Pharmacovigilance Methods: Leveraging Heterogeneous Adverse Drug Reaction Data

药物警戒方法：利用异质药物不良反应数据

基本信息

批准号：
8660067
负责人：
CAROL FRIEDMAN
金额：
$ 41.78万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-01 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8660067
关键词：
Academic Medical Centers Address Adverse event Adverse reactions Cereals Cessation of life Chemicals Clinical Clinical Data Complement Data Data Set Data Sources Databases Detection Drug usage Effectiveness Electronic Health Record Evaluation Health Care Costs Healthcare Hospitalization Hospitals Individual Knowledge Lead Literature Logistic Regressions Medical Care Costs Methodology Methods Modeling Myocardial Infarction Natural Language Processing New York Patients Performance Pharmaceutical Preparations Population Population Heterogeneity Positioning Attribute Presbyterian Church Probability Process PubMed Publications Reaction Reference Standards Reporting Research Research Infrastructure Resources Risk Factors Rofecoxib Safety Signal Transduction Site Source Structure System Techniques United States Food and Drug Administration Work base chemical property conditioning cost data mining improved knowledge base novel patient population patient safety post-market prevent racial/ethnic difference research and development text searching

项目摘要

Adverse drug reactions (ADRs) are a major burden for patients and healthcare, causing preventable hospitalizations and deaths, and incurring a huge cost. The long-term objective of this proposal is to advance patient safety and reduce costs by discovering novel serious ADRs through use of automated methods that combine information from large and varied patient populations as well as from the literature. There have been considerable advances in pharmacovigilance, but more work is needed. For example, Vioxx, a commonly used drug, was recently found to cause at least 88,000 occurrences of myocardial infarction, highlighting the insufficiency of current methods. To date, methods have mainly depended on the use of single sources of data, primarily from the Federal Food and Drug Administration Adverse Event Reporting System (FAERS) and from electronic health records (EHRS). Although important, each of the sources has different limitations and advantages, and therefore, combining the data across them should lead to more effective drug safety surveillance by increasing the statistical power, and also by allowing each data source to complement the other sources. We already have developed methods associated with each of the single sources, and therefore, this is an excellent opportunity to build upon our research accomplishments to advance the state of the art in pharmacovigilance. More specifically, we will a) acquire and combine comprehensive clinical data from the electronic health records (EHRs) of two different health care sites serving diverse populations by utilizing natural language processing (NLP) to obtain vast quantities of fine-grained data, and then by developing data mining methodologies on the clinical data to detect novel ADR signals, b) analyze differences in therapy-related risk factors between the two EHR populations, such as racial and ethnic differences, c) detect ADR signals in the FAERS database using an established methodology, d) develop improved methods to acquire ADR signals based on information in the literature, and e) develop methods that utilize the results from the above sources to maximize effectiveness. We will focus on eight serious ADRs, and collect a high-quality reference standard for those ADRs so that we will be able to evaluate and compare performance of the different detection methods individually as well as the methods that combine the sources. This proposal is well positioned to overcome problems associated with existing automated methods, which are primarily based on use of individual sources of data. We are confident the methods will be effective because a strong infrastructure is in place for us to build upon. Most importantly, the methodology developed in this proposal presents an excellent chance to leverage heterogeneous data sources to dramatically improve patient safety and reduce costs.

药物不良反应 (ADR) 是患者和医疗保健人员的主要负担，导致可预防的不良反应住院和死亡，并造成巨大的费用。该提案的长期目标是推进通过使用自动化方法发现新的严重 ADR，确保患者安全并降低成本结合来自大量不同患者群体以及文献的信息。曾经有过药物警戒方面取得了相当大的进展，但还需要做更多的工作。例如，Vioxx，一种常用的最近发现这种药物会导致至少 88,000 例心肌梗塞发生，这凸显了现有方法的不足。迄今为止，方法主要依赖于单一数据源的使用，主要来自联邦食品和药物管理局不良事件报告系统 (FAERS) 和电子健康记录（EHRS）。尽管很重要，但每个来源都有不同的局限性和优点，因此，结合它们之间的数据应该会带来更有效的药物安全性通过提高统计能力以及允许每个数据源相互补充来进行监测来源。我们已经开发了与每个单一来源相关的方法，因此，这是一个很好的机会，可以利用我们的研究成果来推进最先进的技术药物警戒。更具体地说，我们将 a) 获取并结合来自电子健康的全面临床数据利用自然语言为不同人群提供服务的两个不同医疗保健站点的记录 (EHR) 处理（NLP）以获得海量的细粒度数据，然后通过开发数据挖掘检测新 ADR 信号的临床数据方法，b) 分析治疗相关风险的差异两个 EHR 人群之间的因素，例如种族和民族差异，c) 检测 ADR 信号使用既定方法的 FAERS 数据库，d) 开发改进的方法来获取 ADR 信号基于文献中的信息，以及 e) 开发利用上述来源的结果的方法最大限度地提高效率。我们将重点关注八个严重的ADR，为ADR收集高质量的参考标准。这些 ADR 以便我们能够评估和比较不同检测方法的性能单独以及组合来源的方法。该提案很好地克服了与现有自动化方法相关的问题，主要基于个人数据源的使用。我们相信这些方法将是有效的因为我们有强大的基础设施可供发展。最重要的是，该方法开发于该提案提供了一个利用异构数据源来显着改进的绝佳机会患者安全并降低成本。