Risk Prediction of Gestational Diabetes Mellitus In Women With Polycystic Ovary Syndrome Based On A Nomogram Model

Abstract Women with polycystic ovary syndrome are prone to develop gestational diabetes mellitus, a disease which may have significant impact on the postpartum health of both mother and infant. We performed a retrospective cohort study to develop and test a model that could predict gestational diabetes mellitus in the first trimester in women with polycystic ovary syndrome. Our study included 520 pregnant women who were referred to the obstetrics department between December 2017 and March 2020 with a diagnosis of polycystic ovary syndrome. Of these women, 171 were diagnosed with gestational diabetes mellitus in the second trimester. Univariate analysis revealed that in the first trimester, parity, family history of diabetes, age, body mass index (BMI), testosterone, low density lipoprotein cholesterol, triglyceride(TG), total cholesterol(TC), fasting plasma glucose(FPG), Hemoglobin A1c (HbA1C), diastolic blood pressure(DBP)，and insulin levels were predictive factors of gestational diabetes mellitus (P<0.05). Logistic analysis revealed that TG, age, HbA1C, Insulin, TC, BMI and family history of diabetes were independent risk factors for gestational diabetes mellitus. The area under the ROC curve of the gestational diabetes mellitus risk prediction model was 0.917 in this retrospective analysis, demonstrating the great ability to predict. The sensitivity and specificity of the prediction model were 0.814 and 0.871, respectively. The Hosmer–Lemeshow test also showed a good fit to the test.