Pegorie Claire, Liu Becky, Thilaganathan Basky, Bhide Amar
Department of Obstetrics and Gynaecology, St George's University Hospitals NHS Foundation Trust, London SW17 0QT, United Kingdom.
Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London SW17 0QT, United Kingdom.
Matern Fetal Med. 2024 Jul 3;6(3):178-189. doi: 10.1097/FM9.0000000000000237. eCollection 2024 Jul.
Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG's limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG's role in enhancing fetal well-being assessments during pregnancy.
胎儿心率（FHR）监测是产科护理的核心内容之一。基于超声的技术，如胎心宫缩图（CTG），仍然是FHR监测最常用的方法。CTG存在局限性，包括主观解读、观察者间差异大以及需要专业技术人员，这促使了计算机化CTG（cCTG）的发展。虽然cCTG显示出优势，但其相对于视觉解读的优越性仍无定论。这促使人们探索非侵入性胎儿心电图（NIFECG）等替代方法。本综述探讨了产前FHR监测的现状以及在以患者为中心的护理模式中进行远程FHR监测的必要性。此外，FHR监测需要从传统方法发展，纳入人工智能和机器学习。该综述强调了使胎儿监测与现代医疗保健相匹配、利用人工智能算法进行准确评估以及增强患者参与度的重要性。解释了FHR变异性（FHRV）的生理机制，强调了其在评估胎儿健康方面的重要性。描述了FHRV的其他测量方法及其相关性。它深入探讨了NIFECG有前景的领域，详细介绍了其历史和近期技术进展。NIFECG的潜在优势包括客观的FHR评估、逐搏变异性、患者舒适度、远程长期使用以及随着孕妇体重指数增加信号损失减少。尽管有前景，但信号损失等挑战必须得到解决。讨论了NIFECG的临床应用、其与cCTG测量的相关性以及持续的技术进展。总之，本综述探讨了产前FHR监测的演变，强调了NIFECG在提供可靠的家庭式监测解决方案方面的潜力。概述了未来的研究方向，敦促进行纵向研究并生成证据，以确定NIFECG在加强孕期胎儿健康评估中的作用。
