PDF(Pubmed)
Abstract：
UNASSIGNED: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.
UNASSIGNED: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.
UNASSIGNED: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.
UNASSIGNED: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.
UNASSIGNED: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.
UNASSIGNED: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.
UNASSIGNED: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.
UNASSIGNED: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.
UNASSIGNED: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.
摘要：
■这项研究系统回顾和荟萃分析了2型糖尿病患者中糖尿病肾病（DKD）的现有风险预测模型，旨在为我国学者开发更高质量的风险预测模型提供参考。
■我们搜索了包括中国国家知识基础设施(CNKI)在内的数据库，万方数据,VIP中国科技期刊数据库,中国生物医学文献数据库(CBM),PubMed,WebofScience,Embase,和Cochrane图书馆研究2型糖尿病患者DKD风险预测模型的构建，直到2023年12月28日。两名研究人员独立筛选了文献，并根据数据提取表和偏倚风险评估工具对信息进行了提取和评估，以进行预测模型研究。使用STATA14.0软件对模型的曲线下面积（AUC）值进行荟萃分析。
■共纳入32项研究，31进行内部验证和22报告校准。2型糖尿病患者中DKD的发生率为6.0%～62.3%。AUC范围为0.713至0.949,表明预测模型具有公平至优异的预测准确性。纳入研究的整体适用性良好；然而，总体上有很高的偏见风险，主要是由于大多数研究的回顾性性质，不合理的样本量，和在一个中心进行的研究。模型的荟萃分析得出的联合AUC为0.810（95％CI：0.780-0.840），表明良好的预测性能。
■我国2型糖尿病患者DKD风险预测模型研究尚处于起步阶段,总体偏倚风险较高，缺乏临床应用。未来的努力可以集中在建设高性能，基于可解释的机器学习方法的易于使用的预测模型，并将其应用于临床环境。
■本系统评价和荟萃分析是根据系统评价和荟萃分析（PRISMA）声明的首选报告项目进行的。这种研究的公认指南。
■https://www.crd.约克。AC.英国/普华永道/，标识符CRD42024498015。
■我们搜索了包括中国国家知识基础设施(CNKI)在内的数据库，万方数据,VIP中国科技期刊数据库,中国生物医学文献数据库(CBM),PubMed,WebofScience,Embase,和Cochrane图书馆研究2型糖尿病患者DKD风险预测模型的构建，直到2023年12月28日。两名研究人员独立筛选了文献，并根据数据提取表和偏倚风险评估工具对信息进行了提取和评估，以进行预测模型研究。使用STATA14.0软件对模型的曲线下面积（AUC）值进行荟萃分析。
■共纳入32项研究，31进行内部验证和22报告校准。2型糖尿病患者中DKD的发生率为6.0%～62.3%。AUC范围为0.713至0.949,表明预测模型具有公平至优异的预测准确性。纳入研究的整体适用性良好；然而，总体上有很高的偏见风险，主要是由于大多数研究的回顾性性质，不合理的样本量，和在一个中心进行的研究。模型的荟萃分析得出的联合AUC为0.810（95％CI：0.780-0.840），表明良好的预测性能。
■我国2型糖尿病患者DKD风险预测模型研究尚处于起步阶段,总体偏倚风险较高，缺乏临床应用。未来的努力可以集中在建设高性能，基于可解释的机器学习方法的易于使用的预测模型，并将其应用于临床环境。
■本系统评价和荟萃分析是根据系统评价和荟萃分析（PRISMA）声明的首选报告项目进行的。这种研究的公认指南。
■https://www.crd.约克。AC.英国/普华永道/，标识符CRD42024498015。
