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

对基于处方的新型模型进行跨州验证，以预测新的长期阿片类药物使用

• 批准号: 10654837
• 负责人: Iraklis Erik Tseregounis
• 金额: $ 8.5万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10654837
• 关键词: Address; Administrator; Algorithms; Calibration; California; Clinical; Data; Discrimination; Documentation; Elasticity; Foundations; Future; Goals; Incidence; Individual; Intercept; Investments; Kentucky; Logistic Regressions; Machine Learning; Measures; Mission; Modeling; Opioid; Outcome; Overdose; Patient risk; Patients; Peer Review; Performance; Population; Prevention strategy; Privatization; Procedures; Public Health; Research; Resources; Risk Reduction; State Interests; Time; Translating; United States; United States National Institutes of Health; Update; Validation; catalyst; clinical decision support; clinical decision-making; clinical predictive model; demographics; drug abuse prevention; future implementation; improved; insight; machine learning model; novel; opioid overdose; opioid use; opioid use disorder; predictive modeling; predictive tools; prescription monitoring program; prescription opioid; prevent; programs; random forest; risk prediction model; statistics; tool; tool development; web interface

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.

项目总结/摘要 阿片类药物使用障碍和过量仍然是美国重大的公共卫生问题。疾控中心 已经确定减少以前从未使用过阿片类药物的患者过渡到长期（>90天）的人数 阿片类药物的使用，与过量和阿片类药物使用障碍相关的结果，作为一个关键的战略， 防止这些阿片类药物相关的危害。预测患者过渡到长期使用的风险的临床工具可以 是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，人口水平的受控物质 处方数据，B）大多数州的临床医生在开处方前需要检查的网络界面 阿片类药物，和c）对临床决策的基本支持（例如，识别接受处方的患者 从多个处方者）。为PDMP配备这些工具的主要障碍是各州人口不同 和PDMP独立运作;从头开始建立50个不同的预测模型是低效的， 不切实际各州需要准确和可推广的基于PDMP的预测模型， 临床工具，以帮助临床医生避免不适当的过渡到长期使用阿片类药物，并通过扩展， 防止阿片类药物相关的伤害。这个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，Pandemic管理员可以将其用作未来工具的基础 发展使用2016-2018年加州的数据，我们开发并验证了一种新的风险 预测模型，预测阿片类药物初治患者过渡到长期阿片类药物使用的可能性， 高区分度（一致性统计= 0.91）。我们提出的项目有两个目标。第一，评估 加利福尼亚州的风险预测模型如何推广到肯塔基州，一个有着实质性不同的州 人口统计学和阿片类药物处方和过量的比例高于加州，通过更新我们现有的 2018年模型预测事件长期使用在肯塔基州。第二，比较更新表单的准确性 现有的加州模型与使用肯塔基州Pestrian数据开发的新的州特定模型的比较。 这个项目将为Pingdom管理员提供有关使用 这一建议作为一个“基本模式”，而不是投资资源，以发展自己的国家特定的 模型研究结果将为未来在PDMP中实施预测工具奠定基础 肯塔基州和加州，并提供关键信息，以PPENDIX机构从其他国家感兴趣的 来实现他们自己的预测工具。拟议的研究是研究计划的必要步骤， 建立和实施基于PDMP的工具，以促进安全的阿片类药物处方并减少 阿片类药物过量，阿片类药物使用障碍，以及美国的其他阿片类药物相关危害。