Assessing the risk factors and establishing multivariable prediction models for singleton macrosomia - Luo, Jinying; Huang, Wenyan; Luo, Suping; Deng, Lin; Lin, Lihua; Liao, Qiuping; Yan, Jianying; Zhou, Jinfu (Frontiers in Medicine, 2025)
IntroductionFetal macrosomia is related to adverse neonatal and maternal health outcomes. Therefore, we aimed to evaluate the risk factors for macrosomia and establish multivariable prediction models to enable early identification, prevention, and mitigation of its adverse outcomes.MethodsThis retrospective case-control study included 800 singleton pregnant women who delivered in 2022 at Fujian Maternity and Child Health Hospital and Quanzhou Women and Children's Hospital. They were categorized into the macrosomia [birth weight (BW) ≥ 4,000 g, n = 400] and non-macrosomia (BW = 2,500–3,999 g, n = 400) groups according to the BW of the newborns. Prediction models in singleton fetuses during mid-to-late pregnancy and before delivery were constructed.ResultsMaternal height ≥ 165 cm [odds ratio (OR) = 2.303, 95% confidence interval (CI): 1.232–4.305], pre-pregnancy overweight (OR = 2.166, 95% CI: 1.119–4.195), pre-pregnancy obesity (OR = 3.189, 95% CI: 1.020–9.968), excessive gestational weight gain in the second trimester (OR = 2.083, 95% CI: 1.250–3.470), and at least two abnormal blood glucose values in the oral glucose tolerance test (OR = 5.267, 95% CI: 1.814–15.29) were identified as risk factors for macrosomia. Additionally, maternal abdominal circumference (AC) plus fundal length ≥ 140 cm (OR = 6.283, 95% CI: 3.976–9.927), fetal biparietal diameter ≥ 10 cm (OR = 3.373, 95% CI: 1.103–10.31), fetal head circumference ≥ 35 cm (OR = 3.473, 95% CI: 1.334–9.041), and fetal AC ≥ 36 cm at pre-delivery (OR = 23.46, 95% CI: 14.81–37.16) were risk factors for macrosomia.DiscussionThe construction of the macrosomia prediction model in singleton fetuses during mid-to-late pregnancy and before delivery showed a strong predictive value. This study identified key high-risk factors for macrosomia during the perinatal period. The macrosomia prediction model developed here is expected to enable early identification of macrosomia, allowing for timely interventions aimed at reducing the risk of adverse perinatal outcomes.