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

• Main Authors: Sattamat Lappharat (Author), Penkae Rothmanee (Author), Kasemsak Jandee (Author), Manaphat Suksai (Author), Tippawan Liabsuetrakul (Author)
• Format: Book
• Published: Korean Society of Obstetrics and Gynecology, 2022-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2287-8572; 2287-8580; 10.5468/ogs.21250
• Keywords: diabetes, gestational; pregnancy trimester, first; nomograms; diagnostic screening programs; Gynecology and obstetrics; RG1-991
• Source: Obstetrics & Gynecology Science, Vol 65, Iss 2, Pp 156-165 (2022)

Objective 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. Methods A 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. Results Of the 553 pregnant women, 54 (9.8%) were diagnosed with early GDM. In the integrated model, there was a history of GDM (adjusted odds ratio [aOR], 5.15; 95% confidence interval [CI], 1.82-14.63; P=0.004), HbA1c threshold ≥5.3% (aOR, 2.61; 95% CI, 1.44-4.74; P=0.002), and family history of dyslipidemia (aOR, 2.68; 95% CI, 1.37-5.21; 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. Conclusion Application of the predictive model and nomogram will help healthcare providers investigate the probability of early GDM, especially in resource-limited countries.