The Impact of Diagnostic Criteria for Gestational Diabetes Mellitus on Adverse Maternal Outcomes: A Systematic Review and Meta-Analysis.
Fahimeh Ramezani TehraniMarzieh Saei Ghare NazRazieh Bidhendi YarandiSamira Behboudi-GandevaniPublished in: Journal of clinical medicine (2021)
This systematic review and meta-analysis aimed to examine the impact of different gestational-diabetes (GDM) diagnostic-criteria on the risk of adverse-maternal-outcomes. The search process encompassed PubMed (Medline), Scopus, and Web of Science databases to retrieve original, population-based studies with the universal GDM screening approach, published in English language and with a focus on adverse-maternal-outcomes up to January 2020. According to GDM diagnostic criteria, the studies were classified into seven groups. A total of 49 population-based studies consisting of 1409018 pregnant women with GDM and 7,667,546 non-GDM counterparts were selected for data analysis and knowledge synthesis. Accordingly, the risk of adverse-maternal-outcomes including primary-cesarean, induction of labor, maternal-hemorrhage, and pregnancy-related-hypertension, overall, regardless of GDM diagnostic-criteria and in all diagnostic-criteria subgroups were significantly higher than non-GDM counterparts. However, in meta-regression, the increased risk was not influenced by the GDM diagnostic-classification and the magnitude of the risks among patients, using the IADPSG criteria-classification as the most strict-criteria, was similar to other criteria. In conclusion, a reduction in the diagnostic-threshold increased the prevalence of GDM, but the risk of adverse-maternal-outcome was not different among those women who were diagnosed through more or less intensive strategies. Our review findings can empower health-care-providers to select the most cost-effective approach for the screening of GDM among pregnant women.
Keyphrases
- pregnancy outcomes
- birth weight
- pregnant women
- healthcare
- data analysis
- public health
- deep learning
- blood pressure
- randomized controlled trial
- weight gain
- autism spectrum disorder
- body mass index
- type diabetes
- climate change
- case control
- artificial intelligence
- big data
- physical activity
- human health
- cervical cancer screening