Using a Novel Comprehensive Linked Dataset to Determine Early Predictors of Opioid Overdose

使用新型综合关联数据集确定阿片类药物过量的早期预测因素

• 批准号: 9521054
• 负责人: Scott Gordon Weiner
• 金额: $ 50.09万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9521054
• 关键词: Address; Affect; American; Area; Benzodiazepines; Biometry; Cessation of life; Characteristics; Clinical; Clinical Research; Communities; Comorbidity; Complex; Data; Data Set; Databases; Death Rate; Diagnosis; Disease; Drug abuse; Early Intervention; Education; Emergency medical service; Environment; Environmental Risk Factor; Funding; Future; Goals; Guidelines; Hospitals; Household; Individual; Interdisciplinary Study; Investigation; Knowledge; Link; Logistic Models; Medicaid; Modeling; Opioid; Opioid Analgesics; Oregon; Outcome; Overdose; Patient risk; Patients; Pattern; Pharmacy facility; Play; Population; Predictive Factor; Prevention; Provider; Public Health; Recording of previous events; Records; Registries; Research; Risk; Risk Factors; Role; Source; Time; United States National Institutes of Health; adverse outcome; base; clinical practice; demographics; dosage; experience; improved; individual patient; innovation; novel; opioid overdose; opioid use; overdose risk; prescription drug abuse; prescription monitoring program; prescription opioid; prevent; response; tool

PROJECT SUMMARY/ABSTRACT To address more than a quadrupling of death rates from opioid overdose between 2000 and 2015, federal and state agencies have promoted clinician education and guidelines to reduce risky prescribing. Previous research has identified prescription patterns associated with elevated risk of opioid overdose, yet the relationship between patient and environmental factors, prescription use/misuse trajectories, and overdose likelihood remains largely unknown. This proposal, submitted in response to PAR-16-234 (Accelerating the Pace of Drug Abuse Research Using Existing Data), will develop comprehensive models for assessing opioid overdose risk, filling critical gaps in understanding of how prescription opioid use/misuse changes over time, how such changes affect overdose risk, which patients are most vulnerable to risky patterns, and what role household- and community-level prescription risk plays in overdose. The specific aims are: 1. Model effects of patient demographic and clinical characteristics and patient prescription patterns and their interactions on opioid-involved overdose (fatal or nonfatal). 2. Determine the effect of household-level prescription availability on opioid overdose. 3. Determine the effect of community-level prescription availability on opioid overdose. A key strength of our study is our novel linked dataset: the Oregon Comprehensive Opioid Risk Registry (CORR), which links prescription and clinical history across payers with diverse sources of overdose data, including data from the Oregon Prescription Drug Monitoring Program, Medicaid Claims, Vital Records, and Hospital Discharge registry, as well as All Payer/All Claims and Emergency Medical Services data. Our study will determine the odds of opioid-related overdose based on interactions of patient demographics, diagnoses/comorbidities, initial opioid prescriptions, household prescription risk levels, and community prescription risk levels. The study will use models to examine how risk builds over time and identify prescription patterns that portend increased risk at an early stage. Innovation: Our study creates a novel linked dataset and applies a complex analytic approach to radically expand understanding of patients' individual risk environments. Significance: This study will inform clinical practice by generating new knowledge that can help identify the most at-risk patients and modify opioid prescribing decisions regarding them. Impact: Hierarchical models which combine individuals' prescription trajectories and clinical histories with household-level and community-level risk factors can be extended to other complex diseases in which the adverse outcomes occur as a result of effects acting at different levels.

项目总结/摘要 为了解决2000年至2015年间阿片类药物过量导致的死亡率翻了两番以上的问题，联邦和 州政府机构已经促进了临床医生教育和指导方针，以减少风险处方。先前 研究已经确定了与阿片类药物过量风险升高相关的处方模式， 患者与环境因素、处方使用/误用轨迹和用药过量之间的关系 其可能性仍然很大程度上未知。该提案是根据PAR-16-234（加速 利用现有数据进行药物滥用研究的步伐），将开发评估阿片类药物的综合模型 过量风险，填补了理解处方阿片类药物使用/滥用如何随时间变化的关键空白， 这些变化如何影响过量风险，哪些患者最容易受到风险模式的影响，以及 家庭和社区一级的处方风险在过量用药中发挥作用。具体目标是： 1.患者人口统计学和临床特征以及患者处方模式及其 阿片类药物过量（致死性或非致死性）的相互作用。 2.确定家庭处方可用性对阿片类药物过量的影响。 3.确定社区处方可用性对阿片类药物过量的影响。 我们研究的一个关键优势是我们新的关联数据集：俄勒冈州综合阿片类药物风险登记处 （CORR），它将支付者的处方和临床历史与各种过量数据来源联系起来， 包括来自俄勒冈州处方药监测计划、医疗补助索赔、重要记录和 出院登记，以及所有付款人/所有索赔和紧急医疗服务数据。我们的研究 将根据患者人口统计学的相互作用确定阿片类药物相关过量的几率， 诊断/合并症，初始阿片类药物处方，家庭处方风险水平和社区 处方风险水平。该研究将使用模型来研究风险如何随着时间的推移而建立，并确定处方 在早期阶段预示风险增加的模式。创新：我们的研究创建了一个新的链接数据集， 应用复杂的分析方法，从根本上扩大对患者个体风险的理解 环境.意义：这项研究将通过产生新的知识来指导临床实践， 确定风险最高的患者，并修改有关他们的阿片类药物处方决定。影响：层级 将联合收割机个人处方轨迹和临床历史与家庭水平相结合的模型， 社区一级的危险因素可以扩展到其他发生不良后果的复杂疾病 这是由于不同层次的影响。