing these opioid-related harms. Clinical tools to predict patient risk of transition to long-term use can be an important component of this strategy. Prescription drug monitoring programs (PDMPs) are ideal implementation platforms because they contain: a) statewide, population-level controlled substance prescription data, b) web interfaces that clinicians in most states are required to check before prescribing opioids, and c) basic support for clinical decision-making (e.g., identifying patients receiving prescriptions from multiple prescribers). Key barriers to equipping PDMPs with these tools are that state populations differ and PDMPs operate independently; building 50 different prediction models from scratch is inefficient and impractical. States need accurate and generalizable PDMP-based prediction models that can be turned into clinical tools to help clinicians avoid inappropriate transitions to long-term opioid use and, by extension, prevent opioid-related harms. The goal of this project is to produce a scientifically transparent, generalizable, and clinically useful prediction model that PDMP administrators can use as a foundation for future tool development. Using 2016-2018 California PDMP data, we have developed and validated a novel risk prediction model that predicts an opioid-naïve patients’ likelihood of transitioning to long-term opioid use with high discrimination (concordance statistic = 0.91). Our proposed project has two objectives. First, to assess how a California-based risk prediction model generalizes to Kentucky, a state with substantially different demographics and higher rates of opioid prescribing and overdose than California, by updating our existing 2018 model to predict incident long-term use in Kentucky. Second, to compare accuracy of an updated form of the existing California model versus new state-specific models developed using Kentucky PDMP data. This project will provide PDMP administrators information about the trade-offs between using the product of this proposal as a “foundational model” versus investing resources to develop their own state-specific models. Study findings will set the stage for future implementation of prediction tools into the PDMPs of Kentucky and California, and also provide critical information to PDMP agencies from other states interested in implementing their own prediction tools. The proposed study is a necessary step in a research program to build and implement PDMP-based tools that promote safe opioid prescribing and reduce the incidence of opioid overdose, opioid use disorder, and other opioid-related harms in the United States.