Development and validation of a new predictive model for macrosomia at late-term pregnancy: A prospective study - Wang, Yuhan; Liu, Hongzhou; Wang, Jincheng; Hu, Xiaodong; Wang, Anning; Nie, Zhimei; Xu, Huaijin; Li, Jiefei; Xin, Hong; Zhang, Jiamei; Zhang, Han; Wang, Yueheng; et al. (Frontiers in Endocrinology, 2022)
Objective: Fetal macrosomia is defined as 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, EFW is inaccurate when the gestational week gradually increasing. To assess precisely the risk of macrosomia, we developed a new predictive model to estimate the risk of macrosomia. Methods: 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. Seventeen maternal features and two 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) since November 2021 to March 2022 as the external validation set. The least absolute shrinkage and selection operator (LASSO) method were used to select most appropriate predictive features, and optimized them via ten-fold cross-validation. The multivariate logistical regressions were used to build the predictive model. Receiver operating characteristic curves (ROCs), C-indices, and calibration plots were obtained to assess model discrimination and accuracy. The model’s clinical utility was evaluated via decision curve analysis (DCA). Results: Four predictors were finally included to develop this new model: pre-pregnancy obesity (pre-pregnancy body mass index ≥ 30 kg/m2), hypertriglyceridemia, gestational diabetes mellitus (GDM), 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. Conclusions: We developed a predictive model for macrosomia and performed external validation in other region 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.