Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance

项目概要 美国食品和药物管理局的首要目标之一是有效实施上市后 已批准药物的药物警戒能力。为儿科人群实现这一目标的关键是 特别具有挑战性。例如，目前对安全性、风险、药物相互作用和药物相互作用知之甚少。 由于严格的法规，怀孕期间使用的许多药物具有致畸作用 孕妇参与药物开发试验。此外，许多药物的安全性和有效性 由于缺乏针对儿童的临床试验，儿科使用很少。为此，儿科实践中经常 涉及“标签外”使用副作用未知的药物。这可能会造成不可预测的悲剧性影响 儿科患者，包括可能影响其发育和发育的严重药物不良反应和毒性 未来的生殖能力。大量现实世界医疗保健数据的可用性，例如电子数据 健康记录（EHR）提供了一个机会来满足有效调查健康记录影响的关键需求 儿童群体大规模接触药物。我们的目标是进行药物和表组范围的研究 对母子二人组的大型 EHR 数据库进行关联研究，这将使我们能够研究儿科不良反应 与以下因素相关的结果： 1) 母亲在怀孕期间和怀孕前接触药物和物质的情况；和 2) 儿童在所有发育里程碑期间的药物暴露。二次分析将包括 母亲的物质使用暴露与儿科结局、广泛的药物相互作用之间的关联 关联研究，以及药物与物质使用相互作用的广泛关联研究。此外，我们将利用 自然语言处理（NLP）和机器学习等人工智能方法来解决 暴露错误分类并改善拟议研究的儿科结果识别。我们的项目 目标是：1）进行高通量药物流行病学研究，以确定儿科不良结局， 2）利用利益相关者评估实时儿科药物警戒系统的临床效用 参与策略。该提案的预期成果是一个利益相关者知情的工具来监控 实时了解儿童药物不良反应。这将为临床部署铺平道路 用于早期发现儿科人群药物不良反应并进行实时决策支持系统 识别有此类负面结果风险的患者。

PROJECT SUMMARY One overarching goal of the US Food and Drug Administration is to effectively implement post-market pharmacovigilance capabilities of already approved medications. Achieving this goal for pediatric population is particularly challenging. For example, little is currently known about the safety, risks, drug-interactions, and teratogenic effects of many drugs used during pregnancy due to the strict regulations imposed for the participation of pregnant women in drug development trials. Further, safety and efficacy of many drugs for pediatric use is scarce due to the lack of clinical trials on children. For this reason, pediatric practice often involves “off-label” use of drugs with unknown side effects. This may cause unpredictable and tragic effects in pediatric patients including severe adverse drug reactions and toxicity that can affect their development and future reproductive capacity. The availability of large volumes of real-world healthcare data such as electronic health records (EHRs) provides an opportunity to meet the critical need of effectively investigating the effect of drug exposures on pediatric populations at large scale. Our goal is to conduct drug- and phenome-wide association studies on a large EHR database of mother-child dyads that will allow us to study adverse pediatric outcomes associated with 1) drug and substance use exposures of mothers during and before pregnancy; and 2) drug exposures of children during all their developmental milestones. Secondary analyses will include associations between substance use exposure of mothers and pediatric outcomes, drug-drug interaction wide association studies, and drug-substance use interaction wide association studies. Further, we will leverage artificial intelligence methods such as natural language processing (NLP) and machine learning to address exposure misclassification and improve pediatric outcome identification for the proposed studies. Our project aims are to: 1) conduct high-throughput pharmacoepidemiologic studies to identify adverse pediatric outcomes, and 2) evaluate the clinical utility of a real-time pediatric pharmacovigilance system using stakeholder engagement strategies. The expected outcome of this proposal is a stakeholder-informed tool to monitor adverse drug reactions of children in real-time. This will pave the way towards the deployment of a clinical decision support system for early detection of adverse drug reactions in pediatric populations and for real-time identification of patients who are at risk of such negative outcomes.