A meta‐analysis of diabetes risk prediction models applied to prediabetes screening
Authors' conclusion
Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.
Study context
- Population
- Studies of diabetes risk prediction models used for prediabetes screening20 models from 24 studies
- Outcome
- Model predictors and reported prediction performance
Methodological detailsQUADAS-2 risk of bias assessment, Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) searched to 30 August 2023; pooled with Stata and R
- QUADAS-2 risk of bias assessment
- Cochrane, PubMed, Embase, Web of Science and CNKI searched to 30 August 2023; pooled with Stata and R
Pooled result
As reported in the abstract
Articles screened
29 375
Studies reporting discrimination
79.2%
Studies reporting calibration
4.2%
Original publication
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