Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean Deliveries

在分娩过程中，电子胎儿监护(EFM)的重要作用是防止可能导致的不良后果 胎儿缺氧和缺血。其公认的弱点包括：1)产科医生的高度主观性 信号模式的视觉解释以及2)未经证实的替代品的广泛使用 相关的胎儿缺氧和/或缺血性损伤，如脐动脉酸碱度、分娩时死产、 新生儿阿普加评分和新生儿癫痫发作。在过去的50年里，这项技术一直没有 显示可以减少死产或减少脑瘫婴儿的数量。目前的EFM 在临床环境中的使用与手术的使用的异常增加有关 阴道分娩和剖腹产。还没有开发出一种函数算法来集成 在产前和分娩期间收集的临床数据以及任何其他患者特有的数据 与有限元分析的结果相吻合。拟议研究的主要目标是利用最近在 机器学习推动预测分析的发展，以支持和改进 EFM数据的解释，特别是在真实世界条件下和临床医生必须 及时做出关于干预措施的决定，以防止不良后果。据预计， 拟议的研究将显著减少手术阴道分娩和剖腹产的使用 分娩，同时更准确地定义胎儿发生代谢性酸中毒的风险和长期 神经损伤。

The essential role of electronic fetal monitoring (EFM) during labor is to prevent adverse outcomes due to fetal hypoxia and ischemia. Its established weaknesses include: 1) the obstetrician’s highly subjective visual interpretations of the signal patterns and 2) the widespread use of unproven surrogates for relevant fetal hypoxic and/or ischemic injury such as umbilical arterial pH, intrapartum stillbirth, newborn Apgar scores and neonatal seizures. This technology over the past 50 years has not been shown to decrease stillbirths or reduce the numbers of infants with cerebral palsy. EFM as it is presently used in the clinical setting has been associated with an extraordinary increase in the use of operative vaginal delivery and cesarean delivery. No functional algorithm has yet been developed that integrates clinical data collected in the antepartum period and during labor and any other patient specific data with the results of EFM. The main objective of the proposed research is to use recent breakthroughs in machine learning to drive the development of predictive analytics to support and improve the interpretation of EFM data, especially under real world conditions and in real time where clinicians must make timely decisions about interventions to prevent adverse outcomes. It is anticipated that the proposed research will result in significantly decreased use of operative vaginal delivery and cesarean delivery while more precisely defining the fetus at risk for developing metabolic acidosis and long term neurologic injury.