Gestational diabetes mellitus prediction model: A risk factors analysis of pregnant women with gestational diabetes mellitus but have normal oral glucose tolerance test results in the second trimester of pregnancy

Author:

Lu Jiayu1,Zhang Song1,Jiang Hongqing2,Yang Lin1,Hao Dongmei1,Yang Yimin1,Li Xuwen1,Chen Aiqing3,Shao Jing3,Liu Xiaohong3

Affiliation:

1. Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, 100124, China

2. Haidian Maternal and Children Health Hospital, Beijing, 100080, China

3. Beijing Yes Medical Devices Co. Ltd., Beijing, 100152, China

Abstract

BACKGROUND: Oral glucose tolerance test (OGTT) is a standard for the diagnosis of gestational diabetes mellitus (GDM). However, clinically, some cases with normal results were diagnosed as GDM in the third trimester. OBJECTIVE: To establish a risk model based on energy metabolism, epidemiology, and biochemistry that could predict the GDM pregnant women with normal OGTT results in the second trimester. METHODS: Qualitative and quantitative data were analyzed to find out the risk factors, and the binary logistic backward LR regression was used to establish the prediction model of each factor and comprehensive factor, respectively. RESULTS: The risk factors including the rest energy expenditure per kilogram of body weight, oxygen consumption per kilogram of body weight, if more than the weight gain criteria of the Institute of Medicine, the increase of body mass index between the second trimester and pre-pregnancy, and fasting blood glucose. By comparison, the comprehensive model had the best prediction performance, indicating that 85% of high-risk individuals were correctly classified. CONCLUSION: Energy metabolism, epidemiology, and biochemistry had better recognition ability for the GDM pregnant women with normal OGTT results in the second trimester. The addition of metabolic factors in the second trimester also improved the overall prediction performance.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

Reference15 articles.

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