Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, New York University Langone Health, New York, NY, United States.
Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States.
J Med Internet Res. 2022 Jul 18;24(7):e34108. doi: 10.2196/34108.
Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States.
The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery.
Women aged 18 to 55 years delivering at a major academic center from July 2013 to October 2018 were included for analysis (N=30,867). A total of 497 variables were collected from the electronic medical record including the following: demographic information; obstetric, medical, surgical, and family history; vital signs; laboratory results; labor medication exposures; and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥1000 mL at the time of delivery, regardless of delivery method, with 2179 (7.1%) positive cases observed. Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (21,606/30,867, 70%) and validation (4630/30,867, 15%) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (4631/30,867, 15%) determined final performance by assessing for accuracy, area under the receiver operating curve (AUROC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus models limited to data available prior to the second stage of labor or at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery.
Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUROC 0.979, 95% CI 0.971-0.986 vs AUROC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination but lacked the sensitivity necessary for clinical applicability.
Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete data sets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery and to validate the findings of this study. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.
产后出血仍然是美国产妇发病率和死亡率的最大原因之一。
本文旨在使用机器学习技术来识别产科分娩时产后出血风险患者。
对 2013 年 7 月至 2018 年 10 月在一家主要学术中心分娩的 18 至 55 岁的妇女进行了分析（n=30867）。从电子病历中收集了 497 个变量，包括以下内容：人口统计学信息；产科、医疗、外科和家族史；生命体征；实验室结果；分娩药物暴露；和分娩结果。产后出血定义为分娩时出血量≥1000ml，无论分娩方式如何，观察到 2179 例（7.1%）阳性病例。使用回归、树和核机器学习方法进行有监督学习，基于训练（21606/30867，70%）和验证（4630/30867，15%）队列创建分类模型。使用特征选择算法和领域知识对模型进行调优。使用独立的测试队列（4631/30867，15%）通过评估准确性、接收器操作曲线下面积（AUROC）和产后出血正确分类的敏感性来确定最终性能。使用所有收集的数据和仅使用第二产程前或决定行剖宫产时的数据创建了单独的模型。还检查了按分娩方式的模型。
梯度提升决策树在整体模型中实现了最佳区分度。包括所有数据的模型略优于第二阶段模型（AUROC 0.979，95%CI 0.971-0.986 vs AUROC 0.955，95%CI 0.939-0.970）。最佳模型的准确性为 98.1%，阳性预测产后出血的敏感性为 0.763。第二阶段模型的准确率为 98.0%，敏感性为 0.737。其他选定的算法返回的模型区分度降低。按分娩方式分层的模型实现了良好到优秀的区分度，但缺乏临床应用所需的敏感性。
机器学习方法可用于识别产后出血风险妇女，她们可能受益于个体化预防措施。仅使用分娩前可用的数据的模型几乎与具有更完整数据集的模型一样有效，支持其在临床环境中的潜在效用。进一步的工作是必要的，以创建基于分娩方式的成功模型，并验证本研究的结果。基于无偏风险预测方法可能优于人为风险评估，是未来研究的一个领域。
