OBJECTIVE To develop a prediction model for adverse neonatal outcomes using electronic fetal monitoring (EFM) interpretation data and other relevant clinical information known at the start of the second stage of labor. METHODS This was a retrospective cohort study of individuals who labored and delivered at two academic medical centers between July 2016 and June 2020. Individuals were included if they had a singleton gestation at term (more than 37 weeks of gestation), a vertex-presenting, nonanomalous fetus, and planned vaginal delivery and reached the start of the second stage of labor. The primary outcome was a composite of severe adverse neonatal outcomes. We developed and compared three modeling approaches to predict the primary outcome using factors related to EFM data (as interpreted and entered in structured data fields in the electronic health record by the bedside nurse), maternal comorbidities, and labor characteristics: traditional logistic regression, LASSO (least absolute shrinkage and selection operator), and extreme gradient boosting. Model discrimination and calibration were compared. Predicted probabilities were stratified into risk groups to facilitate clinical interpretation, and positive predictive values for adverse neonatal outcomes were calculated for each. RESULTS A total of 22,454 patients were included: 14,820 in the training set and 7,634 in the test set. The composite adverse neonatal outcome occurred in 3.2% of deliveries. Of the three modeling methods compared, the logistic regression model had the highest discrimination (0.690, 95% CI, 0.656-0.724) and was well calibrated. When stratified into risk groups (no increased risk, higher risk, and highest risk), the rates of the composite adverse neonatal outcome were 2.6% (95% CI, 2.3-3.1%), 6.7% (95% CI, 4.6-9.6%), and 10.3% (95% CI, 7.6-13.8%), respectively. Factors with the strongest associations with the composite adverse neonatal outcome included the presence of meconium (adjusted odds ratio [aOR] 2.10, 95% CI, 1.68-2.62), fetal tachycardia within the 2 hours preceding the start of the second stage (aOR 1.94, 95% CI, 1.03-3.65), and number of prior deliveries (aOR 0.77, 95% CI, 0.60-0.99).

中文翻译：

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开发第二产程开始时新生儿不良结局的实用预测模型。


目的 利用电子胎儿监护 （EFM） 解释数据和第二产程开始时已知的其他相关临床信息，开发新生儿不良结局预测模型。方法 这是一项回顾性队列研究，研究对象为 2016 年 7 月至 2020 年 6 月期间在两个学术医疗中心分娩的个体。如果个体在足月时 （妊娠超过 37 周）、头端呈状、无异常的胎儿、计划阴道分娩并达到第二产程的开始，则将其纳入。主要结局是严重不良新生儿结局的复合结局。我们开发并比较了三种建模方法，使用与 EFM 数据相关的因素（由床边护士解释并输入到电子健康记录的结构化数据字段中）、孕产妇合并症和分娩特征：传统 logistic 回归、LASSO （最小绝对收缩和选择运算符）和极端梯度提升。比较模型鉴别和校准。将预测概率分层为风险组，以促进临床解释，并计算每个风险组对不良新生儿结局的阳性预测值。结果 共纳入 22,454 例患者： 训练集 14,820 例，测试集 7,634 例。复合不良新生儿结局发生于 3.2% 的分娩。在比较的 3 种建模方法中，logistic 回归模型的鉴别度最高 （0.690,95% CI，0.656-0.724） 并且校准良好。当分层为风险组（无增加风险、高风险和最高风险）时，复合不良新生儿结局的发生率为 2.6% （95% CI，2.3-3.1%），6。分别为 7% （95% CI， 4.6-9.6%） 和 10.3% （95% CI， 7.6-13.8%）。与复合不良新生儿结局相关性最强的因素包括胎粪的存在 （校正比值比 [aOR] 2.10,95% CI，1.68-2.62）、第二产程开始前 2 小时内的胎儿心动过速 （aOR 1.94,95% CI，1.03-3.65） 和既往分娩次数 （aOR 0.77,95% CI，0.60-0.99）。