Predicting the risk of fetal macrosomia at pregnancy in Shandong province: a case-control study.

Abstract

BACKGROUND AND AIM\nMacrosomia is used to describe an infant born with excessively high weight, and it brings lots of unexpected risks in clinical work. Macrosomia causes considerable challenges for both physicians and pregnant women. Our objectives were to identify factors in gravida to be associated with the risk of macrosomia, to guide clinical prevention and treatment.\n\n\nMETHODS\nThe study assessed risk factors of macrosomia by comparison with normal birth weight neonates, and a case-control study was conducted at Shandong Provincial Maternity and Child Healthcare Hospital. We followed and selected the relevant indicators of gravida who gave birth to macrosomia or normal infants, and applied statistical analysis to identify clinical indicators related to macrosomia.\n\n\nRESULTS\nMaternal blood glucose (OR 3.88 (1.07, 14.15)), history of abnormal conception (OR 18.44 (1.05, 322.89)), situation of menarche (OR 13.53 (1.28, 142.66)), and menstrual cycle of gravida (OR 13.24 (1.17, 150.24)) were significant influencing factors of macrosomia, but did not appear in the univariate analysis. Adding gestational age at delivery (OR 4.00 (1.45, 11.09)), triglyceride (OR 0.01 (<0.01, 0.40)), and MBI (OR 46.35 (2.08, >99.99)) of pregnant women, the area under the curve (AUC) curve was drawn for forecasting the risk of macrosomia, and the value of AUC was 0.9174. The triglyceride blood index of pregnant women was the only one that was inversely proportional to the probability of giving birth to macrosomic infants. The low-density lipoprotein (LDL) (OR 0.29 (0.12, 0.72)) and total cholesterol (OR 0.39 (0.20, 0.75)) were important factors in univariate analysis, and both of them were negative correlation factors of macrosomia. All influencing factors in multivariate analysis were selected for drawing a receiver operating characteristic (ROC) curve, and the value of the AUC was 0.9174.\n\n\nCONCLUSIONS\nThis analysis could therefore accurately predict the risk of pregnant women who would deliver macrosomic infants.