Comparative Effectiveness of Interventions for Labor Induction

项目摘要/摘要 尽管美国每年有近100万名新生儿涉及引产，但在 用于诱导的方案和干预措施在提供者中存在。引产的时间更长，而且 与自发分娩相比，需要更多的医院资源。此外，所涉及的方法、路线和剂量 引产可能会导致不良的临床结局或患者的分娩体验。因此，它是 当务之急是确定最优的引产方法，将危害降至最低，并在 平衡运营成本。在此K01应用程序中，我们建议使用高级统计技术来 研究引产的干预措施和时机，重点关注利益、风险、患者偏好和资源 使用。该项目将受益于妇幼医院的多种数据来源，包括劳工、 分娩和复苏现场分娩日志、引产入院表格、药房声明、病人图表、 电子病历系统和医院出院数据。目标1将确定已知的关于 通过系统回顾和荟萃分析比较引产的有效性。在临床上 以妇幼医院为依托，建立完整的产程数据库 引产，将通过向产后患者发放关于其分娩情况的问卷进行补充 上岗体验。作为目标2的一部分进行的分析将探索以患者为中心的结果，并将 将现有知识应用于妇幼数据，使用数学模型整合干预措施 对不同类型的引产结局及治疗效果进行异质性分析。这个 目标3的方法论办法将包括决策分析和信息分析的价值，以确定 未来在其他种群中进行研究的最关键领域。在整个项目中，我们将参与 对引产感兴趣的利益相关者包括患者、医生、医院运营委员会、 保险提供者、卫生部门和政策制定者。建议研究的贡献载于 更好地综合现有研究，并进行包含多个 采取干预措施，平衡多重结果。在这项应用的每一个目标上，我们都提出了创新 将改变当前研究标准并随后影响临床实践规范的方法。这些 技术和方法不经常在同一研究的连续体中组合使用，也不是 广泛应用于产科。目标1的荟萃分析结果将导致对 迄今为止所研究的引产实践和结果。然后我们将使用这些结果来帮助告知 目标2中的分析。目标2中的数学建模的结果有可能影响政策 和产科实践建议。目标3中信息分析价值的结果将有所帮助 优先考虑未来引产的研究议程。我们将使用我们的分析结果来翻译 将成果付诸实践，并告知未来的研究需要。

Project Summary/ Abstract Despite nearly one million births involving labor induction annually in the United States, substantial variability in the protocols and interventions used for induction exists among providers. Induced labors are longer and require more hospital resources than spontaneous labors. Moreover, the approach, route, and dose involved in labor induction may result in adverse clinical outcomes or patient experience of labor. Therefore, it is imperative to determine optimal methods of labor induction that minimize harm and maximize benefits while balancing operational costs. In this K01 application, we propose to utilize advanced statistical techniques to study interventions and timing of labor induction, focusing on benefits, risks, patient preferences, and resource use. This project will benefit from multiple data sources at Women & Infants Hospital including the labor, delivery, and recovery floor delivery log, labor induction intake forms, pharmacy claims, patient charts, the electronic medical record system, and hospital discharge data. Aim 1 will establish what is known about the comparative effectiveness of labor induction through a systematic review and meta-analysis. Within the clinical setting at Women & Infants Hospital, we will establish a comprehensive database of deliveries involving labor induction which will be supplemented by a questionnaire administered to postpartum patients about their labor induction experience. Analyses conducted as part of Aim 2 will explore patient-centered outcomes and will apply existing knowledge to the Women & Infants data using mathematical modeling to integrate interventions for labor induction with different types of outcomes and analyzing treatment effect heterogeneity. The methodological approach for Aim 3 will include decision analyses and value of information analyses to identify the most critical areas for future research in other populations. Throughout the project, we will engage stakeholders that have interest in labor induction including patients, physicians, hospital operations committee, insurance providers, health departments, and policy makers. The contribution of the proposed research is in better synthesizing existing studies as well as conducting a prospective analysis that incorporates multiple interventions and balances multiple outcomes. In every aim of this application, we propose innovative approaches that will shift current research standards and subsequently influence clinical practice norms. These technologies and methods are not often used in combination in a continuum of the same study and are not widely used in obstetrics. The results of the meta-analysis in Aim 1 will lead to a comprehensive summary of labor induction practices and outcomes studied to-date. We will then use these results to help inform the analyses in Aim 2. The findings from the mathematical modeling in Aim 2 have the potential to influence policy and obstetric practice recommendations. The results from the value of information analysis in Aim 3 will help prioritize the future research agenda in labor induction. We will use the findings of our analyses to translate results into practice and inform future research needs.