The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
BJOG. 2021 Oct;128(11):1824-1832. doi: 10.1111/1471-0528.16700. Epub 2021 Apr 15.
To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour.
Retrospective Electronic-Medical-Record (EMR) -based study.
A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO.
A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high-risk and low-risk groups based on the Youden index to maximise sensitivity and specificity.
SANO was defined as either umbilical cord pH levels ≤7.1 or 1-minute or 5-minute Apgar score ≤7.
The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748-0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high-risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7-6.0; high-risk versus low-risk groups).
Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a 'time out' session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources.
Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.
建立预测分娩第二产程严重新生儿不良结局（SANO）的个体化机器学习模型。
基于回顾性电子病历（EMR）的研究。
纳入 73868 例单胎足月分娩且进入第二产程的产妇队列，其中 1346 例（1.8%）发生 SANO。
构建梯度提升模型，分析入院时采集的产前特征（如孕次和产次）和第一产程采集的产时数据（如宫颈扩张和消失程度）的 2100 万个数据点。根据约登指数将分娩分配到高危和低危组，以最大限度地提高敏感性和特异性。
SANO 定义为脐动脉 pH 值≤7.1 或 1 分钟和 5 分钟 Apgar 评分≤7。
SANO 预测模型的受试者工作特征曲线下面积为 0.761（95%CI 0.748-0.774）。三分之一的队列（33.5%，n=24721）被分配到 SANO 的高危组，该组捕获了高达 72.1%的病例（优势比 5.3，95%CI 4.7-6.0；高危组与低危组）。
使用机器学习模型可以从第一产程采集的数据中预测第二产程的 SANO。在第二产程开始时进行“暂停”，对产妇进行分层，可以针对分娩的这一具有挑战性的方面采取个性化的管理方法，并改善人员和资源的分配。
使用机器学习模型预测分娩中严重新生儿不良结局的个体化预测评分。
