International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Ministry of Education -Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ministry of Education -Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Am J Obstet Gynecol. 2023 May;228(5S):S1063-S1094. doi: 10.1016/j.ajog.2022.11.1299. Epub 2023 Mar 16.
The past 20 years witnessed an invigoration of research on labor progression and a change of thinking regarding normal labor. New evidence is emerging, and more advanced statistical methods are applied to labor progression analyses. Given the wide variations in the onset of active labor and the pattern of labor progression, there is an emerging consensus that the definition of abnormal labor may not be related to an idealized or average labor curve. Alternative approaches to guide labor management have been proposed; for example, using an upper limit of a distribution of labor duration to define abnormally slow labor. Nonetheless, the methods of labor assessment are still primitive and subject to error; more objective measures and more advanced instruments are needed to identify the onset of active labor, monitor labor progression, and define when labor duration is associated with maternal/child risk. Cervical dilation alone may be insufficient to define active labor, and incorporating more physical and biochemical measures may improve accuracy of diagnosing active labor onset and progression. Because the association between duration of labor and perinatal outcomes is rather complex and influenced by various underlying and iatrogenic conditions, future research must carefully explore how to integrate statistical cut-points with clinical outcomes to reach a practical definition of labor abnormalities. Finally, research regarding the complex labor process may benefit from new approaches, such as machine learning technologies and artificial intelligence to improve the predictability of successful vaginal delivery with normal perinatal outcomes.
在过去的 20 年中,人们对分娩进程的研究产生了浓厚的兴趣,对正常分娩的认识也发生了变化。新的证据不断涌现,更先进的统计方法也被应用于分娩进程分析。鉴于活跃分娩的起始和分娩进程的模式存在广泛的差异,人们越来越认为异常分娩的定义可能与理想化或平均分娩曲线无关。已经提出了替代方法来指导分娩管理;例如,使用分娩持续时间分布的上限来定义异常缓慢的分娩。尽管如此,分娩评估方法仍然很原始,容易出错;需要更客观的测量方法和更先进的仪器来识别活跃分娩的开始,监测分娩进程,并确定分娩持续时间与母婴风险相关。仅仅宫颈扩张可能不足以定义活跃分娩,并且纳入更多的物理和生化指标可能会提高诊断活跃分娩开始和进展的准确性。由于分娩持续时间与围产结局之间的关系相当复杂,并受到各种潜在和医源性因素的影响,未来的研究必须仔细探讨如何将统计截止值与临床结局相结合,以达到对分娩异常的实际定义。最后,关于复杂分娩过程的研究可能受益于新方法,如机器学习技术和人工智能,以提高具有正常围产结局的阴道分娩的可预测性。