Usefulness of Multidetector Row Computed Tomography for Predicting Cardiac Events in Asymptomatic Chronic Kidney Disease Patients at the Initiation of Renal Replacement Therapy - Jung Eun Lee; Yong Kyu Lee; Eun Jeong Choi; Ji Sun Nam; Byoung Wook Choi; Beom Seok Kim (The Scientific World Journal)
Background. The prevalence of coronary artery stenosis (CAS) at the initiation of renal replacement therapy (RRT) in chronic kidney disease (CKD) patients has not been fully elucidated. Although coronary angiography is the gold standard in diagnosing CAS its invasiveness and economic burden lead to searching for a noninvasive alternative method. In this study, we evaluated the prevalence of CAS by multidetector row computed tomography (MDCT) and related risk factor to articulate the usefulness of MDCT. Method. Seventy-four asymptomatic CKD patients who began dialysis were evaluated with echocardiography and MDCT. The patients were stratified into two groups according to CAS and coronary artery calcification score (CACS) by MDCT to detect silent CAS and evaluate its predictability for cardiac events. Results. CAS was seen in 24 (32.4%) of 74 asymptomatic CKD patients on MDCT. Both groups showed increasing frequencies of CAS with age (P<0.01), presence of diabetes (P<0.05), uric acid level (P<0.01), and calcium score (P<0.01). Multiple regression analysis revealed that age and uric acid level were independent risk factors for CAS and high CACS in asymptomatic CKD patients at the initiation of dialysis. Patients with both CAS and high CACS were presented with higher cardiac events rates compared to those without any of them. In Cox regression model, age and the presence of CAS and high CACS on MDCT were an independent risk factor for cardiac events in these patients. Conclusion. We showed that CAS was highly seen in asymptomatic CKD patients starting dialysis. Moreover, both high CACS and CAS on MDCT might predict cardiac events in these patients and MDCT can be a useful screening tool for evaluating coronary artery disease and predicting cardiovascular mortality noninvasively.