Maternal Antecedents and Electronic Fetal Monitoring in Term Asphyxia (MAESTRA)

新生儿缺氧缺血性脑病(HIE)是一种神经综合征，由脑血流减少引起 含氧血液进入胎儿或新生儿的大脑。每1,000名足月新生儿中有1-3人发生缺氧缺血性脑病，并可能导致死亡或 神经性残疾，如脑性瘫痪。电子胎儿监护仪是S在20世纪70年代发展起来的 评估胎儿氧合的充分性作为预防HIE的一种策略，现在是护理的标准。然而，临床上 试验报告说，使用EFM并没有降低CP、围产儿死亡或HIE的发生率，但与 剖腹产大幅增加。目前使用的三类胎儿心率(FHR)分类 该系统基于易于在床边应用的简单规则，在预测HIE方面有一定的实用价值。 然而，构成绝大多数痕迹的第二类FHR模式对HIE和 带来“不确定”的风险。由于敏感性低，III类模式在预测HIE方面的作用也有限。 迫切需要开发更好的客观方法来评估EFM，以识别更多的 HIE风险及时采取纠正措施。子宫收缩过快，或子宫收缩频率过高， 已经被认为是HIE的一个可预防的原因；然而研究报告了相互矛盾的结果。EFM研究已经 无法访问和手动分析研究HIE所需的大型数据集，这一点受到了限制。我们现在 有能力使用自动化方法分析数字EFM信号，以测量标准FHR模式 以及发现可能不容易被床边的临床医生检测到的描记的新方面。 我们假设，现代信号处理和机器学习技术可以创建高度可预测的 通过分析已有的和新的EFM轨迹特征，结合人口统计学，建立HIE模型 以及来自母亲和胎儿的相关临床信息。我们提出了一个基于人口的回溯性队列 2010-19年在北加州凯撒永久医院出生的350,000名怀孕36周的≥婴儿的研究。我们的 具体目标是：1)创建Maestra队列数据集，将EFM记录与HIE和新生儿联系起来 2010-19年在加州北部凯撒永久医院≥36周出生的350,000名婴儿酸中毒；2) 利用现代信号处理和机器学习技术，提取已有的和新的FHR和 从EFM记录的子宫收缩特征，并确定这些特征中哪些是最多的 结合母婴临床资料预测缺氧缺血性脑病和酸中毒；3)体外检查 通过将最终预测模型应用于历史数据集进行验证。我们期待着机器学习 结合新的胎心率和子宫收缩模式的技术随着时间的推移，以及产前和围产期 临床特征，将提高EFM数据的预测价值，这些数据已经作为部分收集 例行公事。我们的结果将为未来的临床试验提供参考。如此史无前例的大规模多学科 研究将提高我们使用EFM数据预防新生儿脑损伤的能力，同时将 不必要的剖腹产。

Neonatal hypoxic-ischemic encephalopathy (HIE) is a neurologic syndrome that results from reduced flow of oxygenated blood to the fetal or newborn brain. HIE occurs in 1-3 per 1,000 term births and may cause death or neurologic disabilities such as cerebral palsy. Electronic fetal monitoring (EFM) was developed in the 1970's to assess the adequacy of fetal oxygenation as a strategy to prevent HIE, and is now standard of care. Yet clinical trials report that EFM usage has not reduced the rate of CP, perinatal death or HIE, but is associated with a dramatic increase in cesarean deliveries. The currently used 3 Category fetal heart rate (FHR) classification system, based on simple rules designed to be easy to apply at the bedside, has some utility in predicting HIE. However, Category II FHR patterns that make up the vast majority of tracings are poorly predictive of HIE and confer “indeterminate” risk. Category III patterns are also of limited use in predicting HIE due to low sensitivity. There is an urgent need to develop better objective methods to assess EFM that would identify more fetuses at risk of HIE in time for corrective actions. Uterine tachysystole, or excessive frequency of uterine contractions, has been implicated as a preventable cause of HIE; yet studies report conflicting results. EFM research has been limited by an inability to access and manually analyze the large datasets needed to study HIE. We now have the ability to analyze digital EFM signals using automated methods to measure standard FHR patterns as well as to discover novel aspects of the tracing that may not be readily detectable by a clinician at the bedside. We hypothesize that modern signal processing and machine learning techniques can create highly predictive models of HIE by analyzing established and novel features of EFM tracings, in combination with demographic and pertinent clinical information from the mother and fetus. We propose a population-based retrospective cohort study of 350,000 infants born at ≥ 36 weeks gestation at Kaiser Permanente Northern California in 2010-19. Our specific aims are: 1) To create the MAESTRA Cohort dataset that links EFM recordings to HIE and neonatal acidosis among 350,000 infants born at ≥ 36 weeks gestation in 2010-19 at Kaiser Permanente Northern CA; 2) Using modern signal processing and machine learning techniques, to extract established and novel FHR and uterine contractility features from the EFM recordings, and to determine which of these features are most predictive of HIE and acidosis when combined with maternal and fetal clinical data; and 3) To perform external validation by applying the final predictive models to a historical dataset. We anticipate that machine learning techniques incorporating novel FHR and uterine contractility patterns over time, as well as pre- and perinatal clinical characteristics, will improve the predictive value of the EFM data that are already being collected as part of routine care. Our results will inform future clinical trials. Such an unprecedented large-scale multidisciplinary study will lead to improvements in our ability to use EFM data to prevent neonatal brain injury while minimizing unnecessary cesarean sections.