Leveraging Data Science to Understand Outcomes for Mothers and Children Affected by Opioids

利用数据科学了解受阿片类药物影响的母亲和儿童的结果

• 批准号: 10309017
• 负责人: Stephen W Patrick
• 金额: $ 34.55万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10309017
• 关键词: Accountability; Address; Admission activity; Affect; Algorithms; Biological; Birth; Breast Feeding; Buprenorphine; Cessation of life; Child; Child Welfare; Childhood; Clinical; Code; Collaborations; Data; Data Linkages; Data Science; Databases; Diagnosis; Diagnostic; Drug Monitoring; Drug Prescriptions; Early Intervention; Educational workshop; Electronic Health Record; Emergency department visit; Enrollment; Exposure to; Fostering; Fright; Government; Hospital Costs; Hospitals; Incidence; Infant; Information Systems; Inpatients; Intervention; Knowledge; Length of Stay; Link; Literature; Maternal Mortality; Medicaid; Methadone; Methodology; Methods; Minority; Mothers; National Institute of Child Health and Human Development; Neonatal; Neonatal Abstinence Syndrome; Opioid; Outcome; Overdose; Personal Satisfaction; Pharmaceutical Preparations; Population; Postpartum Period; Postpartum Women; Precision therapeutics; Pregnancy; Pregnant Women; Premature Birth; Premature Infant; Research; Research Personnel; Risk; Services; Syndrome; Tennessee; Testing; Therapeutic; Visit; Withdrawal; Woman; adverse outcome; base; criminal behavior; data sharing; experience; high risk; holistic approach; improved; innovation; maternal opioid use; maternal outcome; multiple data sources; novel; opioid epidemic; opioid exposure; opioid use; opioid use disorder; overdose death; overdose risk; postnatal; precision medicine; prenatal; prescription opioid; programs; prospective; relapse risk; service intervention; service utilization; severe maternal morbidity

PROJECT SUMMARY / ABSTRACT- PROJECT 2 Over the past two decades, the number of women with opioid use disorder (OUD) during pregnancy and the number of infants born affected by opioids has increased substantially. However, there remain key knowledge gaps in our understanding of how neonatal and postpartum treatment for opioid use impacts the maternal- infant dyad. Much of the current research around infants exposed to opioids focuses on those diagnosed with Neonatal Opioid Withdrawal Syndrome (NOWS), potentially under-identifying opioid-exposed infants that don’t develop the syndrome but are at risk for adverse outcomes. Postpartum women with OUD are at risk for outcomes that are detrimental to both mother and infant including overdose, severe maternal morbidity, and death. While we know medications for OUD, including buprenorphine and methadone, can improve short-term outcomes for women, evidence of their impact on maternal outcomes and dyadic stability after delivery is limited. Furthermore, maternal and infant wellbeing is intertwined, yet our understanding of dyadic outcomes (e.g., retaining custody) is limited and research has primarily focused on separate mother and infant wellbeing. Improved understanding of dyadic outcomes has the potential to inform tailored therapeutics and precision medicine for opioid-affected dyads. To address these key knowledge gaps we will utilize data linkages and novel data science methodologies to 1) develop and validate electronic health record-based algorithms to identify opioid-exposed infants and their mothers, identify key covariates for dyad wellbeing, and link to state data systems; 2) test the hypothesis that dyads with prenatal maternal treatment for OUD compared to dyads with untreated maternal OUD have improved birth outcomes and post-natal service utilization of recommended services in the first six months postpartum; and 3) test the hypothesis that dyads with prenatal maternal treatment for OUD compared to dyads with untreated maternal OUD have improved dyadic stability at one- year postpartum and whether infant treatment modifies those outcomes. This proposal will fill critical knowledge gaps and create enduring, scalable data science methods that will foster innovation, data sharing, and research focused on maternal-infant dyads affected by the opioid crisis.

项目摘要 /摘要 - 项目2 在过去的二十年中，怀孕期间的阿片类药物使用障碍（OUD）的女性人数 受阿片类药物影响的婴儿人数大大增加。但是，仍然存在关键知识 在我们了解新生儿和产后治疗阿片类药物使用的差距中， 婴儿二元。当前暴露于阿片类药物的婴儿的许多研究都集中在被诊断的患者上 新生儿阿片类药物戒断综合征（NOWS），可能识别不明的阿片类药物暴露于未识别的婴儿 发展该综合征，但面临广告结果的风险。有OUD的产后妇女有危险 对母亲和婴儿有害的结果，包括过量，严重的孕产妇发病率和 死亡。虽然我们知道包括丁丙诺啡和Meadadone在内的OUD药物可以改善短期 妇女的结果，证明她们对孕产妇结果的影响和分娩后二元稳定性的证据是 有限的。此外，孕产妇和婴儿的健康是交织在一起的，但我们对二元成果的理解 （例如，保留监护权）是有限的，研究主要集中于单独的母亲和婴儿健康。 提高对二元成果的理解有可能告知量身定制的疗法和精度 阿片类药物影响二元的药物。为了解决这些关键知识差距，我们将使用数据链接，并 新的数据科学方法至1）开发和验证基于电子健康记录的算法 识别暴露于阿片类药物的婴儿及其母亲，确定二元福利的关键协变量，并链接到状态 数据系统； 2）检验以下假设，即与二元组相比 未经治疗的母亲OUD改善了推荐的出生结果和产后服务的利用 产后最初六个月的服务； 3）检验以产前母体二元组的假设 与未经治疗的母体OUD的二元组相比，OUD的处理能够提高二元稳定性。 产后和婴儿治疗是否会改变这些结果。该建议将填补关键 知识差距并创建持久的可扩展数据科学方法，以促进创新，数据共享， 研究的重点是受阿片类药物危机影响的产妇二元组。