Risk Prediction Models for Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Abstract

Objective: This study aimed to systematically review the existing research on risk prediction models for mild cognitive impairment in patients with type 2 diabetes mellitus and to analyze the predictive performance of these models.

Methods: A systematic computerized search was conducted for studies published in CNKI, Wanfang, VIP, CBM, PubMed, Embase, Cochrane Library, CINAHL, and Web of Science regarding risk prediction models for mild cognitive impairment in patients with type 2 diabetes mellitus, covering the period the inception of the databases through November 10, 2024. Two independent reviewers performed literature screening and data extraction based on predefined inclusion and exclusion criteria. The risk of bias and the applicability of the included studies were subsequently evaluated using the Risk of Bias Assessment Tool for Prediction Models. A meta-analysis of the predictive performance of the models was performed using Stata 17.0 software.

Results: A total of 12 studies and 17 prediction models were included in the analysis, with the area under the receiver operating characteristic curve (AUC) for the models ranging from 0.743 to 0.987. All studies were assessed to be at high risk of bias, particularly concerning the issue of underreporting in the area of data analysis. The combined AUC value of the six validated models was 0.854, indicating that these models exhibited favorable predictive performance. The multivariate models consistently identified age, education, disease duration, depression, and glycosylated hemoglobin level as independent predictors.

Conclusion: The development of risk prediction models for mild cognitive impairment in patients with type 2 diabetes mellitus is still in its infancy. In order to develop more accurate and practical risk prediction models for mild cognitive impairment in patients with type 2 diabetes mellitus, future studies must rely on large-sample, multicenter prospective cohorts and adhere to rigorous study designs.