Li Duanbin, Jiang Hangpan, Yang Xinrui, Lin Maoning, Gao Menghan, Chen Zhezhe, Fu Guosheng, Lai Dongwu, Zhang Wenbin
Department of Cardiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Hangzhou, China.
Front Med (Lausanne). 2022 Mar 11;9:839856. doi: 10.3389/fmed.2022.839856. eCollection 2022.
Identifying high-risk patients for contrast-associated acute kidney injury (CA-AKI) helps to take early preventive interventions. The current study aimed to establish and validate an online pre-procedural nomogram for CA-AKI in patients undergoing coronary angiography (CAG).
In this retrospective dataset, 4,295 patients undergoing CAG were enrolled and randomized into the training or testing dataset with a split ratio of 8:2. Optimal predictors for CA-AKI were determined by Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) algorithm. Nomogram was developed and deployed online. The discrimination and accuracy of the nomogram were evaluated by receiver operating characteristic (ROC) and calibration analysis, respectively. Clinical usefulness was estimated by decision curve analysis (DCA) and clinical impact curve (CIC).
A total of 755 patients (17.1%) was diagnosed with CA-AKI. 7 pre-procedural predictors were identified and integrated into the nomogram, including age, gender, hemoglobin, N-terminal of the prohormone brain natriuretic peptide, neutrophil-to-lymphocyte ratio, cardiac troponin I, and loop diuretics use. The ROC analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset (area under the curve, AUC = 0.766, 95%CI [0.737 to 0.794]) and testing dataset (AUC = 0.737, 95%CI [0.693 to 0.780]). The nomogram was also well-calibrated in both the training dataset ( = 0.965) and the testing dataset ( = 0.789). Good clinical usefulness was identified by DCA and CIC. Finally, this model was deployed in a web server for public use (https://duanbin-li.shinyapps.io/DynNomapp/).
An easy-to-use pre-procedural nomogram for predicting CA-AKI was established and validated in patients undergoing CAG, which was also deployed online.
识别造影剂相关急性肾损伤（CA-AKI）的高危患者有助于早期采取预防干预措施。本研究旨在建立并验证一种用于接受冠状动脉造影（CAG）患者的CA-AKI术前在线列线图。
在这个回顾性数据集中，纳入4295例接受CAG的患者，并以8:2的比例随机分为训练数据集或测试数据集。通过最小绝对收缩和选择算子（LASSO）和随机森林（RF）算法确定CA-AKI的最佳预测指标。开发列线图并在线部署。分别通过受试者工作特征（ROC）和校准分析评估列线图的辨别力和准确性。通过决策曲线分析（DCA）和临床影响曲线（CIC）评估临床实用性。
共有755例患者（17.1%）被诊断为CA-AKI。确定了7个术前预测指标并将其纳入列线图，包括年龄、性别、血红蛋白、脑钠肽前体N端、中性粒细胞与淋巴细胞比值、心肌肌钙蛋白I和袢利尿剂的使用。ROC分析显示，列线图在训练数据集（曲线下面积，AUC = 0.766，95%CI [0.737至0.794]）和测试数据集（AUC = 0.737，95%CI [0.693至0.780]）中对CA-AKI具有良好的辨别性能。列线图在训练数据集（= 0.965）和测试数据集（= 0.789）中校准效果也良好。DCA和CIC显示出良好的临床实用性。最后，该模型部署在一个网络服务器上供公众使用（https://duanbin-li.shinyapps.io/DynNomapp/）。
建立并验证了一种用于预测接受CAG患者CA-AKI的易用术前列线图，该列线图也已在线部署。
