Prediction Model of Large for Gestational Age Infants in Pregnant Women with Gestational Diabetes Mellitus
10.3969/j.issn.1003-6946.2025.10.010
- VernacularTitle:妊娠期糖尿病孕妇大于胎龄儿预测模型的构建
- Author:
Hongying ZHA
1
;
Shasha LI
;
Yumeng CUI
;
Lu SUN
;
Lin YU
;
Qingxin YUAN
Author Information
1. 南京医科大学附属无锡人民医院 南京医科大学无锡医学中心 无锡市人民医院内分泌科,江苏无锡 214000
- Publication Type:Journal Article
- Keywords:
Gestational diabetes mellitus;
Free triiodothyronine;
Large for gestational age;
Prediction model
- From:
Journal of Practical Obstetrics and Gynecology
2025;41(10):825-830
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.
