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

健康妊娠安全治疗的大数据方法 (BOOST-HP)

• 批准号: 10539666
• 负责人: Judith Maro
• 金额: $ 70.7万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-29 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10539666
• 关键词: Address; Algorithms; Animals; Attention; Benefits and Risks; Big Data; Birth Certificates; Centers for Disease Control and Prevention (U.S.); Chemicals; Clinical; Clinical Trials; Data; Data Engineering; Data Set; Death Certificates; Development; Drug Evaluation; Drug Exposure; Effectiveness; Enrollment; Evaluation; Exposure to; Federal Government; Fetal Death; Generations; Goals; Government Agencies; Healthcare; Individual; Infant; Information Systems; Infrastructure; Label; Link; Live Birth; Measurement; Measures; Medicaid; Medicine; Methodology; Methods; Mothers; Neonatal Intensive Care; Observational Study; Outcome; Pathway interactions; Patients; Perinatal; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacology; Pneumonia; Population; Population Surveillance; Pregnancy; Pregnancy loss; Provider; Public Health; Quality Control; Records; Registries; Research; Residual state; Risk; Safety; Scanning; Seizures; Sentinel; Signal Transduction; Site; Small for Gestational Age Infant; Source; Specialist; Spontaneous abortion; System; Techniques; Teratogens; Therapeutic; Time; Triage; United States National Institutes of Health; Vulnerable Populations; Work; adverse birth outcomes; adverse pregnancy outcome; black patient; cohort; collaboratory; data mining; data structure; design; drug use in pregnancy; experience; healthy pregnancy; human data; infant outcome; innovation; malformation; medication safety; novel; novel strategies; offspring; pregnant; prenatal; prenatal exposure; programs; safety study; stillbirth; translational framework; vaccine safety

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.

项目总结/摘要 在美国，孕妇平均使用4种药物，70%的孕妇至少使用一种药物。然而，大多数药物缺乏 关于怀孕期间安全性的结论性证据：在2010年至2019年期间批准的290个新FDA标签中， 90%的研究没有关于妊娠患者风险或获益的人类数据。根据现有证据 根据该系统，怀孕证据形成的平均时间估计为27年，这也是 久了目前的证据生成在很大程度上依赖于观察性研究，通常由来自 动物研究或从已知药理学途径推断，可能会错过妊娠特异性 上下文对查明脆弱亚群体中的因果机制也没有给予足够的重视， 最大的风险基于我们先前在FDA的哨兵系统和CDC的疫苗安全中的数据挖掘工作 数据链，开展药物流行病学研究以评估产前用药安全性， 特别关注怀孕期间的药物扫描，我们将实施一种三阶段的新型逆转录病毒， 加速证据生成的框架，将使用数据挖掘（“扫描”）来识别新的暴露- 结果关联，分类信号，然后正式评估最优先的信号。完成我们 目标，我们将使用我们的基础设施开发的药物评估在怀孕，包括策划计费 来自NIH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，代表了私营和公共保险的怀孕患者及其 后代我们的具体目标是：（目标1）扫描（1a）妊娠丢失与 个别药物、化学品和治疗类水平的产前暴露;以及（1b）50 妊娠期最流行的药物，致畸风险信息不完整， 出生不良后果;和（1c）通过专家小组审查优先考虑信号。(Aim（2）谨慎使用 药物流行病学设计，以评价两个最优先的信号，包括（2a）妊娠丢失，和 (2b)不利的活产结局。为了控制混杂和测量偏倚，这些研究将 采用先前验证的措施，通过与胎儿死亡和出生的联系进一步加强 队列子样本的证书数据，以评价未测量的混杂因素并进行概率 对结局和暴露错误分类的敏感性分析。大数据为安全治疗提供帮助 健康妊娠（BOOST-HP）将通过评估 同时进行多个暴露和结果。我们的长期目标是建立一个可重用的，可扩展的 加快药物安全性和有效性证据生成的方法和基础设施 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， 监控沿着多个联邦政府机构使用的基础设施，我们将重点研究 努力研究对健康怀孕构成最大风险的新的、高优先级的信号。