Improving outcomes of periviable births via an enhanced prediction tool

项目总结 围绕着危险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人提供咨询。其次，缺乏清晰度使 家庭更焦虑，会造成精神创伤。第三，临床医生和家庭都很难最大限度地 为新生儿做出明智的决定。这一点很重要，因为在以下情况下做出复苏的决定 良好结果的可能性非常低，可能导致徒劳的复苏尝试，导致死亡，或者 可能是有严重神经发育障碍的幸存者。另一方面，制造错误的信息 决定不复苏，在有很好的生存机会的情况下进行舒适护理 残疾可能会更悲惨。 我们将开发和测试一个现代的、全面的预测模型，以预测Evivive的结果 利用现有的数据收集和实施基础设施进行妊娠，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，这意味着结果将是可推广的。CPQCC已经有一个 现有数据基础设施，包括产妇和新生儿数据，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，既可以改进现有的预测工具，也可以实施和评估 真实世界环境中的预测工具。 在目标1中，我们将使用最新的数据建立一个预测妊娠结局的模型。 使用广泛的基于人口的队列的数据是可能的。这个模型将被用来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，我们将评估当前的做法如何跨越约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相一致。 目的，我们将评估某些患者水平因素和医院水平因素是否似乎不在 与预后有关的典型做法的规范，产前为母亲提供的治疗，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中实施估计器的使用 在一年半的时间里，每一家医院都有20家医院。然后，我们将比较实践是否以及如何发生变化 对于有危险的妊娠婴儿。在目标4中，我们将对实施这项措施进行成本效益分析。 临床实践中的估计者。这项研究将填补我们在使用预测知识方面的几个空白 危险出生的模型，特别是我们对如何在实践中使用估计器的理解上的差距 影响和改善临床决策和结果。

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.