Gestational diabetes & diabetes in pregnancy
Establishment and validation of a prediction model for gestational diabetes
Authors' conclusion
A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD.
Study context
- Population
- Pregnant women; modelling set 1756 women from the Zunyi birth cohort, internal validation set 1234 women, external validation in the Wuhan cohort
- Comparison
- DLEFM vs demographic-lifestyle factor model (DLFM)
- Outcome
- Discrimination for GD (AUC, internal validation)
Methodological detailsinternal and external validation, lasso-logistic regression, nomogram
- Internal and external validation
- Lasso-logistic regression
- Nomogram
Performance
As reported in the abstract
DLEFM
0.827
AUC
DLFM
0.783
AUC
Further findings
Independent predictors of gestational diabetes (GD) (DLEFM regression)
- Family history of diabetes, OR
- 2.28 (1.05-4.71) (95% CI)
- History of GD, OR
- 4.22 (1.89-9.41) (95% CI)
- Overweight or obese before pregnancy, OR
- 1.71 (1.27-2.29) (95% CI)
- History of hypertension, OR
- 2.61 (1.41-4.72) (95% CI)
- Sedentary time (h/day), OR
- 1.16 (1.08-1.24) (95% CI)
- Monobenzyl phthalate, OR
- 1.95 (1.45-2.67) (95% CI)
- Q4 mono-ethyl phthalate concentration, OR
- 1.85 (1.26-2.73) (95% CI)
Original publication
Establishment and validation of a prediction model for gestational diabetes. Diabetes Obes Metab. Published 11 December 2023. doi:10.
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