开发第二产程开始时新生儿不良结局的实用预测模型。

IF 8.3 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mark A Clapp, Siguo Li, Kaitlyn E James, Emily S Reiff, Sarah E Little, Thomas H McCoy, Roy H Perlis, Anjali J Kaimal
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引用次数: 0

摘要

目的利用电子胎儿监护(EFM)解读数据和第二产程开始时已知的其他相关临床信息,建立新生儿不良结局预测模型:这是一项回顾性队列研究,研究对象为 2016 年 7 月至 2020 年 6 月期间在两家学术医疗中心分娩的产妇。研究对象包括单胎足月产(妊娠超过 37 周)、顶点前倾、非畸形胎儿、计划阴道分娩且达到第二产程开始阶段的产妇。主要结局是新生儿严重不良结局的综合。我们开发并比较了三种建模方法,利用与EFM数据(由床旁护士解释并输入电子健康记录的结构化数据字段)、产妇合并症和分娩特征相关的因素预测主要结局:传统逻辑回归、LASSO(最小绝对收缩和选择算子)和极梯度提升。对模型的区分度和校准进行了比较。为便于临床解释,将预测概率分为不同的风险组,并计算出每个风险组对新生儿不良结局的阳性预测值:结果:共纳入 22,454 名患者:结果:共纳入 22,454 例患者:14,820 例为训练集,7,634 例为测试集。3.2%的分娩发生了新生儿综合不良结局。在所比较的三种建模方法中,逻辑回归模型的区分度最高(0.690,95% CI,0.656-0.724),且校准良好。如果按风险分层(无增加风险、较高风险和最高风险),新生儿综合不良结局的发生率分别为 2.6% (95% CI, 2.3-3.1%)、6.7% (95% CI, 4.6-9.6%) 和 10.3% (95% CI, 7.6-13.8%)。与新生儿综合不良结局相关性最强的因素包括:胎粪的存在(调整后比值比 [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)以及之前的分娩次数(aOR 0.77,95% CI,0.60-0.99)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a Practical Prediction Model for Adverse Neonatal Outcomes at the Start of the Second Stage of Labor.

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).

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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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