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Objective: To identify the risk factors associated with severe maternal morbidity among women with gestational diabetes using common data model Background: Severe maternal morbidity is an unintended, adverse outcome of the pregnancy or the process of labor and delivery that causes short and long-term consequences to women’s and infants’ health. The prevalence of severe maternal morbidity has been increasing, from 5 to 14 cases per every 1,000 births from 1994 to 2014, and is estimated to increase over time. Previous studies have shown an association between gestational diabetes and pregnancy complications including hypertension, preeclampsia, and preterm birth. We assessed the association of representative biomarkers with severe maternal morbidity among women with gestational diabetes. Methods: This cohort study used data collected from common data model database at a single tertiary center in Seoul, Korea during 2004-2019. All patients with indication of gestational diabetes were included in the study. Cases were all women who experienced severe maternal morbidity using the ICD-10 codes identified by the Centers for Disease Control and Prevention. We assessed associations between representative biomarkers and severe maternal morbidity, using t-test and multivariable logistic regression models. Results: Among 15,096 women who gave birth, the prevalence of gestational diabetes was 9.19% (n=1,388). Among those, 329 (23.7%) developed severe maternal morbidity during pregnancy. HbA1c, triglyceride, and fasting blood sugar were higher among women with severe maternal morbidity (p&amp;lt;0.05) and younger age showed association (p&amp;lt;0.01) with severe maternal morbidity. Conclusion: This study showed that gestational diabetes was highly associated with severe maternal morbidity. Blood glucose and lipid metabolism were shown to be associated factors with severe maternal morbidity among women with gestational diabetes.

SUN-641 Identifying Risk Factors Associated with Severe Maternal Morbidity Among Women with Gestational Diabetes Using Common Data Model