Li Yihan, Jin Nan, Zhan Qiuzhong, Huang Yue, Sun Aochuan, Yin Fen, Li Zhuangzhuang, Hu Jiayu, Liu Zhengtang
Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, Macao SAR, China.
Front Endocrinol (Lausanne). 2025 Mar 3;16:1495306. doi: 10.3389/fendo.2025.1495306. eCollection 2025.
Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). However, the performance of these models still varies, which limits their widespread adoption and practical application. Therefore, we conducted a systematic review and meta-analysis to summarize and evaluate the performance and clinical applicability of these risk predictive models and to identify key research gaps.
We conducted a systematic review and meta-analysis to compare the performance of ML predictive models. We searched PubMed, Embase, the Cochrane Library, and Web of Science for English-language studies using ML algorithms to predict the risk of DKD in patients with T2DM, covering the period from database inception to April 18, 2024. The primary performance metric for the models was the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.
26 studies that met the eligibility criteria were included into the meta-analysis. 25 studies performed internal validation, but only 8 studies conducted external validation. A total of 94 ML models were developed, with 81 models evaluated in the internal validation sets and 13 in the external validation sets. The pooled AUC was 0.839 (95% CI 0.787-0.890) in the internal validation and 0.830 (95% CI 0.784-0.877) in the external validation sets. Subgroup analysis based on the type of ML showed that the pooled AUC for traditional regression ML was 0.797 (95% CI 0.777-0.816), for ML was 0.811 (95% CI 0.785-0.836), and for deep learning was 0.863 (95% CI 0.825-0.900). A total of 26 ML models were included, and the AUCs of models that were used three or more times were pooled. Among them, the random forest (RF) models demonstrated the best performance with a pooled AUC of 0.848 (95% CI 0.785-0.911).
This meta-analysis demonstrates that ML exhibit high performance in predicting DKD risk in T2DM patients. However, challenges related to data bias during model development and validation still need to be addressed. Future research should focus on enhancing data transparency and standardization, as well as validating these models' generalizability through multicenter studies.
https://inplasy.com/inplasy-2024-9-0038/, identifier INPLASY202490038.
机器学习（ML）模型越来越多地用于预测2型糖尿病（T2DM）患者发生和进展为糖尿病肾病（DKD）的风险。然而，这些模型的性能仍然存在差异，这限制了它们的广泛应用和实际应用。因此，我们进行了一项系统评价和荟萃分析，以总结和评估这些风险预测模型的性能和临床适用性，并确定关键的研究差距。
我们进行了一项系统评价和荟萃分析，以比较ML预测模型的性能。我们在PubMed、Embase、Cochrane图书馆和Web of Science中检索了使用ML算法预测T2DM患者DKD风险的英文研究，涵盖从数据库建立到2024年4月18日的时间段。模型的主要性能指标是受试者工作特征曲线下面积（AUC）及其95%置信区间（CI）。使用预测模型偏倚风险评估工具（PROBAST）清单评估偏倚风险。
26项符合纳入标准的研究被纳入荟萃分析。25项研究进行了内部验证，但只有8项研究进行了外部验证。共开发了94个ML模型，其中81个模型在内部验证集中进行了评估，13个在外部验证集中进行了评估。内部验证集的合并AUC为0.839（95%CI 0.787-0.890），外部验证集的合并AUC为0.830（95%CI 0.784-0.877）。基于ML类型的亚组分析表明，传统回归ML的合并AUC为0.797（95%CI 0.777-0.816），ML为0.811（95%CI 0.785-0.836），深度学习为0.863（95%CI 0.825-0.900）。共纳入26个ML模型，并对使用三次或更多次的模型的AUC进行了合并。其中，随机森林（RF）模型表现最佳，合并AUC为0.848（95%CI 0.785-0.911）。
这项荟萃分析表明，ML在预测T2DM患者的DKD风险方面表现出高性能。然而，模型开发和验证过程中与数据偏差相关的挑战仍需解决。未来的研究应侧重于提高数据透明度和标准化，以及通过多中心研究验证这些模型的可推广性。
https://inplasy.com/inplasy-2024-9-0038/，标识符INPLASY202490038。
