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 围产期妊娠预期结果的不确定性导致了几个主要的 挑战首先，临床医生可能不确定如何为家庭提供咨询。第二，缺乏明确性使 家庭更加焦虑，造成创伤。第三，无论是临床医生还是家属，都很难做到最好 为新生儿做出明智的决定。这一点很重要，因为当有 获得良好结局的机会非常小，可能导致无效的复苏尝试，从而导致死亡，或 可能是一个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 决定不复苏，并继续舒适护理时，有一个很好的生存机会，没有 残疾可能更悲惨。 我们将开发和测试一个现代化的，全面的预测模型的结果在perivivalent 使用现有的基础设施进行数据收集和实施，加州围产期质量 Care Collaborative（CPQCC）.这个以人口为基础的新生儿重症监护室网络包括 学术和社区单位，这意味着结果将是普遍的。CPQCC已经有一个 现有数据基础设施，包括孕产妇和新生儿数据，包括2岁时的随访数据， 提供了一个研究类似网络中不存在的结果的机会。CPQCC的设置允许 这是一个独特的机会，既可以改进当前的预测工具，又可以实施和评估 预测工具在现实世界的设置。 在目标1中，我们将使用最新的 数据可能使用广泛的基于人群的队列。该模型将用于建立一个在线估计器， 将被加州的20家医院使用。在目标2中，我们将评估目前的做法如何跨越~140 加州新生儿重症监护病房与Aim 1中建立的模型的预后估计一致。在这 目的是，我们将评估某些患者层面的因素和医院层面的因素是否超出了 与预后有关的典型实践规范，用于在出生前向母亲提供的治疗，以及 婴儿出生后。在目标3中，我们将在整个加州新生儿重症监护中实施估计器的使用 在一年半的时间里，每20家医院都有一批单位。然后，我们将比较实践是否以及如何改变 用于围产期婴儿。在目标4中，我们将对实施这一目标进行成本效益分析。 临床实践中的估计器。这项研究将填补我们在预测使用方面的知识空白 模型围产期出生，特别是差距，在我们的理解如何使用一个估计在实践中可能 影响和改善临床决策和结果。