Liu Yi, Xiang Jie, Yan Ping, Liu Yuanqiong, Chen Peng, Song Yujia, Ren Jianhua
Department of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China.
Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, 610041, China.
BMC Pregnancy Childbirth. 2024 Dec 24;24(1):858. doi: 10.1186/s12884-024-07010-z.
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.
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.
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.
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.
母乳喂养是婴幼儿最佳的营养来源，对其健康成长和发育至关重要。然而，在跟踪产后六个月母乳喂养情况的队列研究中存在差距，这可能导致护理人员错过关键的干预机会。
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亲母乳喂养轨迹，并使用先进的机器学习算法为每个时期建立风险预测模型。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，数据通过自行填写的调查问卷和电子健康记录收集。
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、3个月和6个月时的母乳喂养率分别为100%、96%、93%和83%。相同时间段内的纯母乳喂养率分别为91%、64%、72%和58%。在所采用的五种机器学习方法中，随机森林（RF）在预测母乳喂养模式方面表现出更高的准确性，产后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；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出生体重以及母亲分娩前和产前体重。产后3个月和6个月喂养类型的预测因素包括早期喂养类型以及6个月时母乳喂养自我效能量表简版（BSES-SF）的得分。基于随访数据的预测模型表现良好。
从出院到产后6个月，母乳喂养率略有下降。该地区的母乳喂养情况在中国国内和国际上都相对乐观。新生儿出生体重、孕周、母亲怀孕前和怀孕期间的体重管理、早期支持与母乳喂养成功、母乳喂养知识和自我效能等因素与长期母乳喂养结果密切相关。本研究突出了早期母乳喂养阶段关键的、可改变的风险因素，为加强母乳喂养干预项目和明智决策提供了有价值的见解。
