Developing and validating a predictive model of delivering large-forgestational-age infants among women with gestational diabetes mellitus

作     者：Yi-Tian Zhu Lan-Lan Xiang Ya-Jun Chen Tian-Ying Zhong Jun-Jun Wang Yu Zeng 

作者机构：Department of Clinical LaboratoryJinling Clinical Medical College of Nanjing Medical UniversityNanjing 210002Jiangsu ProvinceChina Department of Clinical LaboratoryWomen’s Hospital of Nanjing Medical UniversityNanjing Women and Children’s Healthcare HospitalNanjing 210003Jiangsu ProvinceChina 

出 版 物：《World Journal of Diabetes》 (世界糖尿病杂志（英文版）（电子版）)

年 卷 期：2024年第15卷第6期

页      面：1242-1253页

核心收录：

学科分类：1002[医学-临床医学] 100211[医学-妇产科学] 10[医学] 

基　　金：Supported by National Natural Science Foundation of China,No.81870546 Nanjing Medical Science and Technique Development Foundation,No.YKK23151 Science and Technology Development Foundation Item of Nanjing Medical University,No.NMUB20210117 

主　　题：Large-for-gestational-age Gestational diabetes mellitus Predictive model Nomogram Triglyceride-glucose index 

摘      要：BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy *** has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant ***,traditional methods for the diagnosis of LGA have ***,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA *** To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of *** The multivariable prediction model was developed by carrying out the following ***,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was***,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the *** final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value***,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve *** After using a multistep screening method,we establish a predictive *** risk factors for delivering an LGA infant were identified(P0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational *** nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 f