IC-CSRisk Study: Implementation of Calculated Cesarean Section Risk during labor induction, a multi-site stepped-wedge randomized rollout trial

IC-CSRisk 研究：引产期间计算剖腹产风险的实施，一项多站点阶梯式楔形随机推广试验

• 批准号: 10776163
• 负责人: Rebecca Feldman Hamm
• 金额: $ 191.89万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10776163
• 关键词: Affect; Biometry; Birth; Black race; Black, Indigenous, People of Color; Body mass index; Caring; Catalogs; Cervical; Cesarean section; Child Development; Child Health; Clinical; Clinical effectiveness; Color; Consolidated Framework for Implementation Research; Data; Decision Making; Discipline of obstetrics; Disparity; Epidemiology; Equation; Equity; Evidence based intervention; Evidence based practice; Foundations; Future; Geography; Gestational Age; Goals; Health; Height; Hemorrhage; Induced Labor; Intervention; Interview; Maps; Maternal Health; Maternal health equity; Methodology; Methods; Mission; Morbidity - disease rate; Outcome; Patients; Penetration; Persons; Population; Pregnancy Outcome; Prospective, cohort study; Race; Randomized; Reduce health disparities; Research; Research Design; Research Personnel; Risk; Risk Reduction; Site; Standardization; Surgical Wound Infection; Surveys; System; Testing; United States; Variant; Vision; Woman; Work; clinically significant; demographics; design; disparity reduction; effectiveness evaluation; effectiveness study; effectiveness/implementation hybrid; effectiveness/implementation trial; ethnic diversity; experience; explicit bias; falls; high risk; implementation determinants; implementation evaluation; implementation outcomes; implementation research; implementation science; implementation strategy; implicit bias; improved; innovation; interest; maternal morbidity; mortality; novel; obstetric outcomes; parity; personalized risk prediction; racial disparity; response; risk prediction; risk prediction model; success

PROJECT SUMMARY In the United States, over four million women give birth annually, with more than 20% undergoing labor induction. One-third of inductions end in cesarean delivery, which are associated with increased maternal morbidity. In the US, there are also significant, unacceptable disparities between Black, Indigenous, People of Color (BIPOC) and non-BIPOC birthing people in cesarean rates and maternal morbidity. Outside obstetrics, interventions that increase objectivity and decision-making standardization have been shown to limit the effects of bias on health outcomes. In response, our group created a risk prediction model for cesarean among those undergoing induction, which provides clinicians and patients with an individualized percentage risk of cesarean for a given patient’s labor induction. In a single-site prospective cohort study, use of this cesarean risk calculator was associated with substantial improvements in cesarean delivery rate and maternal morbidity. Through mixed-methods work, we elucidated the calculator’s mechanisms of success: plausibly assisting clinicians in objective decision-making, particularly for patients at very low or very high risk of cesarean, The central hypothesis of this proposal is that, with a foundation in implementation science, implementation of the cesarean risk calculator at diverse labor units across the US can have a profound impact on critical obstetric outcomes for all birthing people, with a particular focus on patients of color. This proposal leverages experienced investigators in maternal health equity (PI Hamm, Co-Is Levine, Howell, Clark, and Hussey), implementation science (PI Hamm, Co-I Lane-Fall, Delgado, Stephens), biostatistics/epidemiology of implementation (Co-I Stephens), and mixed methods (PI Hamm, Co-I Lane-Fall). We plan to test our hypothesis by studying the effectiveness of the cesarean risk calculator, while simultaneously collecting implementation data in a type I hybrid effectiveness-implementation, randomized stepped-wedge rollout design across 14 committed labor units. Before the rollout begins, Aim 1 will evaluate and catalogue contextual determinants at all included sites included in order to map implementation strategies and workflow plans most likely to result in successful local implementation. Aim 2 will then determine effectiveness of the cesarean risk calculator in the stepped wedge design, with planned stratified analysis by patient race to evaluate calculator impact on disparities. Finally, Aim 3 will determine calculator acceptability, penetration, and equitable reach in a mixed-methods approach. By the conclusion of this work, we will have novel, clinically important information on the impact of the cesarean risk calculator on labor outcomes, and an online toolkit for widespread implementation. The results of this proposed R01 will also produce data generalizable to many future large-scale implementation endeavors designed to improve maternal health.

项目摘要 在美国，每年有400多万妇女分娩，其中20%以上的妇女正在分娩 诱导三分之一的引产以剖腹产结束，这与产妇死亡率增加有关。 发病率在美国，黑人、土著人、少数民族和少数民族之间也存在着重大的、不可接受的差距。 彩色（BIPOC）和非BIPOC分娩的人在剖宫产率和产妇发病率。在产科之外， 增加客观性和决策标准化的干预措施已被证明限制了影响 对健康结果的偏见。作为回应，我们的研究小组创建了一个风险预测模型， 进行诱导，为临床医生和患者提供个性化的剖宫产风险百分比 对一个特定病人进行引产在一项单中心前瞻性队列研究中， 计算器与剖腹产率和产妇发病率的实质性改善有关。 通过混合方法的工作，我们阐明了计算器的成功机制： 临床医生在客观决策，特别是对病人在非常低或非常高的风险剖宫产， 这一建议的核心假设是，在实施科学的基础上， 在美国不同的分娩单位实施剖宫产风险计算器可能会产生深远的影响， 对所有分娩的人的关键产科结果，特别关注有色人种的患者。这项建议 利用在孕产妇健康公平方面经验丰富的调查人员（PI哈姆、Co-Is Levine、豪厄尔、克拉克和 Hussey）、实施科学（PI哈姆、Co-I Lane-Fall、Delgado、Stephens）、生物统计学/流行病学 实施（Co-I Stephens）和混合方法（PI哈姆，Co-I Lane-Fall）。 我们计划通过研究剖宫产风险计算器的有效性来验证我们的假设， 同时收集I型混合有效性-实现中的实现数据， 在14个承诺的劳动力单位中采用阶梯式楔形推出设计。在卷展栏开始之前，Aim 1将评估 并对所有包括在内的地点的背景决定因素进行编目，以绘制实施战略图 和工作流程计划，最有可能导致成功的本地实施。目标2将确定 阶梯楔形设计中剖宫产风险计算器的有效性，计划分层分析 患者竞赛，以评估计算器对差异的影响。最后，目标3将确定计算器的可接受性， 渗透，并在混合方法的公平覆盖。 通过这项工作的结论，我们将有新的，临床上重要的信息的影响， 剖腹产风险计算器的劳动成果，并广泛实施的在线工具包。的结果 这个建议的R 01还将产生可推广到未来许多大规模实施工作的数据 旨在改善产妇健康。