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Risk prediction models for postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a scoping review

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Abstract

Objective

The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with gestational diabetes mellitus (GDM). The aim was to provide valuable insights for healthcare professionals in the development of robust risk prediction models.

Methods

A comprehensive literature search was conducted across multiple databases, including PubMed, EBSCO, Web of Science Core Collection, Ovid Full-Text Medical Journal Database, ProQuest, Elsevier ClinicalKey, China National Knowledge Infrastructure, China Biology Medicine, and WanFang Database, spanning from January 1990 to July 2023. To assess the quality of the included models, the Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed.

Results

Fourteen relevant studies were identified and included in the final review, all focusing on model development. The discrimination ability of the included models ranged from 0.725 to 0.940, indicating satisfactory prediction accuracy. However, a notable limitation was that nine of these models (64.3%) did not provide clear guidelines on the selection of potential predictors. Furthermore, only six models (42.86%) underwent internal validation, with none undergoing external validation. A high risk of bias was observed across the included models. Logistic regression, Cox regression, and machine learning were the primary methods employed in the construction of these models.

Conclusion

The risk prediction models included in this review demonstrated favorable prediction accuracy. However, due to variations in construction methodologies, direct comparison of their performance is challenging. These models exhibited certain shortcomings, such as inadequate handling of missing data and a lack of internal and external validation, resulting in a high risk of bias. Therefore, it is recommended that these models be updated and externally validated. The development of prospective, multi-center studies is encouraged to construct predictive models with low risk of bias and high clinical applicability, ultimately guiding evidence-based clinical practice.

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Data availability

All data is contained within the manuscript file and additional file.

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Acknowledgments

Thanks to the National Natural Science Foundation of China for supporting this research (project number: 71904133).

Funding

This work was supported by the National Natural Science Foundation of China for supporting this research (project number: 71904133).

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Authors and Affiliations

  1. School of Nursing, Capital Medical University, No. 10, Xi tou tiao, You An Men Wai, Feng tai District, Beijing, 100069, China

    Zhe Liu, Nan Jia, Qianghuizi Zhang & Weiwei Liu

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  1. Zhe Liu
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  2. Nan Jia
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Contributions

All authors participated contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhe Liu, Huiziqiang Zhang and Nan Jia. Zhe Liu drafted the work and Weiwei Liu revised it critically for important intellectual content. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Weiwei Liu.

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Liu, Z., Jia, N., Zhang, Q. et al. Risk prediction models for postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a scoping review. J Diabetes Metab Disord (2023). https://doi.org/10.1007/s40200-023-01330-1

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