Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Lu He Hospital Capital Medical University, Beijing, 101149, China.
Sci Rep. 2020 Sep 2;10(1):14457. doi: 10.1038/s41598-020-71321-2.
Type 2 diabetes mellitus (T2DM) is one common chronic disease caused by insulin secretion disorder that often leads to severe outcomes and even death due to complications, among which coronary heart disease (CHD) represents the most common and severe one. Given a huge number of T2DM patients, it is thus increasingly important to identify the ones with high risks of CHD complication but the quantitative method is still not available. Here, we first curated a dataset of 1,273 T2DM patients including 304 and 969 ones with or without CHD, respectively. We then trained an artificial intelligence (AI) model using randomly selected 4/5 of the dataset and use the rest data to validate the performance of the model. The result showed that the model achieved an AUC of 0.77 (fivefold cross-validation) on the training dataset and 0.80 on the testing dataset. To further confirm the performance of the presented model, we recruited 1,253 new T2DM patients as totally independent testing dataset including 200 and 1,053 ones with or without CHD. And the model achieved an AUC of 0.71. In addition, we implemented a model to quantitatively evaluate the risk contribution of each feature, which is thus able to present personalized guidance for specific individuals. Finally, an online web server for the model was built. This study presented an AI model to determine the risk of T2DM patients to develop to CHD, which has potential value in providing early warning personalized guidance of CHD risk for both T2DM patients and clinicians.
2 型糖尿病(T2DM)是一种由胰岛素分泌障碍引起的常见慢性疾病,常因并发症导致严重后果甚至死亡,其中冠心病(CHD)最为常见和严重。鉴于 T2DM 患者数量庞大,因此识别出有 CHD 并发症高风险的患者变得越来越重要,但目前还没有定量的方法。在这里,我们首先整理了一个包含 1273 名 T2DM 患者的数据集,其中分别有 304 名和 969 名患者患有或未患有 CHD。然后,我们使用数据集的随机选择的 4/5 训练人工智能(AI)模型,并使用其余数据验证模型的性能。结果表明,该模型在训练数据集上的 AUC 为 0.77(五重交叉验证),在测试数据集上的 AUC 为 0.80。为了进一步确认所提出模型的性能,我们招募了 1253 名新的 T2DM 患者作为完全独立的测试数据集,其中分别有 200 名和 1053 名患者患有或未患有 CHD。该模型在该测试数据集上的 AUC 为 0.71。此外,我们实施了一种模型来定量评估每个特征的风险贡献,从而能够为特定个体提供个性化的指导。最后,我们构建了一个该模型的在线网络服务器。本研究提出了一种用于确定 T2DM 患者发生 CHD 风险的 AI 模型,该模型可为 T2DM 患者和临床医生提供 CHD 风险的早期预警个性化指导,具有潜在的应用价值。