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Am J Med ; 117(9): 657-64, 2004 Nov 01.
Article in English | MEDLINE | ID: mdl-15501203

ABSTRACT

PURPOSE: To determine whether sex and age affect serum C-reactive protein level and its prognostic value in patients with coronary artery disease. METHODS: In a consecutive series of 2254 patients with angiographically defined coronary artery disease, baseline C-reactive protein and predictive value for incident death or nonfatal myocardial infarction by sex and age (<55 and > or =55 years) were compared. C-reactive protein levels were measured by fluorescence polarization immunoassay with use of a medium-sensitivity method. Patients were followed for a mean (+/-SD) of 3.1 +/- 2.2 years. Comparisons used ln-transformed C-reactive protein and linear and time-to-event regression analyses, adjusting for confounders. RESULTS: Overall, women had higher geometric mean C-reactive protein levels than did men (1.47 vs. 1.30 mg/dL, P <0.001), even after adjustment for age, hyperlipidemia, diabetes, prior myocardial infarction, body mass index, and heart failure (P = 0.002). High C-reactive protein levels were associated with increased mortality or myocardial infarction among men (adjusted hazard ratio [HR] = 1.9; 95% confidence interval [CI]: 1.5 to 2.3) but not among women (HR = 1.0; 95% CI: 0.69 to 1.4). Among patients aged <55 years, C-reactive protein level was similarly predictive in men and women (HR = 2.2 vs. 2.7), whereas in patients > or =55 years of age, it remained predictive for men (HR = 1.8; 95% CI: 1.5 to 2.3) but not women (HR = 0.93; 95% CI: 0.63 to 1.4). CONCLUSION: We found that the prognostic value of C-reactive protein in coronary artery disease patients varied by sex and age. This sex-age interaction may have important implications for C-reactive protein-based secondary risk assessment and requires further investigation.


Subject(s)
C-Reactive Protein/analysis , Coronary Disease/blood , Coronary Disease/diagnosis , Age Factors , Aged , Coronary Angiography , Female , Humans , Male , Multivariate Analysis , Predictive Value of Tests , Proportional Hazards Models , Risk Factors , Sex Factors
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