Background: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with chronic kidney disease (CKD). Cardiovascular risk assessment in this population is hampered by the failure of traditional risk factors to fully account for the elevated CVD risk (reverse epidemiology effect) and the presence of emerging risk factors specifically related to kidney failure. Therefore, diagnostic tools capable of improving cardiovascular risk assessment beyond traditional risk factors are currently warranted. We present the protocol of a 4-year prospective study aimed to assess the predictive value of non-invasive imaging techniques and biomarkers for CVD events and mortality in patients with CKD.
Methods: From November 2009 to October 2010, 4137 asymptomatic adult patients with stages 2 to 5 CKD will be recruited from nephrology services and dialysis units throughout Spain. During the same period, 843 participants without CKD (control group) will be recruited from lists of primary care physicians, only at baseline. During the follow-up, CVD events and mortality will be recorded from all CKD patients. Clinical and laboratory characteristics will be collected in a medical documentation sheet. Three trained itinerant teams will carry out a carotid ultrasound to assess intima-media thickness and presence of plaques. A composite atherosclerosis score will be constructed based on carotid ultrasound data and measurement of ankle-brachial index. In CKD patients, presence and type of calcifications will be assessed in the wall of carotid, femoral and brachial arteries, and in cardiac valves, by ultrasound. From all participants, blood samples will be collected and stored in a biobank to study novel biomarkers.
Conclusions: The NEFRONA study is the first large, prospective study to examine the predictive value of several non-invasive imaging techniques and novel biomarkers in CKD patients throughout Spain. Hereby, we present the protocol of this study aimed to explore the most effective way in which these tests can be integrated with traditional risk factors to maximize CVD detection in this population.