지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.3
- 수록면
- 156 - 165 (10page)
- DOI
- https://doi.org/10.5468/ogs.21250
이용수
초록· 키워드
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ObjectiveTo develop a predictive model using the risk factors of gestational diabetes mellitus (GDM) and construct a predictivenomogram 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 andCoustan criteria after an abnormal 50 g glucose challenge test. The factors included in the model were ACOG riskfactors; 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. Thenomograms 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 historyof 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. Itsdiscrimination 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 earlyGDM, especially in resource-limited countries.
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