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[期刊论文]

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.

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Author:

Lu Jiayu (Lu Jiayu.) | Zhang Song (Zhang Song.) | Jiang Hongqing (Jiang Hongqing.) | Unfold

Indexed by:

PubMed

Abstract:

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.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.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.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.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.

Keyword:

gestational diabetes mellitus Prediction model rest energy metabolism

Author Community:

  • [ 1 ] [Lu Jiayu]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 ] [Zhang Song]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
  • [ 3 ] [Jiang Hongqing]Haidian Maternal and Children Health Hospital, Beijing, 100080, China
  • [ 4 ] [Yang Lin]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
  • [ 5 ] [Hao Dongmei]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
  • [ 6 ] [Yang Yimin]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
  • [ 7 ] [Li Xuwen]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
  • [ 8 ] [Chen Aiqing]Beijing Yes Medical Devices Co. Ltd., Beijing, 100152, China
  • [ 9 ] [Shao Jing]Beijing Yes Medical Devices Co. Ltd., Beijing, 100152, China
  • [ 10 ] [Liu Xiaohong]Beijing Yes Medical Devices Co. Ltd., Beijing, 100152, China

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Source :

Technology and health care : official journal of the European Society for Engineering and Medicine

ISSN: 1878-7401

Year: 2021

Issue: S1

Volume: 29

Page: 57-63

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 4

30 Days PV: 0

Online/Total:165/5897002
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