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4 July 2006 | Volume 145 Issue 1 | Pages 21-29
Background: While high-sensitivity C-reactive protein (hsCRP) is an independent predictor of cardiovascular risk, global risk prediction models incorporating hsCRP have not been developed for clinical use.
Objective: To develop and compare global cardiovascular risk prediction models with and without hsCRP.
Design: Observational cohort study.
Setting: U.S. female health professionals.
Participants: Initially healthy nondiabetic women age 45 years and older participating in the Women's Health Study and followed an average of 10 years.
Measurements: Incident cardiovascular events (myocardial infarction, stroke, coronary revascularization, and cardiovascular death).
Results: High-sensitivity CRP made a relative contribution to global risk at least as large as that provided by total, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol individually, but less than that provided by age, smoking, and blood pressure. All global measures of fit improved when hsCRP was included, with likelihood-based measures demonstrating strong preference for models that include hsCRP. With use of 10-year risk categories of 0% to less than 5%, 5% to less than 10%, 10% to less than 20%, and 20% or greater, risk prediction was more accurate in models that included hsCRP, particularly for risk between 5% and 20%. Among women initially classified with risks of 5% to less than 10% and 10% to less than 20% according to the Adult Treatment Panel III covariables, 21% and 19%, respectively, were reclassified into more accurate risk categories. Although addition of hsCRP had minimal effect on the c-statistic (a measure of model discrimination) once age, smoking, and blood pressure were accounted for, the effect was nonetheless greater than that of total, LDL, or HDL cholesterol, suggesting that the c-statistic may be insensitive in evaluating risk prediction models.
Limitations: Data were available only for women.
Conclusions: A global risk prediction model that includes hsCRP improves cardiovascular risk classification in women, particularly among those with a 10-year risk of 5% to 20%. In models that include age, blood pressure, and smoking status, hsCRP improves prediction at least as much as do lipid measures.
Editors' Notes
Context
Contribution
Cautions
Implications
—The Editors
Author and Article Information
From Brigham and Women's Hospital, Harvard Medical School, and Harvard School of Public Health, Boston, Massachusetts.
Grant Support: By grants from the Donald W. Reynolds Foundation (Las Vegas, Nevada), the Leducq Foundation (Paris, France), and the Doris Duke Charitable Foundation (New York). The overall Women's Health Study cohort is supported by grants HL-43851 and CA-47988 from the National Heart, Lung, and Blood Institute and the National Cancer Institute (both in Bethesda, Maryland).
Potential Financial Conflicts of Interest: Honoraria: P.M. Ridker (Dade Behring); Grants received: P.M. Ridker (Reynolds Foundation, Leducq Foundation, Doris Duke Foundation, National Heart, Lung, and Blood Institute, National Cancer Institute, American Heart Association, Dade Behring, AstraZeneca, Novartis, Sanofi-Aventis). Dr. Ridker is listed as a co-inventor on patents held by the Brigham and Women's Hospital that relate to the use of inflammatory biomarkers in cardiovascular disease.
Requests for Single Reprints: Nancy R. Cook, ScD, Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston, MA 02215; e-mail, ncook{at}rics.bwh.harvard.edu.
Current Author Addresses: Drs. Cook, Buring, and Ridker: Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston, MA 02215.
Author Contributions: Conception and design: N.R. Cook, P.M. Ridker.
Analysis and interpretation of the data: N.R. Cook, P.M. Ridker.
Drafting of the article: N.R. Cook, P.M. Ridker.
Critical revision of the article for important intellectual content: N.R. Cook, J.E. Buring, P.M. Ridker.
Final approval of the article: N.R. Cook, P.M. Ridker.
Statistical expertise: N.R. Cook.
Obtaining of funding: P.M. Ridker.
Collection and assembly of data: J.E. Buring. ARTICLE
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