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

重新思考电子胎儿监护以改善围产期结局并减少阴道手术和剖腹产的频率

• 批准号: 10380847
• 负责人: Petar M Djuric
• 金额: $ 66.33万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-10 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10380847
• 关键词: Address; Adoption; Algorithms; Apgar Score; Architecture; Asphyxia; Bayesian Method; Biological Markers; Blood flow; Brain Hypoxia-Ischemia; Brain Injuries; Categories; Cerebral Palsy; Cesarean section; Classification; Clinical; Clinical Data; Complex; Computer software; Computers; Data; Data Analyses; Databases; Decision Making; Decision Support Systems; Development; Disabled Persons; Drops; Engineering; Evaluation; Fetal Death; Fetal Heart Rate; Fetal Monitoring; Fetus; Frequencies; Future; Gaussian model; Goals; Health; Heart Rate; Hypoxia; Infant; Intervention; Knowledge; Lead; Machine Learning; Medical Staff; Metabolic acidosis; Methodology; Methods; Monitor; Neonatal Mortality; Nervous System Trauma; Network-based; Neurological outcome; Newborn Infant; Outcome; Oxygen; Patients; Pattern; Performance; Personal Satisfaction; Physiological; Predictive Analytics; Predictive Value; Process; Records; Research; Role; Science; Scientist; Signal Transduction; Source; System; Technology; Testing; Time; Training; Umbilical cord structure; University Hospitals; Uterus; Vagina; Vaginal delivery procedure; Vision; Visual; Work; adverse outcome; antenatal; base; computerized; data anonymization; deep learning; fetal; fetal medicine; fetus at risk; fetus hypoxia; heart rate monitor; improved; intrapartum; ischemic injury; learning strategy; machine learning method; neonatal seizure; nervous system disorder; online repository; outcome prediction; perinatal outcomes; prevent; public repository; repository; signal processing; stem; stillbirth; theories

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.

电子胎儿监护（EFM）在分娩过程中的重要作用是防止不良后果， 胎儿缺氧和缺血其既定的弱点包括：1）产科医生的高度主观 信号模式的视觉解释和2）广泛使用未经证实的替代品， 相关的胎儿缺氧和/或缺血性损伤，如脐动脉pH，分娩期死胎， 新生儿Apgar评分和新生儿癫痫发作。这项技术在过去的50年里， 减少死胎或脑瘫婴儿的数量。目前的EFM 在临床环境中使用与手术使用的显著增加有关 阴道分娩和剖腹产。还没有开发出集成了 在产前和分娩期间收集的临床数据以及任何其他患者特定数据 EFM的结果。这项研究的主要目标是利用最近的突破， 机器学习推动预测分析的发展，以支持和改善 EFM数据的解释，特别是在真实的世界条件下，临床医生必须 及时做出干预决定，以防止不良后果。预计该 拟议的研究将导致显着减少使用手术阴道分娩和剖腹产 分娩，同时更准确地定义胎儿在发展代谢性酸中毒和长期 神经损伤