Prediction models for maternal and offspring short- and long-term outcomes following gestational diabetes: a systematic review
File(s)
Author(s)
Type
Journal Article
Abstract
Objectives
Gestational diabetes mellitus (GDM), affecting one in seven pregnant women worldwide, can have short- and long-term adverse outcomes for both the mother and her baby. Despite a raft of prognostic models aiming to predict adverse GDM outcomes, very few have impacted clinical practice. This systematic review summarizes and critically evaluates prediction models for GDM outcomes, to identify promising models for further evaluation.
Methods
We searched EMBASE, MEDLINE, Web of Science, CINAHL, and CENTRAL for studies that reported the development or validation of predictive models for GDM outcomes in mother or offspring (PROSPERO: CRD42023396697).
Results
Sixty-four articles detailing 103 developed and 12 validated models were included in this review. Of these, 45% predicted long term, 31% birth, and 23% pregnancy outcomes. Most models (87%) had a high risk of bias, lacking sufficient outcome events, internal validation, or proper calibration. Only eight models were found at low risk of bias.
Conclusions
Our findings highlight a gap in rigorously developed prediction models for adverse GDM outcomes. There is a need to further validate existing models and evaluate their clinical utility to generate risk prediction tools capable of improving clinical decision-making for women with GDM and their children.
Gestational diabetes mellitus (GDM), affecting one in seven pregnant women worldwide, can have short- and long-term adverse outcomes for both the mother and her baby. Despite a raft of prognostic models aiming to predict adverse GDM outcomes, very few have impacted clinical practice. This systematic review summarizes and critically evaluates prediction models for GDM outcomes, to identify promising models for further evaluation.
Methods
We searched EMBASE, MEDLINE, Web of Science, CINAHL, and CENTRAL for studies that reported the development or validation of predictive models for GDM outcomes in mother or offspring (PROSPERO: CRD42023396697).
Results
Sixty-four articles detailing 103 developed and 12 validated models were included in this review. Of these, 45% predicted long term, 31% birth, and 23% pregnancy outcomes. Most models (87%) had a high risk of bias, lacking sufficient outcome events, internal validation, or proper calibration. Only eight models were found at low risk of bias.
Conclusions
Our findings highlight a gap in rigorously developed prediction models for adverse GDM outcomes. There is a need to further validate existing models and evaluate their clinical utility to generate risk prediction tools capable of improving clinical decision-making for women with GDM and their children.
Date Issued
2025-05-04
Date Acceptance
2025-03-26
Citation
Obesity Reviews, 2025
ISSN
1467-7881
Publisher
Wiley
Journal / Book Title
Obesity Reviews
Copyright Statement
© 2025 The Author(s). Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1111/obr.13934
Subjects
gestational diabetes
prediction models
prognosis modeling
Publication Status
Published online
Article Number
e13934
Date Publish Online
2025-05-04