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.

项目概要 在美国，每年有超过 400 万妇女分娩，其中超过 20% 正在进行分娩 就职。三分之一的引产以剖腹产结束，这与产妇分娩率增加有关 发病率。在美国，黑人、原住民、黑人之间也存在着巨大的、不可接受的差异。 有色 (BIPOC) 和非 BIPOC 分娩人群的剖宫产率和孕产妇发病率。产科之外， 事实证明，提高客观性和决策标准化的干预措施可以限制影响 对健康结果的偏见。为此，我们小组创建了剖宫产风险预测模型 接受引产，为临床医生和患者提供个性化的剖宫产风险百分比 对于特定患者的引产。在一项单中心前瞻性队列研究中，利用剖宫产风险 计算器与剖宫产率和孕产妇发病率的显着改善相关。 通过混合方法的工作，我们阐明了计算器的成功机制：合理地协助 临床医生做出客观决策，特别是对于剖宫产风险极低或极高的患者， 该提案的中心假设是，以实施科学为基础， 在美国各地不同的劳动单位实施剖腹产风险计算器可以产生深远的影响 针对所有分娩者的关键产科结果，特别关注有色人种患者。这个提议 利用孕产妇健康公平方面经验丰富的研究人员（PI Hamm、Co-Is Levine、Howell、Clark 和 Hussey）、实施科学（PI Hamm、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 Hamm、Co-I Lane-Fall）。 我们计划通过研究剖腹产风险计算器的有效性来检验我们的假设，同时 同时收集 I 类混合有效性实施、随机的实施数据 跨 14 个承诺劳动单位的阶梯式楔形推出设计。在推出开始之前，目标 1 将评估 并对所有纳入地点的背景决定因素进行编目，以便制定实施战略 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 剖宫产风险计算器在阶梯楔形设计中的有效性，并通过计划进行分层分析 患者竞相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， 混合方法中的渗透率和公平覆盖范围。 通过这项工作的结论，我们将获得关于影响的新颖的、临床上重要的信息。 关于分娩结果的剖宫产风险计算器，以及用于广泛实施的在线工具包。结果 拟议的 R01 还将产生可推广到许多未来大规模实施工作的数据 旨在改善孕产妇健康。