Nomogram-Based Prediction of the Risk of Macrosomia: A Prospective Cohort Study in a Chinese Population

Objective: This study aimed to establish a nomogram for predicting the risk of macrosomia in early pregnancy. Methods: We performed a prospective cohort study involving 1,549 pregnant women. According to the birth weight of newborn, the subjects were divided into two groups: macrosomia group and non-macrosomia group. Multivariate logistic regression was used to analyze the risk factors for macrosomia. Results: The prevalence of macrosomia was 6.13% (95/1549) in our hospital. Multivariate logistic regression analysis showed the risk factors of macrosomia were prepregnancy overweight (OR: 2.126, 95% CI: 1.181-3.826)/obesity (OR: 3.536, 95% CI: 1.555-8.036), multiparity (OR:1.877, 95% CI: 1.160-3.039), the history of macrosomia (OR: 36.971, 95% CI: 19.903-68.674), the history of GDM/DM (OR: 2.285, 95% CI: 1.314-3.976), the higher levels of HbA1c (OR: 1.763, 95% CI: 1.004-3.097) and TC (OR: 1.360, 95% CI: 1.004-1.842). A nomogram was developed for predicting macrosomia based on maternal factors related to the risk of macrosomia in early pregnancy. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.807 (95% CI: 0.755–0.859), the sensitivity and specificity of the model were 0.716 and 0.777, respectively. Conclusion: The nomogram model provides an accurate mothed for clinicians to early predict macrosomia.