Department of Endocrinology, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Department of Endocrinology, First Hospital of Handan City, Handan, Hebei, China.
Front Endocrinol (Lausanne). 2022 Nov 17;13:1019234. doi: 10.3389/fendo.2022.1019234. eCollection 2022.
Fetal macrosomia is defined as a birth weight more than 4,000 g and is associated with maternal and fetal complications. This early metabolic disease may influence the entire life of the infant. Currently, macrosomia is predicted by using the estimated fetal weight (EFW). However, the EFW is inaccurate when the gestational week is gradually increasing. To assess precisely the risk of macrosomia, we developed a new predictive model to estimate the risk of macrosomia.
We continuously collected data on 655 subjects who attended regular antenatal visits and delivered at the Second Hospital of Hebei Medical University (Shijiazhuang, China) from November 2020 to September 2021. A total of 17 maternal features and 2 fetal ultrasonographic features were included at late-term pregnancy. The 655 subjects were divided into a model training set and an internal validation set. Then, 450 pregnant women were recruited from Handan Central Hospital (Handan, China) from November 2021 to March 2022 as the external validation set. The least absolute shrinkage and selection operator method was used to select the most appropriate predictive features and optimize them 10-fold cross-validation. The multivariate logistical regressions were used to build the predictive model. Receiver operating characteristic (ROC) curves, C-indices, and calibration plots were obtained to assess model discrimination and accuracy. The model's clinical utility was evaluated decision curve analysis (DCA).
Four predictors were finally included to develop this new model: prepregnancy obesity (prepregnancy body mass index ≥ 30 kg/m), hypertriglyceridemia, gestational diabetes mellitus, and fetal abdominal circumference. This model afforded moderate predictive power [area under the ROC curve 0.788 (95% confidence interval [CI] 0.736, 0.840) for the training set, 0.819 (95% CI 0.744,0.894) for the internal validation set, and 0.773 (95% CI 0.713,0.833) for the external validation set]. On DCA, the model evidenced a good fit with, and positive net benefits for, both the internal and external validation sets.
We developed a predictive model for macrosomia and performed external validation in other regions to further prove the discrimination and accuracy of this predictive model. This novel model will aid clinicians in easily identifying those at high risk of macrosomia and assist obstetricians to plan accordingly.
胎儿巨大儿是指出生体重超过 4000 克，与母婴并发症有关。这种早期的代谢疾病可能会影响婴儿的整个生命。目前，通过估计胎儿体重（EFW）来预测巨大儿。然而，当孕周逐渐增加时，EFW 并不准确。为了准确评估巨大儿的风险，我们开发了一种新的预测模型来估计巨大儿的风险。
我们连续收集了 2020 年 11 月至 2021 年 9 月在河北医科大学第二医院（石家庄）定期产前检查和分娩的 655 名受试者的数据。在晚期妊娠时共纳入 17 项母体特征和 2 项胎儿超声特征。655 名受试者分为模型训练集和内部验证集。然后，2021 年 11 月至 2022 年 3 月从邯郸市中心医院（邯郸）招募了 450 名孕妇作为外部验证集。使用最小绝对收缩和选择算子法选择最合适的预测特征并进行 10 倍交叉验证优化。使用多元逻辑回归建立预测模型。获得接收者操作特征（ROC）曲线、C 指数和校准图以评估模型的区分度和准确性。通过决策曲线分析（DCA）评估模型的临床实用性。
最终纳入 4 个预测因子来开发这个新模型：孕前肥胖（孕前体重指数≥30kg/m）、高甘油三酯血症、妊娠期糖尿病和胎儿腹围。该模型具有中等预测能力[训练集的 ROC 曲线下面积为 0.788（95%置信区间 [CI] 0.736，0.840），内部验证集为 0.819（95% CI 0.744，0.894），外部验证集为 0.773（95% CI 0.713，0.833）]。在 DCA 上，该模型在内部和外部验证集中均具有良好的拟合度和阳性净效益。
我们开发了一种巨大儿预测模型，并在其他地区进行了外部验证，以进一步证明该预测模型的区分度和准确性。这个新模型将帮助临床医生更容易地识别出那些巨大儿风险较高的患者，并帮助产科医生做出相应的计划。
