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

新生儿缺氧缺血性脑病（HIE）是一种神经系统综合征， 将含氧血液输送到胎儿或新生儿的大脑。新生儿缺氧缺血性脑病的发生率为1-3/1000，可能导致死亡或 神经性残疾，如脑瘫。电子胎儿监护（EFM）是在20世纪70年代开发的， 评估胎儿氧合的充分性作为预防HIE的策略，现在是护理标准。但临床上 试验报告，EFM的使用并没有降低CP、围产期死亡或HIE的发生率，但与 剖腹产的急剧增加。目前使用的3类胎儿心率（FHR）分类 该系统基于简单的规则，设计为易于在床边应用，在预测HIE方面具有一定的实用性。 然而，构成绝大多数描记的II类FHR模式对HIE的预测性较差， 存在“不确定”风险。由于敏感性较低，III类模式在预测HIE方面的作用也有限。 目前迫切需要开发更好的客观方法来评估EFM，以识别更多的胎儿， 及时采取纠正措施。子宫收缩过快，或子宫收缩过频， 被认为是一种可预防的新生儿缺氧缺血性脑病的病因，但研究报告的结果相互矛盾。EFM研究表明， 由于无法访问和手动分析研究HIE所需的大型数据集而受到限制。我们现在 有能力分析数字EFM信号使用自动化方法来测量标准胎心率模式， 以及发现可能不容易被临床医生在床边检测到的示踪的新方面。 我们假设现代信号处理和机器学习技术可以创造高度预测性的 通过分析EFM描记的既定和新特征，结合人口统计学， 以及母亲和胎儿的相关临床信息。我们提出了一个基于人群的回顾性队列 2010- 2019年在Kaiser Permanente北方加州出生的350，000例妊娠≥ 36周婴儿的研究。我们 具体目标是：1）创建MAESTRA队列数据集，将EFM记录与HIE和新生儿 2010- 2019年在Kaiser Permanente北方CA出生的350，000例妊娠≥ 36周婴儿中的酸中毒; 2） 使用现代信号处理和机器学习技术，提取已建立的和新的FHR， 子宫收缩功能的EFM记录，并确定这些功能中的哪些是最重要的 当与母亲和胎儿的临床数据相结合时，可预测HIE和酸中毒;以及3）进行外部 通过将最终预测模型应用于历史数据集来进行验证。我们预计机器学习 技术结合新的胎心率和子宫收缩力模式随着时间的推移，以及产前和围产期 临床特征，将提高EFM数据的预测价值，这些数据已经作为一部分被收集起来， 常规护理。我们的研究结果将为未来的临床试验提供信息。这样一个前所未有的大规模多学科 这项研究将提高我们使用EFM数据预防新生儿脑损伤的能力， 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.