Zhao Meng, Yao Zhixin, Zhang Yan, Ma Lidan, Pang Wenquan, Ma Shuyin, Xu Yijun, Wei Lili
Department of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
Department of Emergency Pediatric, The Affiliated Hospital of Medical College Qingdao University, Qingdao, Shandong, 266003, China.
BMC Med Inform Decis Mak. 2025 Jan 13;25(1):18. doi: 10.1186/s12911-024-02848-x.
This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).
A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science up to July 02, 2024. The quality of the studies included was assessed. The risk of bias was assessed through the prediction model risk of bias assessment tool and a graph was drawn accordingly. The meta-analysis was performed using Stata15.0.
A total of 13 studies were included in the present review, involving 11,320 GDM patients and 22 ML models. The meta-analysis for ML models showed a pooled C-statistic of 0.82 (95% CI: 0.79 ~ 0.86), a pooled sensitivity of 0.76 (0.72 ~ 0.80), and a pooled specificity of 0.57 (0.50 ~ 0.65).
ML has favorable diagnostic accuracy for the progression of GDM to T2DM. This provides evidence for the development of predictive tools with broader applicability.
本系统评价旨在探讨机器学习（ML）模型对妊娠期糖尿病（GDM）进展为2型糖尿病（T2DM）的早期预测价值。
截至2024年7月2日，在PubMed、Cochrane、Embase和Web of Science中进行了全面系统的检索。对纳入研究的质量进行了评估。通过预测模型偏倚风险评估工具评估偏倚风险，并据此绘制图表。使用Stata15.0进行荟萃分析。
本综述共纳入13项研究，涉及11320例GDM患者和22个ML模型。ML模型的荟萃分析显示，合并C统计量为0.82（95%CI：0.790.86），合并灵敏度为0.76（0.720.80），合并特异度为0.57（0.50~0.65）。
ML对GDM进展为T2DM具有良好的诊断准确性。这为开发具有更广泛适用性的预测工具提供了证据。
