- Diabetes, Obesity and Metabolism
Big Data Articles (National Health Insurance Service Database)
- Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
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Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim
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Endocrinol Metab. 2023;38(1):129-138. Published online January 27, 2023
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DOI: https://doi.org/10.3803/EnM.2022.1609
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 Abstract
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- Background
The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women.
Methods
A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM.
Results
A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions.
Conclusion
A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.
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Citations
Citations to this article as recorded by  - Establishment and validation of a nomogram to predict the neck contracture after skin grafting in burn patients: A multicentre cohort study
Rui Li, Yangyang Zheng, Xijuan Fan, Zilong Cao, Qiang Yue, Jincai Fan, Cheng Gan, Hu Jiao, Liqiang Liu
International Wound Journal.2023; 20(9): 3648. CrossRef - Predicting the Need for Insulin Treatment: A Risk-Based Approach to the Management of Women with Gestational Diabetes Mellitus
Anna S. Koefoed, H. David McIntyre, Kristen S. Gibbons, Charlotte W. Poulsen, Jens Fuglsang, Per G. Ovesen
Reproductive Medicine.2023; 4(3): 133. CrossRef - Prepregnancy Glucose Levels Within Normal Range and Its Impact on Obstetric Complications in Subsequent Pregnancy: A Population Cohort Study
Ho Yeon Kim, Ki Hoon Ahn, Geum Joon Cho, Soon-Cheol Hong, Min-Jeong Oh, Hai-Joong Kim
Journal of Korean Medical Science.2023;[Epub] CrossRef - Risk of Cause-Specific Mortality across Glucose Spectrum in Elderly People: A Nationwide Population-Based Cohort Study
Joonyub Lee, Hun-Sung Kim, Kee-Ho Song, Soon Jib Yoo, Kyungdo Han, Seung-Hwan Lee
Endocrinology and Metabolism.2023; 38(5): 525. CrossRef - The CHANGED Score—A New Tool for the Prediction of Insulin Dependency in Gestational Diabetes
Paul Rostin, Selina Balke, Dorota Sroka, Laura Fangmann, Petra Weid, Wolfgang Henrich, Josefine Theresia Königbauer
Journal of Clinical Medicine.2023; 12(22): 7169. CrossRef
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