Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.

IF 2.8 2区 医学 Q1 OBSTETRICS & GYNECOLOGY
Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren
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引用次数: 0

Abstract

Background: Breastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers to miss critical intervention opportunities.

Methods: This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. Conducted at a leading Maternal and Children's hospital in China from October 2021 to June 2022, data were gathered via self-administered surveys and electronic health records.

Results: Of the 3307 women recruited, 3175 completed the surveys, yielding a 96% effective response rate. Breastfeeding(BF) rates were observed at 100%, 96%,93% and 83% at discharge, 42 day, 3 month and 6 month postpartum, respectively. Exclusively breastfeeding(EBF) rates were recorded at 91%, 64%,72% and 58% for the same intervals. Among the five machine learning methods employed, Random Forest (RF) demonstrated superior accuracy in predicting breastfeeding patterns, with classification accuracies of 0.629 and an area under the receiver operating characteristic curve (AUC) of 0.8122 at 42 days, 0.925 and an AUC of 0.9800 at 3 months, and 0.836 and an AUC of 0.9463 at 6 months postpartum, respectively. Key predictive factors for breastfeeding at 42 days postpartum included the newborn's birth weight and the mother's pre-delivery and prenatal weights. Predictors for feeding type at 3 months and 6 months postpartum included early feeding types and the scores from the Breastfeeding Self-Efficacy Scale-short Form (BSES-SF) at 6 months. The predictive model based on follow-up data showed strong performance.

Conclusion: Breastfeeding rates slightly declined from discharge to 6 months postpartum. The breastfeeding context in this region is comparatively optimistic both within China and internationally. Factors such as newborn's birth weight, gestational age, maternal weight management before and during pregnancy, early support and breastfeeding success, breastfeeding knowledge and self-efficacy are intricately linked to long-term breastfeeding outcomes. This study highlights critical, modifiable risk factors for early breastfeeding stages, providing valuable insights for enhancing breastfeeding intervention programs and informed decision-making.

中国女性母乳喂养轨迹及基于机器学习的风险预测模型:一项队列研究
背景:母乳喂养是婴幼儿营养的最佳来源,对他们的健康成长和发育至关重要。然而,跟踪母乳喂养至产后6个月的队列研究的差距可能导致护理人员错过关键的干预机会。方法:本研究进行了三波前瞻性队列分析,以检查产后前六个月内的母乳喂养轨迹,并使用先进的机器学习算法建立每个时期的风险预测模型。该研究于2021年10月至2022年6月在中国一家领先的妇幼医院进行,通过自我管理的调查和电子健康记录收集数据。结果:在招募的3307名女性中,3175名完成了调查,有效回复率为96%。出院时、产后42天、产后3个月和产后6个月的母乳喂养率分别为100%、96%、93%和83%。在相同的时间间隔内,纯母乳喂养(EBF)率分别为91%、64%、72%和58%。在采用的5种机器学习方法中,随机森林(Random Forest, RF)预测母乳喂养模式的准确率较高,42天时分类准确率为0.629,受试者工作特征曲线下面积(AUC)为0.8122,3个月时分类准确率为0.925,AUC为0.9800,产后6个月时分类准确率为0.836,AUC为0.9463。产后42天母乳喂养的关键预测因素包括新生儿的出生体重、母亲的产前和产前体重。产后3个月和6个月喂养方式的预测因子包括早期喂养方式和6个月时母乳喂养自我效能量表-短表(BSES-SF)得分。基于随访数据的预测模型表现出较好的效果。结论:产后6个月母乳喂养率略有下降。无论在国内还是国际上,该地区的母乳喂养情况都比较乐观。新生儿出生体重、胎龄、孕前和孕期孕产妇体重管理、早期支持和母乳喂养成功、母乳喂养知识和自我效能感等因素与长期母乳喂养结果有着复杂的联系。这项研究强调了母乳喂养早期关键的、可改变的风险因素,为加强母乳喂养干预计划和知情决策提供了有价值的见解。
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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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