A Nomogram for Identifying Subclinical Atherosclerosis in Chronic Kidney Disease

Abstract

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 crosssectional 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.

Plasma Cell-Free DNA as a Novel Biomarker for the Diagnosis and Monitoring of Atherosclerosis

“…Significant advances have been made in the novel biomarker identification for AS development and progression. A developed logistic model based on the well-known AS risk factors such as gender, age, hypertension, and diabetes shows excellent predictive ability (AUC = 0.808) and might have a significant clinical implication for detecting subclinical atherosclerosis in patients with chronic kidney disease [ 40 ]. Additionally, circulating miRNA-933 has proven to be related with hyperlipidemia (AUC = 0.739) and atherosclerosis (AUC = 0.703) [ 41 ].…”

Towards Optimal Cardiovascular Health: A Comprehensive Review of Preventive Strategies

“…It is recommended to limit sodium intake to less than 2,300 mg per day (or even lower for certain individuals) [ 61 ]. If prescribed medication for HTN, it is essential to take it as directed by a healthcare professional and attend regular check-ups [ 61 , 62 ]. Regular visits with a healthcare professional can help monitor blood pressure levels and identify any potential risk factors for HTN [ 61 , 62 ].…”