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Objectives <p>To develop a clinically applicable and promotable prediction model for assessing the risk of gestational diabetes mellitus (GDM) within the context of primary healthcare institutions.</p> Methods <p>The construction and the internal validation of the prediction model involved a cohort of 6,216 pregnant women observed from January 2019 to June 2019 in a Class A tertiary hospital in western China. External validation was subsequently conducted with 443 pregnant women from October 2020 to June 2021. Core characteristics were identified and the model was established using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation was performed using the Bootstrap method. Model evaluation included discrimination and calibration tests, decision curve analysis (DCA), and the clinical impact curve. Visualization of the model was achieved through a static nomogram and a risk-scoring model.</p> Results <p>The simplified prediction model possessed seven variables, including age, prepregnancy body mass index (BMI), polycystic ovary syndrome (PCOS), history of GDM, family history of diabetes, fasting plasma glucose (FPG), and urine glucose. This model exhibited a predictive accuracy, as reflected by a C-index of 0.736 (95% CI: 0.720 ~ 0.753) in the training set. The C-indexes were 0.735 and 0.694 in the internal and external testing set. Well-fitted calibration curves, the DCA curve, and the clinical impact curve demonstrated the feasibility of the simplified prediction model. For enhanced clinical application, the static nomogram and the risk-scoring model were employed to visualize the model.</p> Conclusions <p>This study developed a prediction model for assessing the risk of GDM among women from 8 to 14&#xa0;weeks of gestation in western China. The model demonstrated moderate discriminatory ability, well-fitted calibration, and convenient visualization, suggesting its suitability for implementation and widespread adoption, particularly within the context of primary healthcare institutions.</p>

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Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月2日

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  • Jiani Zhang,
  • Qi Cao,
  • Chihui Mao,
  • Jinfeng Xu,
  • Yaqian Li,
  • Yi Mu,
  • Guiqiong Huang,
  • Daijuan Chen,
  • Xixi Deng,
  • Tingting Xu,
  • Fan Zhou,
  • Xiaodong Wang

摘要

Objectives

To develop a clinically applicable and promotable prediction model for assessing the risk of gestational diabetes mellitus (GDM) within the context of primary healthcare institutions.

Methods

The construction and the internal validation of the prediction model involved a cohort of 6,216 pregnant women observed from January 2019 to June 2019 in a Class A tertiary hospital in western China. External validation was subsequently conducted with 443 pregnant women from October 2020 to June 2021. Core characteristics were identified and the model was established using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation was performed using the Bootstrap method. Model evaluation included discrimination and calibration tests, decision curve analysis (DCA), and the clinical impact curve. Visualization of the model was achieved through a static nomogram and a risk-scoring model.

Results

The simplified prediction model possessed seven variables, including age, prepregnancy body mass index (BMI), polycystic ovary syndrome (PCOS), history of GDM, family history of diabetes, fasting plasma glucose (FPG), and urine glucose. This model exhibited a predictive accuracy, as reflected by a C-index of 0.736 (95% CI: 0.720 ~ 0.753) in the training set. The C-indexes were 0.735 and 0.694 in the internal and external testing set. Well-fitted calibration curves, the DCA curve, and the clinical impact curve demonstrated the feasibility of the simplified prediction model. For enhanced clinical application, the static nomogram and the risk-scoring model were employed to visualize the model.

Conclusions

This study developed a prediction model for assessing the risk of GDM among women from 8 to 14 weeks of gestation in western China. The model demonstrated moderate discriminatory ability, well-fitted calibration, and convenient visualization, suggesting its suitability for implementation and widespread adoption, particularly within the context of primary healthcare institutions.