A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women

Introduction This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. Methods This prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical...

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Published inDiabetes therapy Vol. 14; no. 12; pp. 2143 - 2157
Main Authors Duo, Yanbei, Song, Shuoning, Qiao, Xiaolin, Zhang, Yuemei, Xu, Jiyu, Zhang, Jing, Peng, Zhenyao, Chen, Yan, Nie, Xiaorui, Sun, Qiujin, Yang, Xianchun, Wang, Ailing, Sun, Wei, Fu, Yong, Dong, Yingyue, Lu, Zechun, Yuan, Tao, Zhao, Weigang
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Published Cheshire Springer Healthcare 01.12.2023
Springer Nature B.V
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Abstract Introduction This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. Methods This prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples). Results The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer–Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797–0.853, P  < 0.001) and 0.784 (95% CI 0.750–0.818, P  < 0.001), respectively. The performance of our prediction model was superior to that of three other published models. Conclusion We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations. Trial Registration ClinicalTrials.gov identifier: NCT03246295.
AbstractList IntroductionThis study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.MethodsThis prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).ResultsThe prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer–Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797–0.853, P < 0.001) and 0.784 (95% CI 0.750–0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.ConclusionWe developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.Trial RegistrationClinicalTrials.gov identifier: NCT03246295.
This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. This prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples). The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models. We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations. ClinicalTrials.gov identifier: NCT03246295.
This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.INTRODUCTIONThis study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.This prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).METHODSThis prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.RESULTSThe prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.CONCLUSIONWe developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.ClinicalTrials.gov identifier: NCT03246295.TRIAL REGISTRATIONClinicalTrials.gov identifier: NCT03246295.
Introduction This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. Methods This prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples). Results The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer–Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797–0.853, P  < 0.001) and 0.784 (95% CI 0.750–0.818, P  < 0.001), respectively. The performance of our prediction model was superior to that of three other published models. Conclusion We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations. Trial Registration ClinicalTrials.gov identifier: NCT03246295.
Author Peng, Zhenyao
Sun, Qiujin
Yuan, Tao
Nie, Xiaorui
Lu, Zechun
Wang, Ailing
Dong, Yingyue
Chen, Yan
Zhao, Weigang
Qiao, Xiaolin
Yang, Xianchun
Sun, Wei
Zhang, Jing
Song, Shuoning
Duo, Yanbei
Xu, Jiyu
Zhang, Yuemei
Fu, Yong
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  givenname: Shuoning
  surname: Song
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  organization: Department of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College
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  givenname: Xiaolin
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  organization: Department of Obstetrics, Beijing Chaoyang District Maternal and Child Health Care Hospital
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  organization: Department of Obstetrics, Beijing Chaoyang District Maternal and Child Health Care Hospital
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  surname: Yuan
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  surname: Zhao
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  email: xiehezhaoweigang@163.com
  organization: Department of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College
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crossref_primary_10_2147_DMSO_S502043
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Issue 12
Keywords Gestational diabetes mellitus
Early pregnancy
Prediction model
Predictors
Language English
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PublicationSubtitle Research, treatment and education of diabetes and related disorders
PublicationTitle Diabetes therapy
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Snippet Introduction This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the...
This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first...
IntroductionThis study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the...
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StartPage 2143
SubjectTerms Cardiology
Diabetes
Endocrinology
Gestational diabetes
Internal Medicine
Medicine
Medicine & Public Health
NCT
NCT03246295
Original Research
Performance evaluation
Pregnancy
Regression analysis
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Title A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women
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