Nancy R. Cook, ScD; Julie E. Buring, ScD; Paul M Ridker, MD
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, email@example.com.
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.
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.
To develop and compare global cardiovascular risk prediction models with and without hsCRP.
Observational cohort study.
U.S. female health professionals.
Initially healthy nondiabetic women age 45 years and older participating in the Women's Health Study and followed an average of 10 years.
Incident cardiovascular events (myocardial infarction, stroke, coronary revascularization, and cardiovascular death).
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.
Data were available only for women.
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.
The value of adding high-sensitivity C-reactive protein (hsCRP) to a global risk assessment model is unknown.
The authors used the Women's Health Study, a nationwide cohort of 15048 initially healthy women, to develop a cardiovascular disease (CVD) risk prediction model using hsCRP and Framingham risk model predictors. While hsCRP improved overall model fit, the clinical utility of hsCRP in terms of reclassification was most substantial for those with a 5% or greater 10-year risk based on traditional risk factors.
The study does not address the clinical value of lowering hsCRP level.
In this largely low-risk population, adding hsCRP to the Framingham model reclassified patients into groups that better reflected their actual CVD risk. This effect was most clinically relevant for those at intermediate risk.
Table 1. Best-Fitting Global Cardiovascular Risk Prediction Model among the Model Derivation Cohort of 15048 Women from the Women's Health Study
Relative risk (RR) of future cardiovascular events according to baseline high-sensitivity C-reactive protein (hsCRP) levels in the model derivation cohort (n= 15048), adjusted for Framingham covariables.
Cardiovascular point scoring system for women based on Framingham covariables and high-sensitivity C-reactive protein (hsCRP).
Table 2. Relative Contribution of Individual Framingham Covariables and High-Sensitivity C-Reactive Protein to Global Cardiovascular Risk
Calibration curves for risk prediction models without (top) and with (bottom) high-sensitivity C-reactive protein (hsCRP) in the model.
Appendix Table. Comparison of Discrimination and Calibration for Global Risk Prediction Models with and without High-Sensitivity C-Reactive Protein
Table 3. Observed and Expected Risks among all 26927 Nondiabetic Women in the Women's Health Study Using the Final Global Risk Prediction Model with and without High-Sensitivity C-Reactive Protein
The likelihood ratio chi-square provides a global test of model fit. It is a function of the degrees of freedom, or number of terms in the model. The difference between chi-square values provides a test of the model improvement with hsCRP (P< 0.0001 for both the WHS and ATP III models).
The Bayes information criterion is a function of the log likelihood but adds a penalty for added variables based on the sample size (28). It is not influenced by the number of predictors, so models can thus be compared directly. Lower values reflect better fit, suggesting improvement with the addition of hsCRP.
The Bayes information criterion weight provides an estimate of the posterior probability of each model given the set of candidate models considered (29, 32). The weights suggest a much higher probability that the WHS model that includes hsCRP is correct.
The Akaike information criterion is a function of the log likelihood that adds a penalty of 2 for each added variable (32), less extreme than the penalty used in the Bayes information criterion. Lower values are better, again suggesting improvement with hsCRP.
The Akaike information criterion weights reflect the relative likelihood of a model given the data and the set of models (32). These weights display a clear preference for the models with hsCRP.
Nagelkerke's generalized model R2(33, 34) is a measure of the fraction of the−2 log likelihood explained by the predictors, analogous to the percentage of variance explained in a linear model. It is adjusted to a range of 0 to 1 and is higher for models with hsCRP, both in the original data and after adjustment for optimism using the bootstrap (31, 48).
The D-statistic of Royston and Sauerbrei (35) measures the separation of survival curves across levels of the predictor variables, analogous to distance between Kaplan–Meier curves. This is higher for models that included hsCRP, even after adjustment for optimism, suggesting better prediction for these models.
The Brier score (28) computes the sum of squared differences between the observed outcome and the fitted probability. It is lower for models that included hsCRP, indicating that the predicted probabilities are closer to the observed outcomes.
The c-index represents the area under the receiver-operating characteristic curve (30), allowing for censored data. This is a measure of discrimination based on ranks and is similar but slightly higher for models that included hsCRP, even after adjustment for optimism. The c-statistic is the probability that, for a randomly selected pair of subjects, one diseased and the other nondiseased, the person with disease will have the higher estimated disease probability according to the model.
The Hosmer–Lemeshow calibration statistic (36) classifies predicted probabilities into categories and compares the mean predicted probability with the observed risk within each category. A P value representing a significant difference indicates a lack of fit. When decile categories are used, the predicted probability is less than 5% for the first 9 of 10 categories. Calibration is adequate for all models that use this measure and is somewhat better for models without hsCRP. The calibration statistic based on risk percentage compares observed and predicted risk by using 10 categories based on 2–percentage point increments in predicted risk, from 0% to 2% risk to 18% or greater risk. This statistic indicates significant deviation of observed and predicted values in models without hsCRP, suggesting a lack of fit in higher-risk categories.
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Nancy R. Cook, Julie E. Buring, Paul M Ridker. The Effect of Including C-Reactive Protein in Cardiovascular Risk Prediction Models for Women. Ann Intern Med. 2006;145:21–29. doi: 10.7326/0003-4819-145-1-200607040-00128
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Published: Ann Intern Med. 2006;145(1):21-29.
Cardiology, Coronary Risk Factors, Dyslipidemia, Prevention/Screening.
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