A Systematic Review to Compare Adverse Pregnancy Outcomes in Women with Pregestational Diabetes and Gestational Diabetes.
Nompumelelo MalazaMatladi MaseteSumaiya AdamStephanie DiasThembeka NyawoCarmen PheifferPublished in: International journal of environmental research and public health (2022)
Pregestational type 1 (T1DM) and type 2 (T2DM) diabetes mellitus and gestational diabetes mellitus (GDM) are associated with increased rates of adverse maternal and neonatal outcomes. Adverse outcomes are more common in women with pregestational diabetes compared to GDM; although, conflicting results have been reported. This systematic review aims to summarise and synthesise studies that have compared adverse pregnancy outcomes in pregnancies complicated by pregestational diabetes and GDM. Three databases, Pubmed, EBSCOhost and Scopus were searched to identify studies that compared adverse outcomes in pregnancies complicated by pregestational T1DM and T2DM, and GDM. A total of 20 studies met the inclusion criteria and are included in this systematic review. Thirteen pregnancy outcomes including caesarean section, preterm birth, congenital anomalies, pre-eclampsia, neonatal hypoglycaemia, macrosomia, neonatal intensive care unit admission, stillbirth, Apgar score, large for gestational age, induction of labour, respiratory distress syndrome and miscarriages were compared. Findings from this review confirm that pregestational diabetes is associated with more frequent pregnancy complications than GDM. Taken together, this review highlights the risks posed by all types of maternal diabetes and the need to improve care and educate women on the importance of maintaining optimal glycaemic control to mitigate these risks.
Keyphrases
- pregnancy outcomes
- glycemic control
- type diabetes
- preterm birth
- pregnant women
- systematic review
- gestational age
- cardiovascular disease
- birth weight
- meta analyses
- weight loss
- low birth weight
- randomized controlled trial
- case control
- human health
- preterm infants
- machine learning
- palliative care
- adipose tissue
- big data
- quality improvement
- risk factors
- pain management
- respiratory tract
- drug induced
- deep learning