Zheng Yihan, Zhang Li, Wu Xizhu, Zhou Min
Department of Anesthesiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350001, Fujian, People's Republic of China.
J Pain Res. 2024 Jan 9;17:197-208. doi: 10.2147/JPR.S443338. eCollection 2024.
The conversion of epidural labor analgesia (ELA) to epidural surgical anesthesia (ESA) for intrapartum cesarean section (CS) often encounters failures. This study aimed to develop a nomogram for predicting the failure rate of this conversion.
A retrospective analysis was conducted on data from the Fujian Maternity and Child Health Hospital. Pregnant women (n=214) who underwent cesarean section after receiving labor analgesia. We performed correlation heat map and Lasso regression in terms of exclusion confounding factors and screening independent variables. A nomogram was developed to predict the occurrence.
The developed nomogram incorporated variables such as pregnant history, weight, premature rupture of membranes (PROM), dural puncture epidural (DPE), anesthesiologist level of cesarean section (ALOCS), and Anesthesiologist level of labor analgesia (ALOLA). The model demonstrated good predictive performance, providing a practical tool for assessing the risk of failure in converting labor analgesia to cesarean section anesthesia.
The nomogram can aid anesthesiologists in making informed decisions and optimizing patient care. By utilizing the nomogram, clinicians can estimate the probability of conversion failure based on individual patient characteristics and clinical factors.
分娩期剖宫产时,硬膜外分娩镇痛(ELA)转换为硬膜外手术麻醉(ESA)常遭遇失败。本研究旨在制定一种列线图,以预测这种转换的失败率。
对福建省妇幼保健院的数据进行回顾性分析。纳入接受分娩镇痛后行剖宫产的孕妇(n = 214)。我们在排除混杂因素和筛选独立变量方面进行了相关热图和套索回归分析。制定了一种列线图来预测这种情况的发生。
所制定的列线图纳入了诸如孕产史、体重、胎膜早破(PROM)、硬膜外穿刺(DPE)、剖宫产麻醉医生水平(ALOCS)以及分娩镇痛麻醉医生水平(ALOLA)等变量。该模型显示出良好的预测性能,为评估分娩镇痛转换为剖宫产麻醉的失败风险提供了一种实用工具。
该列线图有助于麻醉医生做出明智决策并优化患者护理。通过使用该列线图,临床医生可根据个体患者特征和临床因素估计转换失败的概率。