Developing Patient-level Risk Prediction Models for Prescription Opioid Overdose

处方类阿片类药物过量患者风险预测模型的建立 摘要/摘要 与处方阿片类药物使用和滥用有关的发病率和死亡率是主要的临床和公共卫生问题。 有问题。降低处方阿片类药物过量比率需要多方面的努力，以期减少 两名患者都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)-全州电子数据库，包含所有受控物质 处方和临床医生可以实时查询-是推广安全阿片类药物的一个有前途的工具 开出处方，但它们的全部潜力仍未被开发。其中一个原因是，大多数先前的研究都 专注于患者的平均阿片类药物剂量，并使用了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、患者级别的纵向数据中获得证据 以更好地为国家和州减少处方阿片类药物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，阿片类药物剂量的比率 升级也是一个重要的、研究不足的过量用药风险预测指标。这项提案的长期目标是 为基于PDMP的多变量风险预测工具奠定基础，使临床医生和公共卫生 官员们可以用来评估服药过量的风险，就像目前使用Framingham类型的工具来评估 评估心脏风险。该提案的首要假设是阿片类药物剂量上升的速度将是 与过渡到长期使用阿片类药物和发生阿片类药物过量以及总体过量有关 风险将集中在相对较小的高危患者群体中。这项建议的目的是 确定与a)新阿片使用者过渡到长期使用有关的纵向阿片类药物处方模式 (例如，连续使用阿片类药物90天)，b)患者发生致命性或非致命性阿片类药物相关过量(包括 海洛因过量)，以及c)通过分析纵向、患者层面的处方和过量来重复过量 2008年至2016年加州所有地区的数据。我们将采取新的步骤，将全州范围内的3个纵向 患者级别的数据库：加州PDMP的处方数据、死亡证明数据和全州范围的 医院出院和急诊科数据。纵向数据的混合效应回归方法 用于分析阿片类药物处方模式与建议结果之间的关系。结果来自 这些分析将被用来开发和前瞻性地验证每个项目的临床风险预测模型 结果。该项目将产生经过验证的风险预测模型，这些模型来自基于人群的、患者级别的 将用于构建临床风险预测工具的纵向数据，这些工具最终可以合并到 PDMP，以便在护理点通知处方决定。加州的结果也将是有用的 到其他48个有PDMP的州。该项目推进了一项研究计划，旨在开发和 临床医生可以用来做出更安全的阿片类药物处方决定的评估工具，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。

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.