A Predictive Model Based on a New CI-AKI Definition to Predict Contrast Induced Nephropathy in Patients With Coronary Artery Disease With Relatively Normal Renal Function

作者：Hanjun Mo, Fang Ye, Danxia Chen, Qizhe Wang, Ru Liu, Panpan Zhang, Yaxin Xu, Xuelin Cheng, Zhendong Mei, Yan Zheng, Yuxiang Dai, Sunfang Jiang, Junbo Ge · 发表于：Frontiers in Cardiovascular Medicine · 年份：2021 · DOI：10.3389/fcvm.2021.762576 · 被引用次数：23 · 研究领域：Acute Kidney Injury Research、Hemodynamic Monitoring and Therapy、Cardiac Imaging and Diagnostics

Background: Contrast induced nephropathy (CIN) is a common complication in patients receiving intravascular contrast media. In 2020, the American College of Radiology and the National Kidney Foundation issued a new contrast induced acute kidney injury (CI-AKI) criteria. Therefore, we aimed to explore the potential risk factors for CIN under the new criteria, and develop a predictive model for patients with coronary artery disease (CAD) with relatively normal renal function (NRF). Methods: Patients undergoing coronary angiography or percutaneous coronary intervention at Zhongshan Hospital, Fudan University between May 2019 and April 2020 were consecutively enrolled. Eligible candidates were selected for statistical analysis. Univariate and multivariate logistic regression analyses were used to identify the predictive factors. A stepwise method and a machine learning (ML) method were used to construct a model based on the Akaike information criterion. The performance of our model was evaluated using the area under the receiver operating characteristic curves (AUC) and calibration curves. The model was further simplified into a risk score. Results: A total of 2,009 patients with complete information were included in the final statistical analysis. The results showed that the incidence of CIN was 3.2 and 1.2% under the old and new criteria, respectively. Three independent predictors were identified: baseline uric acid level, creatine kinase-MB level, and log (N-terminal pro-brain...