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.

项目概要 围产期预期结果的不确定性导致了几个主要问题 挑战。首先，临床医生可能不确定如何向家人提供咨询。其次，缺乏明确性使得 家庭更加焦虑并造成创伤。第三，临床医生和家属都难以获得最大利益。 为新生儿做出明智的决定。这很重要，因为在出​​现以下情况时做出复苏的决定： 获得良好结果的机会非常小，可能会导致徒劳的复苏尝试，从而导致死亡，或者 可能是患有严重神经发育障碍的幸存者。另一方面，制造误导 当没有复苏机会且有很大生存机会时，决定不进行复苏并继续接受安慰护理 残疾可能会更加悲惨。 我们将开发并测试一个现代的、全面的预测模型，用于预测结果 使用现有基础设施进行数据收集和实施的妊娠，加州围产期质量 护理协作（CPQCC）。这个以人口为基础的新生儿重症监护病房网络包括 学术和社区单位，这意味着结果将具有普遍性。 CPQCC 已经拥有 现有的数据基础设施，包括孕产妇和新生儿数据，包括 2 岁时的随访数据， 提供研究类似网络中不存在的结果的机会。 CPQCC 的设置允许 获得一个独特的机会来改进当前的预测工具，并实施和评估 现实世界环境中的预测工具。 在目标 1 中，我们将使用最新的方法建立一个针对围产期妊娠结局的预测模型。 使用广泛的基于人群的队列可能获得数据。该模型将用于构建一个在线估计器 将被加州 20 家医院使用。在目标 2 中，我们将评估当前的实践如何跨越约 140 加州新生儿重症监护病房与目标 1 中建立的模型的预后估计一致。 目的是，我们将评估某些患者层面因素和医院层面因素是否出现在范围之外 与预后相关的典型实践规范，为母亲在出生前提供的治疗，以及 婴儿出生后。在目标 3 中，我们将在加州新生儿重症监护室实施估算器的使用 1.5 年期间，每波 20 家医院的单位。然后我们将比较实践是否以及如何改变 对于围产儿。在目标 4 中，我们将对实施该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 围产期模型，特别是我们对如何在实践中使用估计器可能产生的理解上的差距 影响和改善临床决策和结果。