Xia Zhuoran, Cao Songmei, Li Teng, Qin Yuan, Zhong Yu
Department of Nursing, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, People's Republic of China.
School of Medicine, Jiangsu University, Zhenjiang, 212001, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Nov 25;17:4425-4438. doi: 10.2147/DMSO.S489819. eCollection 2024.
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.
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.
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.
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.
本研究旨在系统回顾2型糖尿病患者轻度认知障碍风险预测模型的现有研究，并分析这些模型的预测性能。
对中国知网、万方、维普、中国生物医学文献数据库、PubMed、Embase、Cochrane图书馆、护理学与健康领域数据库以及Web of Science上发表的关于2型糖尿病患者轻度认知障碍风险预测模型的研究进行系统的计算机检索，检索时间跨度为各数据库建库起始至2024年11月10日。两名独立评审员根据预先设定的纳入和排除标准进行文献筛选和数据提取。随后使用预测模型偏倚风险评估工具评估纳入研究的偏倚风险和适用性。使用Stata 17.0软件对模型的预测性能进行荟萃分析。
分析共纳入12项研究和17个预测模型，模型的受试者工作特征曲线下面积（AUC）范围为0.743至0.987。所有研究均被评估为具有较高的偏倚风险，尤其是在数据分析领域存在报告不足的问题。六个验证模型的合并AUC值为0.854，表明这些模型具有良好的预测性能。多变量模型一致将年龄、教育程度、病程、抑郁和糖化血红蛋白水平确定为独立预测因素。
2型糖尿病患者轻度认知障碍风险预测模型的开发仍处于起步阶段。为了开发更准确实用的2型糖尿病患者轻度认知障碍风险预测模型，未来的研究必须依赖大样本、多中心前瞻性队列，并坚持严谨的研究设计。
