Improving outcomes of periviable births via an enhanced prediction tool

通过增强的预测工具改善围产率结果

• 批准号: 10378480
• 负责人: Henry Chong Lee
• 金额: $ 33.39万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10378480
• 关键词: 2 year old; Address; Behavior; Birth; California; Caring; Cessation of life; Clinic; Clinical; Communities; Cost Effectiveness Analysis; Counseling; Data; Data Aggregation; Data Collection; Decision Making; Delivery Rooms; Family; Gestational Age; Hospitals; Individual; Infant; Infant Care; Institution; Knowledge; Lead; Length of Stay; Life; Link; Methods; Modeling; Modernization; Morbidity - disease rate; Mothers; National Institute of Child Health and Human Development; Neonatal; Neonatal Intensive Care Units; Network-based; Neurodevelopmental Disability; Neurodevelopmental Impairment; Neurologic; Outcome; Outcome Study; Palliative Care; Patients; Performance; Perinatal; Population; Pregnancy; Premature Birth; Prognosis; Quality of Care; Randomized; Research; Resources; Resuscitation; Source; Survivors; Testing; Time; Trauma; Uncertainty; Use Effectiveness; Variant; anxious; base; care outcomes; clinical care; clinical decision-making; clinical practice; cohort; collaborative care; cost; cost effectiveness; cost estimate; data infrastructure; demographics; disability; electronic data; falls; fetal; fetal diagnosis; follow-up; improved; improved outcome; innovation; knowledge base; mortality; neonate; periviable; population based; predict clinical outcome; predictive modeling; prognostic; respiratory; resuscitative care; risk prediction; risk prediction model; tool

PROJECT SUMMARY The uncertainty surrounding expected outcomes at periviable gestation leads to several major challenges. First, clinicians may be unsure of how to counsel families. Second, the lack of clarity makes families more anxious and causes trauma. Third, it is difficult for both clinicians and families to make the most informed decisions for the neonate. This is important because making a decision to resuscitate when there are very poor chances for a good outcome could lead to a futile attempt at resuscitation leading to death, or potentially a survivor that has severe neurodevelopmental disability. On the other hand, making a misinformed decision to not resuscitate and proceed to comfort care when there is a good chance of survival without disability could be even more tragic. We will develop and test a modern, comprehensive predictive model for outcomes at periviable gestation using an existing infrastructure for data collection and implementation, the California Perinatal Quality Care Collaborative (CPQCC). This population-based network of neonatal intensive care units includes both academic and community units, which means that results will be generalizable. CPQCC already has an existing data infrastructure that includes maternal and neonatal data, including follow-up data at 2 years of age, giving an opportunity to study outcomes that do not exist in similar networks. The setting of the CPQCC allows for a unique opportunity to both improve on current prediction tools, and to implement and evaluate the prediction tool in a real-world setting. In Aim 1, we will build a predictive model for outcomes in periviable gestation using the most up-to-date data possible using a broad population-based cohort. This model will be used to build an on-line estimator that will be used by 20 hospitals across California. In Aim 2, we will evaluate how current practice across ~140 California neonatal intensive care units align with prognostic estimates from the models built in Aim 1. In this Aim, we will evaluate whether certain patient level factors and hospital level factors appear to fall outside the norms of typical practice in relationship to prognosis, for therapies provided to the mother prior to birth, and the infant after birth. In Aim 3, we will implement usage of the estimator across California neonatal intensive care units in waves of 20 hospitals each over a 1 ½ year period. We will then compare if and how practices change for periviable gestation infants. In Aim 4, we will conduct a cost-effectiveness analysis of implementing this estimator in clinical practice. This research will fill several gaps in our knowledge of the use of prediction models for periviable birth, particularly the gap in our understanding of how using an estimator in practice may influence and improve clinical decisions and outcomes.

项目摘要 周围妊娠时预期结果的不确定性导致了几个主要 挑战。首先，临床医生可能不确定如何为家庭提供咨询。第二，缺乏清晰度 家庭更加焦虑，造成创伤。第三，临床医生和家庭都很难做到最大的 新生儿的明智决定。这很重要，因为在有 取得良好结果的机会很差可能导致徒劳的尝试导致死亡，或者 可能是患有严重神经发育障碍的幸存者。另一方面，使一个错误的信息 决定不恢复并在没有生存的可能性的情况下就继续舒适护理 残疾可能会更加悲惨。 我们将开发和测试一个现代，全面的预测模型，以实现Perivive的结果 使用现有基础架构进行数据收集和实施的妊娠，加利福尼亚围产期质量 护理协作（CPQCC）。这个基于人群的新生儿重症监护病房网络包括 学术和社区单位，这意味着结果将是可推广的。 CPQCC已经有一个 现有的数据基础架构包括材料和新生儿数据，包括2岁时的后续数据 有机会研究类似网络中不存在的结果。 CPQCC的设置允许 为了获得一个独特的机会，可以改善当前的预测工具，并实施和评估 现实世界中的预测工具。 在AIM 1中，我们将使用最新的妊娠期妊娠结果建立一个预测模型 使用广泛的基于人群的队列可能的数据。该模型将用于构建一个在线估计器 在加利福尼亚州的20家医院将使用。在AIM 2中，我们将评估当前的惯例如何140 加利福尼亚新生儿重症监护病房与AIM 1内置的模型的预后估算相一致。 目的，我们将评估某些患者级别的因素和医院级别的因素似乎不超出 与预后有关的典型实践规范，出生前向母亲提供的疗法以及 出生后婴儿。在AIM 3中，我们将在加利福尼亚新生儿重症监护室中实施估算器的使用情况 在1½年的时间内，各有20家医院的浪潮中的单位。然后，我们将比较实践是否以及如何改变 用于妊娠婴儿。在AIM 4中，我们将进行实施成本效益分析 临床实践中的估计量。这项研究将在我们了解预测的情况下填补几个空白 可养生的模型，尤其是我们了解如何在实践中使用估计量的差距 影响并改善临床决策和结果。