建立逻辑回归模型,从孕产妇初次妊娠登记时的社会人口和产科病史预测自发性早产。

IF 2.8 2区 医学 Q1 OBSTETRICS & GYNECOLOGY
Brenda F Narice, Mariam Labib, Mengxiao Wang, Victoria Byrne, Joanna Shepherd, Z Q Lang, Dilly Oc Anumba
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引用次数: 0

摘要

背景:目前的自发性早产预测机器学习技术严重依赖于既往早产史和/或昂贵的技术,如胎儿纤维连接蛋白和宫颈长度超声测量,这对那些被认为是低风险和/或无法获得更昂贵筛查工具的人不利:我们旨在建立一个自发性早产的预测模型:我们开发了一个逻辑回归模型,该模型使用了七个特征变量,这些变量来自英国一家三级产科医院2018年和2020年的早产儿(n = 917)和匹配的足月儿(n = 100)队列中的产妇社会人口学和产科病史。在Python®(3.8版)中采用了三重交叉验证技术,使用最具预测性的因素对数据进行子集训练和测试。然后将模型性能与之前发布的预测算法进行了比较:结果:回顾性模型显示出良好的预测准确性,对自发性早产的 AUC 为 0.76(95% CI:0.71-0.83),基于七个变量的敏感性和特异性分别为 0.71(95% CI:0.66-0.76)和 0.78(95% CI:0.63-0.88):在进一步验证之前,我们的观察结果表明,将关键的孕产妇人口学特征纳入传统的数学模型中,对本地区孕妇的自发性早产具有很好的预测作用,而无需考虑宫颈长度和/或胎儿纤连蛋白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration.

Background: Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools.

Aims and objectives: We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history.

Methods: We developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) cohort in 2018 and 2020 at a tertiary obstetric unit in the UK. A three-fold cross-validation technique was applied with subsets for data training and testing in Python® (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms.

Results: The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71-0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66-0.76) and 0.78 (95% CI: 0.63-0.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity/obstetric history.

Conclusion: Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and/or fetal fibronectin.

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来源期刊
BMC Pregnancy and Childbirth
BMC Pregnancy and Childbirth OBSTETRICS & GYNECOLOGY-
CiteScore
4.90
自引率
6.50%
发文量
845
审稿时长
3-8 weeks
期刊介绍: BMC Pregnancy & Childbirth is an open access, peer-reviewed journal that considers articles on all aspects of pregnancy and childbirth. The journal welcomes submissions on the biomedical aspects of pregnancy, breastfeeding, labor, maternal health, maternity care, trends and sociological aspects of pregnancy and childbirth.
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