Department of Psychology, Hope College, 35 E 12th St, Office 1159, PO Box 9000, Holland, 49422, MI, USA.
BMC Pregnancy Childbirth. 2024 Sep 17;24(1):603. doi: 10.1186/s12884-024-06812-5.
Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models to predict markers of fetal growth and development-specifically, newborn birthweight and head circumference (HC).
We used a pre-registered archival data analytic approach. These data consisted of maternal and newborn characteristics of 594 maternal-infant dyads in the western U.S. Participants also completed a measure of emotion dysregulation. In total, there were 22 predictors of newborn HC and birthweight. We used regularized regression for predictor selection and linear prediction, followed by nonlinear models if linear models were overfit.
HC was predicted best with a linear model (ridge regression). Newborn sex (male), number of living children, and maternal BMI predicted a larger HC, whereas maternal preeclampsia, number of prior preterm births, and race/ethnicity (Latina) predicted a smaller HC. Birthweight was predicted best with a nonlinear model (support vector machine). Occupational prestige (a marker similar to socioeconomic status) predicted higher birthweight, maternal race/ethnicity (non-White and non-Latina) predicted lower birthweight, and the number of living children, prior preterm births, and difficulty with emotional clarity had nonlinear effects.
HC and birthweight were predicted by a variety of variables associated with prenatal stressful experiences, spanning medical, psychological, and social markers of health and stress. These findings may highlight the importance of viewing prenatal maternal health across multiple dimensions. Findings also suggest that assessing difficulties with emotional clarity during standard obstetric care (in the U.S.) may help identify risk for adverse newborn outcomes.
新生儿的成长受产前母亲经历的影响。这些经历包括孕妇的身体健康状况、既往妊娠经历、情绪调节以及社会决定的健康指标。我们使用一系列机器学习模型来预测胎儿生长和发育的指标，特别是新生儿的出生体重和头围（HC）。
我们采用了预先注册的档案数据分析方法。这些数据包括美国西部 594 对母婴特征，参与者还完成了情绪失调的测量。共有 22 个新生儿 HC 和出生体重的预测指标。我们使用正则化回归进行预测因子选择和线性预测，如果线性模型过拟合，则使用非线性模型。
HC 最佳预测模型是线性模型（岭回归）。新生儿性别（男）、活产子女数量和母亲 BMI 预测 HC 较大，而母亲子痫前期、既往早产次数和种族/民族（拉丁裔）预测 HC 较小。出生体重的最佳预测模型是非线性模型（支持向量机）。职业声望（类似于社会经济地位的指标）预测出生体重较高，母亲的种族/民族（非白人和非拉丁裔）预测出生体重较低，活产子女数量、既往早产次数和情绪清晰度困难具有非线性效应。
HC 和出生体重由与产前压力经历相关的各种变量预测，涵盖了健康和压力的医学、心理和社会指标。这些发现可能强调了从多个维度看待产前母亲健康的重要性。研究结果还表明，在美国，在标准产科护理中评估情绪清晰度的困难可能有助于识别不良新生儿结局的风险。
