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The effect of including C-reactive protein in cardiovascular risk prediction models for women
Nancy R Cook1, Julie E Buring, Paul M Ridker
1Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA. ncook@rics.bwh.harvard.edu
Insights
High-sensitivity C-reactive protein (hsCRP) improves cardiovascular risk prediction in women, especially those with a 5-20% 10-year risk. Including hsCRP enhances risk classification as much as lipid measures when age, blood pressure, and smoking are considered.
Area of Science:
- Cardiology
- Biomarkers
- Public Health
Background:
- High-sensitivity C-reactive protein (hsCRP) is a known cardiovascular risk predictor.
- Existing global risk models lack hsCRP integration for clinical application.
Purpose of the Study:
- Develop and compare cardiovascular risk prediction models with and without hsCRP.
- Assess the impact of hsCRP on risk classification accuracy in women.
Main Methods:
- Observational cohort study of U.S. female health professionals (Women's Health Study).
- Followed initially healthy, nondiabetic women aged 45+ for an average of 10 years.
- Monitored incident cardiovascular events: myocardial infarction, stroke, revascularization, and cardiovascular death.
Main Results:
- hsCRP contributed significantly to global risk prediction, comparable to individual lipid measures (HDL, LDL).
- Models incorporating hsCRP showed improved global fit and more accurate risk prediction, particularly for 5-20% 10-year risk categories.
- Reclassification analysis showed significant improvements in risk stratification when hsCRP was added.
Conclusions:
- A global risk prediction model incorporating hsCRP enhances cardiovascular risk classification in women.
- hsCRP improves prediction accuracy comparable to lipid measures when key clinical factors are included.
- The study highlights the value of hsCRP in refining cardiovascular risk assessment in women.
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.
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