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; 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，每年有超过四百万妇女分娩，超过20％的妇女正在劳动 就职。剖宫产的三分之一的归纳终止，这与母亲增加有关 发病率。在美国，黑人，土著人之间也存在重大，不可接受的差异 剖腹产和母体发病率的颜色（BIPOC）和非Bipoc生日的人。外部产科， 提高客观性和决策标准化的干预措施已显示出限制效果 对健康结果的偏见。作为回应，我们的小组为剖宫产创建了一个风险预测模型 接受诱导，为临床医生和患者提供剖宫产的个性百分比风险 对于给定患者的实验室归纳。在一项单位前瞻性队列研究中，使用这种剖宫产风险 计算器与剖宫产率和母体发病率的实质性改善有关。 通过混合方法的工作，我们阐明了计算器成功机制：合理协助 临床医生在客观决策中，特别是对于剖宫产风险非常低或非常高的患者， 该提议的核心假设是，在实施科学的基础上， 在美国各地的潜水实验室单位实施剖宫产风险计算器可能会产生深远的影响 关于所有生日的关键产科结果，特别关注有色人种。这个建议 杠杆经验丰富的孕产妇健康公平研究人员（Pi Hamm，Co-IS Levine，Howell，Clark和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宫产计算器的有效性来检验我们的假设，而 类似地，在I型混合有效性实施中收集实现数据，随机分组 跨14个致力于的劳动力部队的跨斜向外推出设计。在推出开始之前，AIM 1将评估 和目录上下文确定器在所有包含的网站上都包含以映射实施策略 工作流计划最有可能导致成功的本地实施。 AIM 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 评估计算器对差异的影响的患者竞赛。最后，AIM 3将确定计算器的可接受性， 在混合方法方法中的渗透和公平范围。 通过这项工作的结论，我们将获得有关临床上重要的有关影响的新颖，临床上重要的信息 剖宫产的劳动成果计算器和用于宽度实施的在线工具包。结果 该提出的R01还将产生可推广到许多未来大规模实施的数据 旨在改善母子健康。