Gestational diabetes & diabetes in pregnancy
Exploring Relationships Between Maternal Characteristics, Continuous Glucose Monitoring Data, and Neonatal Hypoglycemia in Gestational Diabetes Pregnancies Using Probabilistic Modeling
Authors' conclusion
Bayesian network allowed to explore the complex interactions between variables in pregnancies affected by GDM. This framework will be extended with wider data sets to provide valuable insights for clinical decision-making able to mitigate maternal and neonatal risks.
Study context
- Population
- Women with gestational diabetes mellitus (GDM) in the STEADY SUGAR clinical trial; Continuous glucose monitoring (CGM)-derived features from the 90 days preceding delivery
- Participants
- 118 women
- Primary endpoint
- Interactions between maternal characteristics, CGM-derived features and neonatal outcomes, particularly NH
Methodological detailsprobabilistic modeling, Bayesian networks
- Probabilistic modeling
- Bayesian networks
Primary result
As reported in the abstract
Maternal hypertension and NH, OR
2.13
1.02, 4.46
Family history for diabetes and NH, OR
1.43
0.57, 3.57
Elevated maternal body mass index (BMI) and NH, OR (comparing lower vs higher BMI categories)
3.59
1.42, 9.08
Cesarean delivery and NH risk, OR
2.05
0.98, 4.28
Further findings
Hyperglycemia by ethnicity
- Higher hyperglycemia among Afro-American patients, OR
- 2.91 [1.19, 7.11]
CGM-derived metrics and neonatal outcomes
- CGM-derived metrics were associated with multiple neonatal outcomes
Original publication
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