The Effect of Including C-Reactive Protein in Cardiovascular Risk Prediction Models for Women

Context The value of adding high-sensitivity C-reactive protein (hsCRP) to a global risk assessment model is unknown. Contribution 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. Cautions The study does not address the clinical value of lowering hsCRP level. Implications 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. The Editors The Framingham risk model (1) is used extensively for detecting risk for coronary heart disease and has been adapted by the Adult Treatment Panel III (ATP III) of the National Cholesterol Education Program (2). The traditional risk factors included are strong predictors of cardiovascular risk, and the model has been validated in several populations (3). However, despite the model's success, up to 20% of all coronary events occur in the absence of any major risk factor (4, 5). In addition, most individuals who do not develop coronary heart disease have at least 1 clinically elevated Framingham risk factor (6). Given these modest levels of sensitivity and specificity, research over the past decade has focused on novel blood-based atherosclerotic risk factors that, like cholesterol, can be inexpensively obtained and interpreted in the primary care setting. One of the most promising of these is high-sensitivity C-reactive protein (hsCRP), a biomarker of inflammation that has consistently been shown to predict incident myocardial infarction, stroke, and cardiovascular death among apparently healthy men and women after adjustment for all components of the Framingham risk score (7-16). Blood levels of hsCRP also correlate with hypofibrinolysis and abnormal glucose metabolism, and thus reflect pathophysiologic processes that are related to vascular occlusion but are not easily measured with traditional risk factors (17-20). On that basis, in 2003 the Centers for Disease Control and Prevention and the American Heart Association published the first set of guidelines to endorse the use of hsCRP as a potential adjunct to traditional risk factor screening (21). Despite these data, no simple clinical algorithm that includes Framingham covariables and hsCRP has been developed, and thus it has not been possible to determine whether individuals might be more accurately classified if hsCRP were added to global risk prediction models for major cardiovascular disease (CVD), including myocardial infarction, coronary revascularization, stroke, and cardiovascular death (22). Methods We compared the clinical utility of global cardiovascular risk prediction models based on Framingham covariables with and without hsCRP among participants in the Women's Health Study (WHS) (23-25), a large-scale, nationwide cohort of U.S. women age 45 years and older who were free of CVD and cancer at study entry. Women were followed annually for the development of CVD, with an average follow-up of 10 years. All reported CVD outcomes, including myocardial infarction, ischemic stroke, coronary revascularization procedures, and deaths from cardiovascular causes, were adjudicated by an end points committee after medical record review. All study participants provided written informed consent, and the study protocol was approved by the institutional review board of Brigham and Women's Hospital in Boston, Massachusetts. Baseline blood samples were assayed for C-reactive protein with a validated, high-sensitivity assay (Denka Seiken, Tokyo, Japan) and for total, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol with direct-measurement assays (Roche Diagnostics, Basel, Switzerland). Women who were diabetic at baseline were excluded from predictive modeling because ATP III labeling considers diabetes to be a risk equivalent for coronary heart disease (2). In parallel with guidelines established for lipid evaluation (26), models were initially fitted in a derivation cohort limited to women not taking hormone replacement therapy at baseline (n= 15048 with data on all variables) and were then applied to all nondiabetic women (n= 26927) for clinical risk prediction. Development of Risk Prediction Models Models were fitted by using Cox proportional hazards models (27), restricting predictors to components of the Framingham risk score (including either total or LDL cholesterol as well as HDL cholesterol), and adding hsCRP. To determine the functional form used for each predictor, we examined spline plots and fitted power functions to determine the best fit for each variable. When fit was similar, we chose the simplest form, usually a linear term or log transformation, especially when supported by previous data from existing ATP III algorithms. To model blood pressure more fully, we included a nonlinear term for systolic blood pressure. As in the Framingham score, we included antihypertensive medication use and considered its interaction with blood pressure. At baseline in the WHS, use of cholesterol-lowering medications was rare, was not composed of statins, and was not statistically significant in these models. As also in the Framingham score, current but not past smoking was included. We considered interactions of all predictors with smoking and age, particularly on the basis of inclusion in the ATP III risk score. To enhance model simplicity, these were not included when they were only of marginal statistical significance. Of interest, when body mass index was added to the final model, it was not a statistically significant predictor of CVD in these data, an observation consistent with its absence from the Framingham risk score. In addition to comparing the performance of the final model derived from the WHS data with hsCRP to the WHS model without hsCRP, we also compared the performance of the formal ATP III model (2), with and without hsCRP (Appendix). The ATP III model includes terms for the natural logarithms of age, of total and HDL cholesterol, and of systolic blood pressure and was developed for the prediction of hard coronary heart disease events, including myocardial infarction and death from coronary heart disease. Total CVD, including ischemic stroke as well as revascularization procedures, was used as the end point in these analyses. To be conservative and allow the best possible fit for all traditional covariables, we recalculated -coefficients for the ATP III model in the WHS data before evaluating any additive effects of hsCRP. Measures of Model Fit The primary means of comparing predictive models based on Framingham covariables with and without hsCRP was the Bayes information criterion, a likelihood-based measure that adds a penalty for model complexity (28, 29). Lower values indicate better fit. Because of its common use in the medical literature, we also computed the c-index (28), or concordance probability, which is a generalization of the c-statistic, or the area under the receiver-operating characteristic curve (30), that allows for censored data. For these analyses, the c-index was computed and adjusted for optimism due to overfitting with bootstrap sampling (31) using the Design library in S-PLUS software (Insightful Corp., Seattle, Washington) (28). For comparison, we also computed several other measures of global model fit (provided in the Appendix), including other likelihood-based measures such as model weights for the Bayes information criterion, which provide an estimate of the posterior probability of each model given the set of candidate models considered (29, 32); the Akaike information criterion and its corresponding model weights (32); and Nagelkerke's generalized model R2 (33, 34). We computed the D-statistic of Royston and Sauerbrei (35), based on the separation of survival curves by predictor variables, and again adjusted for optimism. Differences in statistics between nested models were tested with a 1-sided test using bootstrap sampling (31). We also calculated the Brier score (28), which directly compares the observed outcomes with the fitted probabilities. To assess model calibration, or how closely the predicted probabilities reflect actual risk, observed risk was calculated on the basis of 8 years of follow-up (available for all participants) and was extrapolated to 10 years for display purposes. We computed the HosmerLemeshow calibration statistic (36) comparing observed and predicted risk using 10 categories based on 2percentage point increments in predicted risk, ranging from less than 2% to 18% or greater. We also computed this statistic using decile categories of predicted probabilities. To address clinical utility, we directly compared predicted risk estimates that are based on models using Framingham covariables with and without hsCRP with actual risk that was observed during study follow-up among all 26927 women for whom data were available. We used weighted statistics (37) to compare the predicted probabilities with and without hsCRP. To approximate clinical criteria commonly used in current treatment guidelines (2, 38), we grouped the predicted probabilities into 10-year risk categories of 0% to less than 5%, 5% to less than 10%, 10% to less than 20%, and 20% or greater. Finally, to address the generalizability of the final WHS risk prediction model with hsCRP, we calibrated the predicted probabilities to observed risk in the Framingham Heart Study, using a limited-access data set available from the National Heart, Lung, and Blood Institute at www.nhlbi. nih.gov/resources/deca/default.htm. Mean values among women age 47 years and o