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Coronary Artery Disease: HELP
Articles by Jelena Kotur-Stevuljević
Based on 2 articles published since 2010
(Why 2 articles?)

Between 2010 and 2020, Jelena Kotur-Stevuljevic wrote the following 2 articles about Coronary Artery Disease.
+ Citations + Abstracts
1 Article Factor analysis of risk variables associated with iron status in patients with coronary artery disease. 2014

Spasojevic-Kalimanovska, Vesna / Bogavac-Stanojevic, Natasa / Kalimanovska-Ostric, Dimitra / Memon, Lidija / Spasic, Slavica / Kotur-Stevuljevic, Jelena / Jelic-Ivanovic, Zorana. ·Institute of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia. Electronic address: vkalima@pharmacy.bg.ac.rs. · Institute of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia. · Faculty of Medicine, University of Belgrade, Clinical Center of Serbia, Belgrade, Serbia. · Clinical Chemistry Laboratory, Clinical Center "Bezanijska Kosa", Belgrade, Serbia. ·Clin Biochem · Pubmed #24690216.

ABSTRACT: OBJECTIVES: Epidemiological evidence concerning the role of iron, a lipid peroxidation catalyst, in atherosclerosis and coronary artery disease (CAD) is inconsistent. DESIGN AND METHODS: Exploratory factor analysis was used to examine the potential clustering of variables known to be associated with CAD using data from 188 patients with angiographically-approved disease. The resulting factors were then tested for their association with serum ferritin and soluble transferrin receptor (sTfR) as indicators of body iron status. RESULTS: Factor analysis resulted in a reduction of a variable number from the original 15 to 5 composite clusters. These factors were interpreted as (1) "proatherogenic factor" with positive loadings of TC, LDL-C, apoB and TG; (2) "inflammatory factor" with positive loadings of hsCRP, fibrinogen and MDA; (3) "antiatherogenic factor" with positive loadings of HDL-C and apoA-I; (4) "obesity factor" with positive loadings of weight and waist; and (5) "antioxidative status factor" with positive loadings of SOD and age and negative loading of superoxide anion. "Inflammatory", "obesity" and "antiatherogenic" factors predicted high ferritin values and the "proatherogenic factor" predicted high sTfR values. We compared the ability of the "proatherogenic factor" with that of a multivariable logistic model that included the "proatherogenic factor" and sTfR values in predicting significant stenosis in patients. The area under the ROC curve was 0.692 vs. 0.821, respectively. CONCLUSIONS: "Inflammatory", "obesity", "antiatherogenic" and "proatherogenic" factors were associated with increased parameters of body iron status. The measurement of sTfR improves the prediction of CAD based on clustered cardiovascular risk factors.

2 Article A multimarker approach for the prediction of coronary artery disease: cost-effectiveness analysis. 2010

Lakić, Dragana / Bogavac-Stanojević, Nataša / Jelić-Ivanović, Zorana / Kotur-Stevuljević, Jelena / Spasić, Slavica / Kos, Mitja. ·Institute for Social Pharmacy and Pharmacy Legislation, Faculty of Pharmacy, University of Belgrade, Serbia. dlakic@pharmacy.bg.ac.rs ·Value Health · Pubmed #20667056.

ABSTRACT: OBJECTIVES: Coronary artery disease (CAD), as the leading cause of death, poses a huge economic burden on health-care systems. We used a multi-marker approach to explore discriminative abilities of several lipid, inflammatory, and oxidative stress/antioxidative defense markers as CAD predictors. We assessed their cost-effectiveness compared with the Framingham risk score (FRS). METHODS: Using a decision model, we evaluated the costs, accuracy, and cost-effectiveness of each model. The FRS was used as the baseline model. Other models were formed with the consecutive addition of selected markers: apolipoprotein A-I (apoA-I), apolipoprotein B (apoB), apolipoprotein (a) [apo(a)] isoform, lipoprotein (a), high-sensitivity C-reactive protein, malondialdehyde, superoxide dismutase (SOD), sulfhydryl, and superoxide anion (O(2) (-) ). A best-case model was formed from a combination of diagnostic markers to yield the best patient stratification algorithm. All models were assessed by their predictive probabilities using receiver operating characteristic curves. To accomplish our goals, we recruited 188 CAD patients (verified by coronary angiography) and 197 asymptomatic CAD-free subjects for comparison. The analysis was performed from a third-party payer perspective. RESULTS: Only two strategies had outstanding discriminative abilities: the best-case model (FRS, SOD, and O(2) (-) ) and FRS plus SOD with area under the curve (AUC) values of 0.924 and 0.906, respectively. The cost-effectiveness ratio varied between €593 per AUC for the baseline model to €2425 per AUC for FRS plus apo(a) isoform. Strategies involving oxidative stress/antioxidative defense markers were more cost-effective than strategies involving lipid or inflammatory markers. All results were robust. CONCLUSION: Our results support the feasibility of a multimarker approach for CAD screening. The introduction of oxidative stress/antioxidative defense markers in the clinical laboratory would be convenient and cost-effective.