UCD Perinatal Research Centre, School of Computer Science, University College Dublin, Dublin, Ireland.
UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland.
PLoS One. 2023 Feb 21;18(2):e0281821. doi: 10.1371/journal.pone.0281821. eCollection 2023.
A myriad of maternal and neonatal complications can result from delivery of a large-for-gestational-age (LGA) infant. LGA birth rates have increased in many countries since the late 20th century, partially due to a rise in maternal body mass index, which is associated with LGA risk. The objective of the current study was to develop LGA prediction models for women with overweight and obesity for the purpose of clinical decision support in a clinical setting. Maternal characteristics, serum biomarkers and fetal anatomy scan measurements for 465 pregnant women with overweight and obesity before and at approximately 21 weeks gestation were obtained from the PEARS (Pregnancy Exercise and Nutrition with smart phone application support) study data. Random forest, support vector machine, adaptive boosting and extreme gradient boosting algorithms were applied with synthetic minority over-sampling technique to develop probabilistic prediction models. Two models were developed for use in different settings: a clinical setting for white women (AUC-ROC of 0.75); and a clinical setting for women of all ethnicity and regions (AUC-ROC of 0.57). Maternal age, mid upper arm circumference, white cell count at the first antenatal visit, fetal biometry and gestational age at fetal anatomy scan were found to be important predictors of LGA. Pobal HP deprivation index and fetal biometry centiles, which are population-specific, are also important. Moreover, we explained our models with Local Interpretable Model-agnostic Explanations (LIME) to improve explainability, which was proven effective by case studies. Our explainable models can effectively predict the probability of an LGA birth for women with overweight and obesity, and are anticipated to be useful to support clinical decision-making and for the development of early pregnancy intervention strategies to reduce pregnancy complications related to LGA.
许多母婴并发症可能是由于分娩巨大儿（LGA）引起的。自 20 世纪后期以来，许多国家的 LGA 出生率有所增加，部分原因是母体体重指数上升，这与 LGA 风险有关。本研究的目的是为超重和肥胖的女性开发 LGA 预测模型，以便在临床环境中为临床决策提供支持。从 PEARS（怀孕运动和营养与智能手机应用程序支持）研究数据中获得了 465 名超重和肥胖孕妇在妊娠前和大约 21 周妊娠时的母体特征、血清生物标志物和胎儿解剖扫描测量值。应用随机森林、支持向量机、自适应增强和极端梯度增强算法，并结合合成少数过采样技术来开发概率预测模型。为不同的应用场景开发了两种模型：白人女性的临床应用场景（AUC-ROC 为 0.75）；以及所有种族和地区女性的临床应用场景（AUC-ROC 为 0.57）。发现母亲年龄、上臂中部周长、第一次产前检查时的白细胞计数、胎儿生物测量值和胎儿解剖扫描时的妊娠周数是 LGA 的重要预测因素。特定于人群的 Pobal HP 剥夺指数和胎儿生物测量百分位数也很重要。此外，我们使用局部可解释模型不可知解释（LIME）来解释我们的模型，案例研究证明这是有效的。我们的可解释模型可以有效地预测超重和肥胖女性发生 LGA 分娩的概率，预计对支持临床决策和制定早期妊娠干预策略以减少与 LGA 相关的妊娠并发症有用。
