Abstract
INTRODUCTION: Fetal macrosomia is a common and concerning complication of gestational diabetes mellitus (GDM), associated with increased risks for both maternal and neonatal morbidity. Traditional methods of predicting macrosomia often lack precision, particularly in diabetic pregnancies. This study aimed to evaluate the efficacy of three sonographic parameters, umbilical cord thickness (UCT), fetal fat layer (FFL), and interventricular septal thickness (IVS), as predictors of fetal macrosomia in women with GDM. MATERIALS AND METHODS: This prospective study included 123 pregnant women with GDM between 34 and 40 weeks of gestation. Comprehensive maternal data, including body mass index (BMI) and glycemic parameters (fasting blood sugar (FBS), postprandial blood sugar (PPBS), and glycated hemoglobin (HbA1c)), were recorded. Sonographic measurements of UCT, FFL, and IVS were performed and analyzed for their association with birth outcomes. Macrosomia was defined as a birth weight greater than 4000 g. RESULTS: Macrosomia occurred in 62.6% of pregnancies, with strong associations with maternal BMI (p<0.001) and HbA1c levels (p<0.001). Sonographic parameters showed significant correlations with birth weight: UCT (r=0.792, p<0.001), FFL (r=0.34, p<0.001), and IVS (r=0.295, p=0.001). A UCT ≥25 mm demonstrated excellent sensitivity (93.3%) and specificity (85.4%) for predicting macrosomia. FFL ≥4.5 mm showed high specificity (93.3%) and positive predictive value (PPV) (97.3%), while IVS ≥3.9 mm exhibited good specificity (85%) but lower sensitivity (71.8%). Despite the high prevalence of macrosomia, 88.6% of deliveries were uncomplicated, though the cesarean section rate was high (64.2%). CONCLUSION: Sonographic measurements of UCT, FFL, and IVS are valuable predictors of fetal macrosomia in GDM pregnancies. UCT, in particular, demonstrated the strongest correlation with birth weight and superior diagnostic accuracy. The integration of these sonographic parameters with maternal factors can enhance the accuracy of macrosomia prediction, potentially improving clinical decision-making and optimizing maternal and neonatal outcomes.