Department of Internal Medicine "T," Sourasky Medical Center, and the Department of Obstetrics and Gynecology, Lis Maternity Hospital, Sourasky Medical Center, Tel Aviv, Israel.
Obstet Gynecol. 2019 May;133(5):857-866. doi: 10.1097/AOG.0000000000003196.
To design a clinically based predictive model for the likelihood of successful external cephalic version (ECV).
This single-center retrospective study was conducted from February 2016 to July 2018 and included all candidates for ECV between 36 and 41 weeks of gestation. Variables with a potential effect on ECV success were collected. These variables include: body mass index, amniotic fluid index, gestational age, parity, location of placenta, fetal trunk posture, time in breech presentation before the procedure and the ultrasonographically measured size of the amniotic fluid preceding the fetal presenting part (fore-bag). Variables' association with ECV success was evaluated using a multivariate logistic regression and a decision tree predicting ECV outcome was developed using 75% of the patients and validated on the remaining 25%.
Overall, 250 pregnant women were identified and opted for a trial of ECV by a single operator, with a success rate of 64.8%. Body mass index, size of fore-bag, and parity were independent determinants of the version success, whereas other variables had no statistically significant effect on the success rate. Our decision tree model divided the cohort into five subgroups according to various combinations of the three variables. When evaluated on the internal validation set, the C-Index of the tree was 0.933 (0.863-1) and the prediction accuracy was 91.9% (86.5%-97.3%).
A prediction model composed of three easily measurable variables enables accurate prediction of successful ECV at term. Fore-bag was identified as the most important discriminator. Our model holds in internal validation and it can be used to support patient counseling and decision making for ECV but should be externally validated.
设计一种基于临床的预测模型，以预测外倒转术（ECV）成功的可能性。
这是一项单中心回顾性研究，于 2016 年 2 月至 2018 年 7 月进行，纳入了所有 36 至 41 孕周行 ECV 的候选者。收集了对 ECV 成功有潜在影响的变量。这些变量包括：体重指数、羊水指数、孕龄、产次、胎盘位置、胎儿躯干姿势、在进行该操作前的臀位持续时间，以及在胎儿先露部（前袋）之前超声测量的羊水大小。使用多变量逻辑回归评估变量与 ECV 成功的相关性，并使用 75%的患者开发并在其余 25%的患者中验证预测 ECV 结果的决策树。
总体而言，有 250 名孕妇被识别并由一名医生选择进行 ECV 试验，成功率为 64.8%。体重指数、前袋大小和产次是版本成功的独立决定因素，而其他变量对成功率没有统计学意义。我们的决策树模型根据三个变量的各种组合将队列分为五个亚组。在内部验证集上评估时，树的 C-指数为 0.933（0.863-1），预测准确率为 91.9%（86.5%-97.3%）。
由三个易于测量的变量组成的预测模型能够准确预测足月时 ECV 的成功。前袋被确定为最重要的判别器。我们的模型在内部验证中成立，可以用于支持 ECV 的患者咨询和决策制定，但需要进行外部验证。
