FACTORS ASSOCIATED WITH INTRAVENTRICULAR HEMORRHAGE IN THE PRETERM NEONATE

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这项研究的目的是确定和描述可能影响 或者演示早产儿？S对有害刺激的反应，通过明年将开展的三个试点项目。初步拨款计划将在胎龄小于或等于32周的早产儿群体中确定与有害刺激相关的脑血流动力学和生命体征，作为开发脑室内出血预测模型的第一步。提出了三个具体的试点项目。首先，一项题为？与新生儿脑内脑血流量相关的测量问题？将为主要提案的工程师提供近红外光谱(NIRS)在新生儿中使用的数据。第二个项目，NICU的峰值和持续噪声水平？将记录一个可能是NICU中的混杂因素的环境变量。第三个项目将在项目一和项目二完成后进行，将是一个NICU的母婴因素与IVH发病率的关系。这个项目将是一个回顾图表，以提供可能对预测模型感兴趣的人口统计和护理变量。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The purpose of this research is to identify and describe factors that may impact or demonstrate the preterm infant?s response to noxious stimuli via three pilot projects to be undertaken within the next year. The primary grant proposal will identify cerebral blood flow dynamics and vital signs associated withnoxious stimuli in the preterm infant population of less than and equal to 32 weeks gestational age as a first step in developing a predictive model for intraventricular hemorrhage. Three specific pilot projects are proposed. First, a study entitled ?Measurement Issues Related to Cerebral Blood Flow in the Neonatal Brain? will serve to provide the engineers on the major proposal with data on the use of Near Infrared Spectroscopy (NIRS) in the neonate. The second project, ?Peak and Sustained Noise Levels in the NICU? will document an environmental variable that may be a confounder in the NICU. The third project, to take place after the completion of projects one and two, will be ?Correlation of Maternal and Infant factors with Incidence of IVH in one NICU?. This project will be a retrospective chart review to provide demographic and care variables that may be of interest in the predictive model.