Value of 2 risk prediction models in predicting the risk of atherosclerosis in patients with chronic kidney disease.

OBJECTIVES: Cardiovascular disease (CVD) remains a high morbidity in patients with chronic kidney disease (CKD), and atherosclerosis (AS) is the main pathological basis of CVD. Atherosclerotic cardiovascular disease (ASCVD) is one of the major complications and causes of death in CKD patients. Monitoring and paying attention to the risk of AS is the manifestation of multidisciplinary comprehensive treatment. Through continuous monitoring the risk factors of AS, patients with high AS risk can be detected early, and then risk factors can be treated to improve the prognosis and life quality of CKD patients and reduce the CVD mortality of CKD. The Framingham risk prediction model and Chinese 10-year ASCVD risk prediction model can comprehensively evaluate the AS risk. The predictive value of the 2 risk prediction models has been widely confirmed in the general population, but the application in CKD population needs to be further explored. Thus, the aim of this study is to analyze the value of the 2 risk prediction models in predicting AS risk of CKD population and to find a simple and reliable method to predict AS risk of CKD population. METHODS: This study was a cross-sectional study and 146 CKD patients (CKD group) and 14 healthy controls (control group) were recruited. Carotid ultrasound examination was performed to measure the intima-media thickness of left and right carotid arteries to evaluate the prevalence of AS. Medical history, physical examination, and clinical laboratory test data were collected to calculate Framingham Risk Score (FRS) and ASCVD score for all participants. We compared baseline data and AS positive rate between the CKD group and the control group. The AS positive rates detected by FRS, ASCVD score, and carotid ultrasound examination were compared in terms of Kappa consistency test, authenticity, reliability, and benefits. RESULTS: <0.001). The sensitivities of FRS and ASCVD score were 67.65% and 73.53%, respectively, the specificities were 76.92% and 64.10%, respectively. The sensitivity was 91.44% and specificity was 49.31% when FRS and ASCVD score were used in parallel. And the sensitivity was 49.74% and the specificity was 91.71% when FRS and ASCVD score were used in series. CONCLUSIONS: The positive rates of AS in CKD patients detected by risk prediction models are not different from that detected by carotid artery ultrasound, and there are good consistency and coincidence rate among risk prediction models and carotid artery ultrasound. Risk prediction models are suitable for predicting the risk of AS in patients with CKD, and the combined application of them can further improve the sensitivity or specificity of diagnosis.