A model for predicting gestational diabetes mellitus in early pregnancy: a prospective study in Thailand

Abstract

To develop a predictive model using the risk factors of gestational diabetes mellitus (GDM) and construct a predictive nomogram for GDM risk in women during early pregnancy. MethodsA prospective study was conducted in two tertiary hospitals among pregnant women with gestational age ≤14 weeks. Early GDM was diagnosed if an abnormal 100 g oral glucose tolerance test was detected using the Carpenter and Coustan criteria after an abnormal 50 g glucose challenge test. The factors included in the model were ACOG risk factors; maternal age; family history of hypertensive disorder in pregnancy; family history of dyslipidemia; gravida; parity; histories of preterm birth, early fetal death, abortion, stillbirth, and low birth weight; and glycated hemoglobin (HbA1c) levels. The predictive models for early GDM were analyzed using multiple logistic regression analyses. The nomograms were constructed, and their discrimination ability and predictive accuracy were tested. ResultsOf the 553 pregnant women, 54 (9.8%) were diagnosed with early GDM. In the integrated model, there was a history of GDM (aOR, 5.15; 95% confidence interval [CI], 1.82-14.63; P=0.004), HbA1c threshold ≥5.7% (aOR, 2.61; 95% CI, 1.44-4.74; P=0.002), and family history of dyslipidemia (aOR, 2.68; 95% CI, P=0.005). The integrated nomogram model showed that a history of GDM had a high impact on the risk of early GDM. Its discrimination and mean absolute error were 0.76 and 0.009, respectively. ConclusionApplication of the predictive model and nomogram will help healthcare providers investigate the probability of early GDM, especially in resource-limited countries.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

“…Other models for the early prediction of GDM have not been as effective as the predictive models introduced above [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

The Factor of Association of Diabetes Knowledge in Diabetes Mellitus type 2 patients

“…In Thailand, according to the Thailand Diabetes Registry (TDR) study (N=8,416, and the Diabetes care-Asia (Thailand) survey (N=1,078), severe complications are prevalent in both urban and rural areas. When urban and rural areas are compared, the prevalence of nephropathy (44% vs. 17%); retinopathy (31% vs. 13.6%), cardiovascular problems (8% vs. 0.7%), and stroke (4.4% vs. 1.9%) is higher in urban areas [13][14][15]. The specific prevalence of peripheral neuropathy is not reported but is estimated to be 34% in rural areas.…”