Zhu Mengmeng, Li Yiwen, Wang Wenting, Liu Yanfei, Tong Tiejun, Liu Yue
National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, No.1 of Xiyuan Caochang, Haidian District, Beijing, 100091, China.
Cardiovascular Disease Group, China Center for Evidence-Based Medicine of TCM, China Academy of Chinese Medical Sciences, Beijing, China.
Sci Rep. 2024 Jun 13;14(1):13652. doi: 10.1038/s41598-024-64430-9.
Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram ( https://mr.cscps.com.cn/wscoringtool/index.html ) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.
迫切需要简单实用的工具来筛查经皮冠状动脉介入治疗（PCI）后新发高危糖尿病（NODAP），以改善PCI后的预后。我们旨在评估NODAP的危险因素，并基于多中心数据库使用传统变量开发一种在线预测工具。中国循证医学数据库由中国大陆4家医院的249987例患者组成。本研究纳入年龄≥18岁、因急性冠状动脉综合征植入冠状动脉支架且PCI前无糖尿病的患者。根据PCI后新发糖尿病的发生情况，将患者分为NODAP组和非NODAP组。经过最小绝对收缩和选择算子回归以及逻辑回归后，选择模型特征，然后绘制列线图。通过受试者工作特征曲线、校准曲线、Hosmer-Lemeshow检验和决策曲线分析评估模型性能。该列线图也在另一家医院进行了外部验证。随后，我们基于该模型开发了一个在线可视化工具和相应的风险分层系统，以预测PCI后发生NODAP的风险。基于多中心数据库，最终分析纳入了2698例PCI术后患者（1255例NODAP患者和1443例非NODAP患者）。筛选后确定了五个预测因素：空腹血糖、低密度脂蛋白胆固醇、高血压、糖尿病家族史和利尿剂的使用。然后，我们开发了一个基于网络的列线图（https://mr.cscps.com.cn/wscoringtool/index.html），纳入上述传统因素，用于预测NODAP高危患者。该列线图显示出良好的区分度、校准度和临床实用性，能够准确地将患者分层为不同的NODAP风险。我们基于多中心数据库开发了一个简单实用的基于网络的列线图来筛查NODAP风险，这可以帮助临床医生准确识别NODAP高危患者，并制定PCI后管理策略以改善患者预后。
