Wang Zhaokai, Yang Shuping, Li Cheng, Zhou Chunxue, Wang Chaofan, Jiang Tangxing, Chen Chengcheng, Shao Mengxin, Xu Tongda
Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Front Cardiovasc Med. 2025 Jun 26;12:1552762. doi: 10.3389/fcvm.2025.1552762. eCollection 2025.
This study aimed to develop and validate an angiography-derived microcirculatory resistance index (AMR)- based nomogram to predict the probability of contrast-induced nephropathy (CIN) following percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI).
In this study, 595 STEMI patients from the Affiliated Hospital of Xuzhou Medical University from January 1, 2022 to December 31, 2023 were included as the training cohort, and 256 patients from the East Hospital of Xuzhou Medical University were included as the validation cohort. Independent risk factors for the development of nomogram were identified using univariate logistic regression, randomized forest regression, multifactorial logistic regression, and LASSO regression analyses. The study evaluated performance by creating calibration curves, analyzing the area under the curve (AUC-ROC) of subjects' work characteristics, examining calibration plots, and conducting decision curve analysis (DCA).
Multifactorial logistic regression analysis identified five independent predictors, including eGFR (OR:0.975; 95% CI: 0.970-0.983; < 0.001), AMR (OR: 2.505; 95% CI: 1.756-3.656; < 0.001), Serum blood uric acid to high-density lipoprotein cholesterol ratio (UHR) (OR: 1.006; 95% CI: 1.003-1.007; < 0.001), The triglyceride and glucose index (TyG) (OR: 1.829; 95% CI: 1.346-2.502; < 0.001), Contrast agent dosage (OR: 1.022; 95% CI: 1.016-1.028; < 0.001), The nomogram accurately predicted the probability of CIN after PCI. Both the training cohort (AUC: 0.881) and validation cohort (AUC: 0.841) demonstrated good predictive ability of the model. Calibration plots confirmed the agreement between the predictions of the training and validation cohorts. DCA analysis also demonstrated the feasibility of the nomogram in clinical patient management.
The nomogram showed good performance in predicting CIN, and it could help clinicians optimize the clinical treatments to improve the prognosis of STEMI patients.
本研究旨在开发并验证一种基于血管造影衍生的微循环阻力指数（AMR）的列线图，以预测急性ST段抬高型心肌梗死（STEMI）患者经皮冠状动脉介入治疗（PCI）后发生对比剂肾病（CIN）的概率。
本研究纳入了2022年1月1日至2023年12月31日徐州医科大学附属医院的595例STEMI患者作为训练队列，以及徐州医科大学东院的256例患者作为验证队列。使用单因素逻辑回归、随机森林回归、多因素逻辑回归和LASSO回归分析确定列线图的独立危险因素。该研究通过创建校准曲线、分析受试者工作特征曲线下面积（AUC-ROC）、检查校准图以及进行决策曲线分析（DCA）来评估性能。
多因素逻辑回归分析确定了五个独立预测因素，包括估算肾小球滤过率（eGFR）（OR：0.975；95%CI：0.970-0.983；P<0.001）、AMR（OR：2.505；95%CI：1.756-3.656；P<0.001）、血清尿酸与高密度脂蛋白胆固醇比值（UHR）（OR：1.006；95%CI：1.003-1.007；P<0.001）、甘油三酯和葡萄糖指数（TyG）（OR：1.829；95%CI：1.346-2.502；P<0.001）、造影剂剂量（OR：1.022；95%CI：1.016-1.028；P<0.001）。该列线图准确预测了PCI术后CIN的概率。训练队列（AUC：0.881）和验证队列（AUC：0.841）均显示该模型具有良好的预测能力。校准图证实了训练队列和验证队列预测结果的一致性。DCA分析也证明了列线图在临床患者管理中的可行性。
该列线图在预测CIN方面表现良好，可帮助临床医生优化临床治疗，改善STEMI患者的预后。
