Houri Ohad, Bercovich Or, Berezovsky Alexandra, Gruber Shir Danieli, Pardo Anat, Werthimer Avital, Walfisch Asnat, Hadar Eran
Helen Schneider Hospital for Women, Rabin Medical Center-Beilinson Hospital, Petach Tikva, Israel.
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Int J Gynaecol Obstet. 2025 Oct;171(1):317-325. doi: 10.1002/ijgo.70204. Epub 2025 May 14.
Pregnant women with a previous cesarean delivery (CD) may opt for a trial of labor after cesarean (TOLAC) or elective repeat cesarean delivery (ERCD). This study aimed to evaluate the success rate, and maternal, and perinatal outcomes of TOLAC and to develop a predictive decision-tree algorithm for successful TOLAC.
A retrospective study was conducted in a tertiary medical center and included all women with one prior CD who delivered between 2008 and 2019. Maternal and perinatal outcomes were compared between successful and failed TOLAC and ERCD groups. A decision-tree algorithm was constructed using the χ automatic interaction detection method.
Of 10 325 women with one prior CD (out of 103 542 deliveries), the rate of successful TOLAC, defined as vaginal birth after cesarean (VBAC), was 81.92%. Symptomatic uterine rupture occurred in 55 women (0.98%), with no cases of hysterectomy, or maternal or neonatal death. The decision tree identified key predictors of VBAC success, including maternal age, gestational age at delivery, and history of vaginal delivery. Women with a prior vaginal delivery had the highest likelihood of VBAC (90.3%). The overall accuracy of the model was 82.7%.
This study demonstrated a high rate of TOLAC attempts and VBAC success in a tertiary medical center with experienced staff and close monitoring. Uterine rupture was rare and not associated with severe maternal or neonatal morbidity or mortality. The decision-tree algorithm provides a practical tool to predict successful TOLAC, supporting individualized care and informed decision making.
有剖宫产史的孕妇可选择剖宫产术后试产（TOLAC）或择期再次剖宫产（ERCD）。本研究旨在评估TOLAC的成功率、母体及围产期结局，并开发一种预测TOLAC成功的决策树算法。
在一家三级医疗中心进行了一项回顾性研究，纳入了2008年至2019年间所有有一次既往剖宫产史且分娩的妇女。比较了成功和失败的TOLAC组与ERCD组的母体和围产期结局。使用χ自动交互检测方法构建决策树算法。
在103542例分娩中，有10325例有一次既往剖宫产史的妇女，剖宫产术后阴道分娩（VBAC）定义的TOLAC成功率为81.92%。55名妇女（0.98%）发生有症状的子宫破裂，无子宫切除术、母体或新生儿死亡病例。决策树确定了VBAC成功的关键预测因素，包括产妇年龄、分娩时的孕周和阴道分娩史。有既往阴道分娩史的妇女VBAC可能性最高（90.3%）。该模型的总体准确率为82.7%。
本研究表明，在有经验丰富的工作人员和密切监测的三级医疗中心，TOLAC尝试率和VBAC成功率较高。子宫破裂罕见，且与严重的母体或新生儿发病率或死亡率无关。决策树算法提供了一种预测TOLAC成功的实用工具，支持个体化护理和知情决策。
