A Nomogram for Identifying Subclinical Atherosclerosis in Chronic Kidney Disease

Jiachuan Xiong,* Zhikai Yu,* Daohai Zhang, Yinghui Huang, Ke Yang, Jinghong Zhao Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jinghong ZhaoDepartment of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, People’s Republic of ChinaTel/Fax +86 023 68774321Email zhaojh@tmmu.edu.cnPurpose: Atherosclerosis contributes substantially to cardiovascular mortality in patients with chronic kidney disease (CKD). But precise risk model for subclinical atherosclerosis in the CKD population is still lacking. The study aimed to develop and validate a nomogram for screening subclinical atherosclerosis among CKD patients without dialysis.Patients and Methods: A total of 1452 CKD stage 1‒5 has been recruited in this cross-sectional study. Subclinical atherosclerosis was diagnosed with carotid ultrasonography. Patients were divided into the training set and validation set. The risk factors of atherosclerosis were identified by the training set and confirmed by the validation set. The receiver operating characteristic (ROC) curves and decision curve analyses (DCA) were executed to evaluate the accuracy of fitted logistic models in training and validation sets. Finally, a nomogram based on constructed logistic regression model in all participants was plotted.Results: A total of 669 (46.1%) patients were diagnosed with subclinical carotid atherosclerosis. Binary logistic regression analysis showed that males, age, hypertension, diabetes, CKD stages, calcium, platelet, and albumin were risk factors for atherosclerosis. The accuracy of fitted logistic models was evaluated by the area under the ROC curve (AUC), which showed good predictive accuracy in the training set (AUC=0.764 (95% Confidence interval (CI): 0.733– 0.794) and validation set (AUC=0.808 (95% CI: 0.765– 0.852). A high net benefit was also proven by the DCA. Finally, these predictors were all included to generate the nomogram.Conclusion: This proposed nomogram shows excellent predictive ability and might have a significant clinical implication for detecting subclinical atherosclerosis in patients with CKD.Keywords: atherosclerosis, age, sex, risk factor