Gestational diabetes & diabetes in pregnancy
Development and validation of prediction models for gestational diabetes mellitus in first-trimester pregnant women
Authors' conclusion
Both the logistic regression and XGBoost models demonstrated satisfactory performance in the internal and temporal datasets. However, the XGBoost model showed more robust performance in the external validation dataset compared to the logistic regression model.
Study context
- Population
- Pregnant women in early pregnancy enrolled at Beijing Obstetrics and Gynecology Hospital (derivation and temporal datasets) and other subcenters (external validation)20,435 derivation
- Comparison
- Logistic regression vs XGBoost models
- Outcome
- Prediction of gestational diabetes mellitus (GDM) risk (AUC)
Methodological detailstemporal validation, external validation
- Temporal validation
- External validation
Performance
As reported in the abstract
AUC for GDM prediction · temporal validation
| Test or model | AUC |
|---|---|
| Logistic regression | 0.738 (95% CI: 0.707, 0.771) |
| XGBoost | 0.737 (95% CI: 0.706, 0.767) |
AUC
Logistic regression0.738 (95% CI: 0.707, 0.771)
XGBoost0.737 (95% CI: 0.706, 0.767)
Further findings
| Outcome | Logistic regression | XGBoost |
|---|---|---|
| AUC for GDM prediction, external validation | ||
| AUC | 0.674 (95% CI: 0.440, 0.879) | 0.737 (95% CI: 0.510, 0.929) |
Original publication
Development and validation of prediction models for gestational diabetes mellitus in first-trimester pregnant women. Diabetes Res Clin Pract. Published 16 December 2025. doi:10.
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