Big data apprOaches fOr Safe Therapeutics in Healthy Pregnancies (BOOST-HP)

项目摘要/摘要 在美国，孕妇平均使用4种药物，70%的人至少使用一种。然而，大多数药物缺乏 怀孕期间安全性的确凿证据：在2010年至2019年期间批准的290个FDA新标签中， 90%的人没有关于孕妇风险或好处的人类数据。以目前的证据生成 系统，怀孕期间形成证据的平均时间估计为27年，这也是 长。目前的证据生成在很大程度上依赖于观察性研究，通常是由来自 动物研究或从已知的药理途径推断，可能会遗漏妊娠特异性 背景。也没有给予足够的注意，以确定脆弱亚群的因果机制。 最大的风险。基于我们之前在FDA哨兵系统和CDC疫苗安全方面的数据挖掘工作 Datalink，进行药物流行病学研究，以评估产前用药的安全性，并与 特别关注怀孕期间的药物扫描，我们将实施三阶段小说反向翻译 加速证据生成的框架，该框架将使用数据挖掘(“扫描”)来识别新的暴露- 结果关联、分诊信号，然后正式评估优先级别最高的信号。为了实现我们的目标 目标，我们将使用我们为怀孕期间的药物评估开发的基础设施，包括精心策划的账单 来自美国国立卫生研究院合作实验室分布式研究网络和国家医疗补助信息的记录 系统，代表了广泛的私人和公共保险怀孕患者和他们的 后代。我们的具体目标是：(目标1)扫描(1a)流产和 产前暴露于个别药物、化学物质和治疗类别的水平；及(1b)50 妊娠期间最流行的药物，关于致畸风险的信息不完整，活体选择广泛 出生不良后果；以及(1c)通过专家小组审查确定信号的优先顺序。(目标2)用人谨慎 药物流行病学设计，以评估涉及(2a)流产的两个最优先的信号，以及 (2B)不利的活产结局。为了控制混淆和测量偏差，这些研究将 采用以前经过验证的措施，通过与胎儿死亡和出生的联系进一步加强 用于队列次样本的证书数据，以评估未测量的混杂并进行概率分析 对结果和暴露错误分类的敏感性分析。用于安全治疗的大数据方法 健康妊娠(Boost-HP)将通过评估在证据生成方面的创新进步 大量的曝光和结果同时出现。我们的长期目标是构建一个可重用、可伸缩的 加快药物安全性和有效性证据生成的方法和基础设施 在怀孕期间使用。通过利用在公共卫生领域成功部署的数据挖掘方法 以及多个联邦政府机构使用的基础设施，我们将重点研究 对对健康怀孕构成最大风险的新的、高优先级的信号进行努力。

PROJECT SUMMARY/ABSTRACT In the US, pregnant patients use 4 medications on average, and 70% use at least one. Yet, most drugs lack conclusive evidence about safety during pregnancy: of 290 new FDA labels approved between 2010 to 2019, 90% contain no human data on the risks or benefits for pregnant patients. With current evidence generation systems, the mean time for evidence development in pregnancy has been estimated at 27 years, which is too long. Current evidence generation relies largely on observational studies, typically prompted by signals from animal studies or extrapolation from known pharmacological pathways, which may miss pregnancy-specific context. Insufficient attention is also given to identifying causal mechanisms in vulnerable sub-populations at greatest risk. Building on our prior work in data-mining in FDA’s Sentinel System and CDC’s Vaccine Safety Datalink, conduct of pharmacoepidemiologic studies to evaluate prenatal medication safety, and pilot work with special focus on drug scans in pregnancy, we will implement a three-stage novel reverse translational framework to accelerate evidence generation that will use data-mining (“scans”) to identify new exposure- outcome associations, triage signals, and then formally evaluate top prioritized signals. To accomplish our goals, we will use our infrastructure developed for drug evaluations in pregnancy, including curated billing records from the NIH Collaboratory’s Distributed Research Network and the national Medicaid Information System, representing a broad cross-section of privately and publicly insured pregnant patients and their offspring. Our specific aims are: (Aim 1) To scan for associations between (1a) pregnancy loss and antecedent prenatal exposures on the individual drug, chemical and therapeutic class level; and (1b) the 50 most prevalent drugs in pregnancy with incomplete information on teratogenic risk and a broad selection of live birth adverse outcomes; and (1c) to prioritize signals via expert panel review. (Aim 2) To employ careful pharmacoepidemiologic designs to evaluate the two top prioritized signals involving (2a) pregnancy loss, and (2b) an adverse livebirth outcome. To control for confounding and measurement biases, these studies will employ previously validated measures, which are further enhanced via linkage to fetal death and birth certificate data for a cohort subsample to evaluate unmeasured confounding and conduct probabilistic sensitivity analyses on outcome and exposure misclassification. Big data apprOaches fOr Safe Therapeutics in Healthy Pregnancies (BOOST-HP) will offer an innovative advancement in evidence generation by evaluating numerous exposures and outcomes simultaneously. Our long-term goal is to build a reusable, scalable approach and infrastructure to accelerate evidence generation on the safety and effectiveness of medication use during pregnancy. By leveraging data-mining methodologies successfully deployed in public health surveillance along with infrastructure used by multiple federal government agencies, we will focus research efforts on novel, high-priority signals that pose the greatest risk to healthy pregnancies.