Developing Patient-level Risk Prediction Models for Prescription Opioid Overdose

开发处方阿片类药物过量的患者级风险预测模型

• 批准号: 9364793
• 负责人: Stephen G Henry
• 金额: $ 60.48万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9364793
• 关键词: Accident and Emergency department; Acute; Address; Age; Benzodiazepines; California; Cardiac; Cause of Death; Cessation of life; Characteristics; Clinical; Communities; Comorbidity; Data; Data Aggregation; Data Analyses; Databases; Death Certificates; Development; Discipline; Dose; Dose-Rate; Drug Addiction; Drug Monitoring; Drug Prescriptions; Emergency department visit; Face; Goals; Health Planning; Health Policy; Health system; Heart Diseases; Heroin; Hospitals; Inpatients; Insurance Carriers; Intervention; Investigation; Knowledge; Link; Medical; Methods; Mission; Modeling; Morbidity - disease rate; Opioid; Outcome; Overdose; Pain management; Patient risk; Patients; Pattern; Policies; Population; Public Health; Records; Research; Research Personnel; Resources; Risk; Risk Factors; Schedule; System; Time; United States National Institutes of Health; base; clinical risk; cohort; design; drug abuse prevention; fighting; heroin overdose; high risk population; longitudinal analysis; longitudinal database; longitudinal dataset; mortality; novel; opioid abuse; opioid use; point of care; population based; predictive modeling; prescription opioid; prevent; programs; prospective; sex; tool

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.

开发处方阿片类药物过量的患者级风险预测模型 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。降低处方阿片类药物过量率需要在多个方面做出努力，旨在减少 两名患者向长期使用阿片类药物的过渡以及随后的用药过量风险。处方药 监控计划 (PDMP)——包含所有受控物质的全州电子数据库 处方并且临床医生可以实时查询——是促进安全阿片类药物的一种有前途的工具 处方，但其全部潜力尚未开发。造成这种情况的原因之一是大多数先前的研究都 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状况的数据 系统或保险公司。需要从大量、基于人群、患者层面的纵向数据中得出证据 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，大剂量 阿片类药物的使用与更大的过量风险相关，但我们的初步数据表明阿片类药物的剂量率 升级也是用药过量风险的一个重要但尚未得到充分研究的预测因素。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 官员可以使用弗雷明汉型工具目前用于评估药物过量风险的方式来评估药物过量风险。 评估心脏风险。该提案的总体假设是阿片类药物剂量增加的速度将是 与长期使用阿片类药物的过渡和阿片类药物过量事件以及总体过量相关 风险将集中在相对较小的高危患者群体中。该提案的目的是 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 （即连续使用阿片类药物超过 90 天），b) 患者发生致命或非致命的阿片类药物相关过量事件（包括 海洛因过量），以及 c） 通过纵向分析、患者层面的处方和过量重复用药过量 2008 年至 2016 年间整个加州的数据。我们将采取新颖的步骤，将 3 个全州范围的纵向数据联系起来 患者层面的数据库：来自加州 PDMP 的处方数据、死亡证明数据和全州范围的数据 出院和急诊科数据。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与提案结果之间的关联。结果来自 这些分析将用于开发和前瞻性验证每个疾病的临床风险预测模型 结果。该项目将产生基于人群、患者水平的经过验证的风险预测模型 纵向数据将用于构建最终可纳入临床风险预测工具 PDMP 以便在护理时为处方决策提供信息。加利福尼亚州的结果也很有用 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策的工具，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。