Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Issue 2 (April 2023)

• Record Type: Journal Article
• Authors: Huang, Qi-Fang; Hu, Yin-Chu; Wang, Chong-Kun; Huang, Jing; Shen, Mei-Di; Ren, Li-Hua
• Is Part Of: Biological research for nursing. Volume 25:Issue 2(2023)
• Page Start: 185
• Page End: 197
• Publication Date: 2023-04
• Subjects: gestational diabetes mellitus -- prediction model -- prognostic model -- systematic review -- meta-analysis; Clinical biochemistry -- Periodicals; Physiology, Pathological -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- Periodicals; 610.73
• DOI: 10.1177/10998004221131993
• Languages: English
• ISSNs: 1099-8004

Abstract

Background: Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. Methods: Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. Results: We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I 2 ranged from 0% to 97%). Conclusion: Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.