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Aims <p>Gestational diabetes mellitus is commonly observed in pregnant women and is associated with an increased risk of adverse outcomes for both mother and child, not only during pregnancy but also in the long term thereafter. The present study aimed to develop a predictive nomogram for gestational diabetes mellitus in pregnant women.</p> Materials and methods <p>This multicenter prospective cohort study enrolled 1,398 pregnant women from five major obstetric hospitals in Vietnam’s Mekong Delta. GDM was diagnosed based on the 2017 American Diabetes Association criteria. Using Bayesian Model Averaging, the optimal prediction model was identified in the primary cohort (<i>n</i> = 978) and used to construct a nomogram for individualized risk estimation. Model performance was validated in an independent cohort (<i>n</i> = 420), with assessment of discrimination (AUC), calibration (Brier score), and clinical utility (decision curve analysis).</p> Results <p>The prevalence of GDM was 18.0% (95%CI: 16.0-20.1). The final model included maternal age (OR per year: 1.09; 95%CI: 1.06–1.13), history of macrosomia (OR: 6.04; 95%CI: 2.76–13.19), body mass index (OR per kg/m²: 1.62; 95%CI: 1.25–2.10), and weight gain during pregnancy (OR per kg: 1.12; 95%CI: 1.06–1.18). The model demonstrated good discriminative ability and calibration in the primary cohort (AUC = 0.74, 95%CI: 0.69–0.78; Brier score = 0.123), and acceptable performance in the validation cohort (AUC = 0.70; 95%CI: 0.63–0.77; Brier score = 0.139). The nomogram showed good calibration and yielded higher net benefit across a wide range of risk thresholds (0.1–0.4) in decision curve analysis, indicating strong clinical utility.</p> Conclusions <p>A nomogram incorporating four routinely assessed clinical parameters offers good predictive accuracy for gestational diabetes mellitus. This model may facilitate early identification and targeted intervention for high-risk pregnant women in both resource-limited and clinical settings.</p>

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Development and multicenter validation of a novel model for selective screening of gestational diabetes mellitus: the Vietnam gestational diabetes mellitus study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Nga K. Tran,
  • Thanh N. Cao,
  • Linh V. Pham,
  • Tam D. Lam,
  • Trinh A. T. Vo,
  • Thu T. Nguyen,
  • Nghia N. Nguyen,
  • Dang H. Chau,
  • Bao T. Nguyen

摘要

Aims

Gestational diabetes mellitus is commonly observed in pregnant women and is associated with an increased risk of adverse outcomes for both mother and child, not only during pregnancy but also in the long term thereafter. The present study aimed to develop a predictive nomogram for gestational diabetes mellitus in pregnant women.

Materials and methods

This multicenter prospective cohort study enrolled 1,398 pregnant women from five major obstetric hospitals in Vietnam’s Mekong Delta. GDM was diagnosed based on the 2017 American Diabetes Association criteria. Using Bayesian Model Averaging, the optimal prediction model was identified in the primary cohort (n = 978) and used to construct a nomogram for individualized risk estimation. Model performance was validated in an independent cohort (n = 420), with assessment of discrimination (AUC), calibration (Brier score), and clinical utility (decision curve analysis).

Results

The prevalence of GDM was 18.0% (95%CI: 16.0-20.1). The final model included maternal age (OR per year: 1.09; 95%CI: 1.06–1.13), history of macrosomia (OR: 6.04; 95%CI: 2.76–13.19), body mass index (OR per kg/m²: 1.62; 95%CI: 1.25–2.10), and weight gain during pregnancy (OR per kg: 1.12; 95%CI: 1.06–1.18). The model demonstrated good discriminative ability and calibration in the primary cohort (AUC = 0.74, 95%CI: 0.69–0.78; Brier score = 0.123), and acceptable performance in the validation cohort (AUC = 0.70; 95%CI: 0.63–0.77; Brier score = 0.139). The nomogram showed good calibration and yielded higher net benefit across a wide range of risk thresholds (0.1–0.4) in decision curve analysis, indicating strong clinical utility.

Conclusions

A nomogram incorporating four routinely assessed clinical parameters offers good predictive accuracy for gestational diabetes mellitus. This model may facilitate early identification and targeted intervention for high-risk pregnant women in both resource-limited and clinical settings.