Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.
Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.
复制标题
应用自动化机器学习，利用临产患者的持续产时数据来预测分娩方式。
DOI：
10.1055/a-1885-1697
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发表时间：
2022
影响因子：
2
通讯作者：
Gregory,KimberlyD
中科院分区：
文献类型：
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作者：
Wong,MelissaS;Wells,Matthew;Zamanzadeh,Davina;Akre,Samir;Pevnick,JoshuaM;Bui,AlexAT;Gregory,KimberlyD
目的本研究旨在开发并验证一个机器学习（ML）模型，该模型利用分娩过程中从电子健康记录中迭代获得的数据来预测阴道分娩的概率（Partometer）。研究设计：2013年至2019年，在一家三级专科医院进行了一项回顾性队列研究，研究对象是至少进行过两次宫颈检查的产妇。人群分为由无产期单胎顶点（NTSV）剖宫产率<23.9%的医生分娩的人群（Partometer队列）和其余人群（control队列）。这类低风险患者的剖宫产率是比较提供者比率的标准指标；<23.9%为健康人口2020目标。一个有监督的自动化机器学习方法被应用于为每个群体生成一个模型。主要结果是Partometer队列中从入院到分娩4小时时模型的准确性。次要结果包括鉴别能力（受试者工作特征-曲线下面积[ROC-AUC]）、精密度-召回率AUC和Partometer的校准。为了评估通用性，我们将Partometer识别的性能和临床预测因素与对照模型进行了比较。结果研究期间共分娩37932例；排除后，9385例分娩被纳入Partometer队列，19683例分娩被纳入对照队列。Partometer预测4小时阴道分娩的准确率为87.1% （ROC-AUC: 0.82）。在堆叠的产时分娩期计模型中，最重要的临床预测指标包括入院模型预测和与对照人群中发现的相对应的扩张和站位的持续测量。结论与先前发表的基于逻辑回归的两种模型相比，使用自动ML和产时因素提高了阴道分娩概率预测的准确性。利用实时数据和机器学习可以为生成真正的规范性工具提供桥梁，以增强临床决策，预测分娩结果，并降低孕产妇和新生儿发病率。要点
ObjectiveThis study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.Study DesignA retrospective cohort study of deliveries at an academic, tertiary care hospital was conducted from 2013 to 2019 who had at least two cervical examinations. The population was divided into those delivered by physicians with nulliparous term singleton vertex (NTSV) cesarean delivery rates <23.9% (Partometer cohort) and the remainder (control cohort). The cesarean rate among this population of lower risk patients is a standard metric by which to compare provider rates; <23.9% was the Healthy People 2020 goal. A supervised automated ML approach was applied to generate a model for each population. The primary outcome was accuracy of the model developed on the Partometer cohort at 4 hours from admission to labor and delivery. Secondary outcomes included discrimination ability (receiver operating characteristics–area under the curve [ROC-AUC]), precision-recall AUC, and calibration of the Partometer. To assess generalizability, we compared the performance and clinical predictors identified by the Partometer to the control model.ResultsThere were 37,932 deliveries during the study period; after exclusions, 9,385 deliveries were included in the Partometer cohort and 19,683 in the control cohort. Accuracy of predicting vaginal delivery at 4 hours was 87.1% for the Partometer (ROC-AUC: 0.82). Clinical predictors of greatest importance in the stacked Intrapartum Partometer Model included the Admission Model prediction and ongoing measures of dilatation and station which mirrored those found in the control population.ConclusionUsing automated ML and intrapartum factors improved the accuracy of prediction of probability of a vaginal delivery over both previously published models based on logistic regression. Harnessing real-time data and ML could represent the bridge to generating a truly prescriptive tool to augment clinical decision-making, predict labor outcomes, and reduce maternal and neonatal morbidity.Key Points