An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography

Duanbin Li; Hangpan Jiang; Xinrui Yang; Maoning Lin; Menghan Gao; Zhezhe Chen; Guosheng Fu; Dongwu Lai; Wenbin Zhang

doi:10.3389/fmed.2022.839856

An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography

Front Med (Lausanne). 2022 Mar 11:9:839856. doi: 10.3389/fmed.2022.839856. eCollection 2022.

Authors

Duanbin Li^{1

2}, Hangpan Jiang³, Xinrui Yang^{1

2

4}, Maoning Lin^{1

2}, Menghan Gao⁵, Zhezhe Chen^{1

2}, Guosheng Fu^{1

2}, Dongwu Lai^{1

2}, Wenbin Zhang^{1

2}

Affiliations

¹ Department of Cardiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
² Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Hangzhou, China.
³ Department of Cardiology, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China.
⁴ Department of Cardiology, The Hangzhou Lin'an People's Hospital, Hangzhou, China.
⁵ College of Medicine, Zhejiang University, Hangzhou, China.

Abstract

Background: 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).

Methods: 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).

Results: 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 (P = 0.965) and the testing dataset (P = 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/).

Conclusion: An easy-to-use pre-procedural nomogram for predicting CA-AKI was established and validated in patients undergoing CAG, which was also deployed online.

Keywords: contrast-associated acute kidney injury; coronary angiography; coronary artery disease; nomogram; percutaneous coronary intervention.