Developing Patient-level Risk Prediction Models for Prescription Opioid Overdose

开发处方阿片类药物过量的患者水平风险预测模型 摘要/摘要 与处方阿片类药物使用和滥用相关的发病率和死亡率是主要的临床和公共卫生问题 问题减少处方阿片类药物过量率需要在多个方面做出努力， 患者过渡到长期使用阿片类药物及其随后的过量风险。处方药 监测计划（PDMP）-包含所有受控物质的全州电子数据库 处方和临床医生可以查询真实的时间-是一个有前途的工具，促进安全阿片类药物 处方，但其全部潜力尚未开发。原因之一是大多数先前的研究 重点关注患者的平均阿片类药物剂量，并使用汇总数据或仅限于特定健康的数据 系统或保险公司。需要从大量的、基于人群的、患者水平的纵向数据中获得证据 更好地为国家和州减少处方类阿片相关危害的努力提供信息。例如，高剂量 阿片类药物的使用与更大的过量风险有关，但我们的初步数据表明，阿片类药物的剂量率 剂量递增也是一个重要的、研究不足的过量风险预测因素。该提案的长期目标是 为临床医生和公共卫生部门基于多变量PDMP的风险预测工具奠定基础， 官员们可以用Framingham类型的工具来评估过量服用的风险， 评估心脏风险。该提案的首要假设是阿片类药物剂量递增的速度将是 与过渡到长期阿片类药物使用和阿片类药物过量事件相关， 风险将集中在相对较小的高风险患者群体中。这项建议的目的是 确定与a）新阿片类药物使用者过渡到长期使用相关的纵向阿片类药物处方模式 (i.e.,持续阿片类药物使用>90天），B）患者的致命或非致命阿片类药物相关过量事件（包括 海洛因过量），以及c）通过分析纵向患者水平处方和过量重复过量 2008年至2016年期间加州的所有数据。我们将采取新的步骤，连接3个全州纵向 患者水平的数据库：来自加州PICESTRA的处方数据、死亡证明数据和全州范围的数据 医院出院和急诊室数据。纵向数据的混合效应回归方法将 用于分析阿片类药物处方模式和提案结果之间的关联。结果 这些分析将用于开发和前瞻性验证每种疾病的临床风险预测模型。 结果。该项目将产生经验证的风险预测模型，这些模型来自基于人群的患者水平 纵向数据，将用于建立临床风险预测工具，最终可以纳入 PDMP，以便在护理点告知处方决定。来自加州的结果也将是有用的 其他48个拥有PDMP的州该项目推进了一项研究计划，旨在开发和 评估临床医生可以用来做出更安全的阿片类药物处方决定的工具，以及研究人员和 政策制定者可以用来设计和评估临床、公共卫生和政策干预措施。

Developing Patient-level Risk Prediction Models for Prescription Opioid Overdose Summary / Abstract Morbidity and mortality related to prescription opioid use and abuse are major clinical and public health problems. Reducing prescription opioid overdose rates requires efforts on multiple fronts aimed at reducing both patients’ transition to long-term opioid use and their subsequent overdose risk. Prescription drug monitoring programs (PDMPs)—statewide electronic databases containing all controlled substance prescriptions and that clinicians can query in real time—are one promising tool for promoting safe opioid prescribing, but their full potential remains untapped. One reason for this is that most prior research has focused on patients’ mean opioid dose, and has used either aggregate data or data restricted to specific health systems or insurers. Evidence derived from large, population-based, patient-level longitudinal data is needed to better inform national and state efforts to reduce prescription opioid-related harms. For example, high-dose opioid use is associated with greater overdose risk, but our preliminary data indicate that rate of opioid dose escalation is also an important and under-studied predictor of overdose risk. This proposal’s long-term goal is to lay the groundwork for multivariable PDMP-based risk prediction tools that clinicians and public health officials can use to assess overdose risk in the same way that Framingham-type tools are currently used to assess cardiac risk. The proposal’s overarching hypotheses are that rate of opioid dose escalation will be associated with both transition to long-term opioid use and incident opioid overdose, and that overall overdose risk will be concentrated in a relatively small group of high-risk patients. The objective of this proposal is to identify longitudinal opioid prescribing patterns associated with a) new opioid users’ transition to long-term use (i.e., continual opioid use for >90 days), b) patients’ incident fatal or nonfatal opioid-related overdose (including heroin overdose), and c) repeat overdose by analyzing longitudinal, patient-level prescribing and overdose data for all of California between 2008 and 2016. We will take the novel step of linking 3 statewide longitudinal databases at the patient level: prescribing data from California’s PDMP, death certificate data, and statewide hospital discharge and emergency department data. Mixed-effects regression methods for longitudinal data will be used to analyze associations between opioid prescribing patterns and proposal outcomes. Results from these analyses will be used to develop and prospectively validate clinical risk prediction models for each outcome. This project will produce validated risk prediction models derived from population-based, patient-level longitudinal data that will be used to build clinical risk prediction tools that can eventually be incorporated into PDMPs in order to inform prescribing decisions at the point of care. Results from California will also be useful to the 48 other states that have PDMPs. This project advances a research program aimed at developing and evaluating tools that clinicians can use to make safer opioid prescribing decisions, and that researchers and policymakers can use to design and evaluate clinical, public health, and policy interventions.