The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel; Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
Am J Obstet Gynecol. 2020 Jun;222(6):613.e1-613.e12. doi: 10.1016/j.ajog.2019.12.267. Epub 2020 Jan 30.
Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery. Vaginal birth after cesarean delivery calculators have been developed in different populations; however, some limitations to their implementation into clinical practice have been described. Machine-learning methods enable investigation of large-scale datasets with input combinations that traditional statistical analysis tools have difficulty processing.
The aim of this study was to evaluate the feasibility of using machine-learning methods to predict a successful vaginal birth after cesarean delivery.
The electronic medical records of singleton, term labors during a 12-year period in a tertiary referral center were analyzed. With the use of gradient boosting, models that incorporated multiple maternal and fetal features were created to predict successful vaginal birth in parturients who undergo a trial of labor after cesarean delivery. One model was created to provide a personalized risk score for vaginal birth after cesarean delivery with the use of features that are available as early as the first antenatal visit; a second model was created that reassesses this score after features are added that are available only in proximity to delivery.
A cohort of 9888 parturients with 1 previous cesarean delivery was identified, of which 75.6% of parturients (n=7473) attempted a trial of labor, with a success rate of 88%. A machine-learning-based model to predict when vaginal delivery would be successful was developed. When features that are available at the first antenatal visit are used, the model showed a receiver operating characteristic curve with area under the curve of 0.745 (95% confidence interval, 0.728-0.762) that increased to 0.793 (95% confidence interval, 0.778-0.808) when features that are available in proximity to the delivery process were added. Additionally, for the later model, a risk stratification tool was built to allocate parturients into low-, medium-, and high-risk groups for failed trial of labor after cesarean delivery. The low- and medium-risk groups (42.4% and 25.6% of parturients, respectively) showed a success rate of 97.3% and 90.9%, respectively. The high-risk group (32.1%) had a vaginal delivery success rate of 73.3%. Application of the model to a cohort of parturients who elected a repeat cesarean delivery (n=2145) demonstrated that 31% of these parturients would have been allocated to the low- and medium-risk groups had a trial of labor been attempted.
Trial of labor after cesarean delivery is safe for most parturients. Success rates are high, even in a population with high rates of trial of labor after cesarean delivery. Application of a machine-learning algorithm to assign a personalized risk score for a successful vaginal birth after cesarean delivery may help in decision-making and contribute to a reduction in cesarean delivery rates. Parturient allocation to risk groups may help delivery process management.
全球范围内都在努力将剖宫产率降低到 12-15%。特别关注的是那些在剖宫产后尝试阴道分娩的产妇，以减少重复剖宫产的负担。当阴道分娩成功时，并发症发生率最低，而当计划外剖宫产时，并发症发生率最高，这强调了需要提前评估剖宫产后阴道分娩成功的可能性。不同人群已经开发了剖宫产后阴道分娩计算器，但在将其应用于临床实践方面存在一些限制。机器学习方法可以用于研究具有传统统计分析工具难以处理的输入组合的大规模数据集。
本研究旨在评估使用机器学习方法预测剖宫产后阴道分娩成功的可行性。
分析了 12 年内一家三级转诊中心的单胎足月分娩的电子病历。使用梯度提升，为剖宫产后尝试阴道分娩的产妇创建了包含多个母婴特征的模型，以预测阴道分娩成功。创建了一个模型，使用最早在第一次产前检查中可用的特征，为剖宫产后阴道分娩提供个性化风险评分；创建了第二个模型，在添加仅在接近分娩时可用的特征后重新评估此评分。
确定了 9888 名有 1 次剖宫产史的产妇队列，其中 75.6%（n=7473）的产妇尝试了阴道试产，成功率为 88%。开发了一种基于机器学习的预测阴道分娩成功的模型。当使用在第一次产前检查中可用的特征时，该模型的受试者工作特征曲线下面积为 0.745（95%置信区间，0.728-0.762），当添加在接近分娩过程中可用的特征时，该面积增加到 0.793（95%置信区间，0.778-0.808）。此外，对于后期模型，构建了一个风险分层工具，将产妇分为低风险、中风险和高风险组，以评估剖宫产后阴道试产失败的风险。低风险和中风险组（分别为产妇的 42.4%和 25.6%）的成功率分别为 97.3%和 90.9%。高风险组（32.1%）的阴道分娩成功率为 73.3%。将该模型应用于选择再次剖宫产的产妇队列（n=2145），结果表明，如果尝试阴道试产，其中 31%的产妇将被分配到低风险和中风险组。
剖宫产后阴道试产对大多数产妇是安全的。成功率很高，即使在剖宫产后阴道试产率较高的人群中也是如此。应用机器学习算法为剖宫产后阴道分娩成功分配个性化风险评分可能有助于决策，并有助于降低剖宫产率。将产妇分配到风险组可能有助于分娩过程管理。
