C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men

Background— High-sensitivity C-reactive protein and family history are independently associated with future cardiovascular events and have been incorporated into risk prediction models for women (the Reynolds Risk Score for women); however, no cardiovascular risk prediction algorithm incorporating these variables currently exists for men. Methods and Results— Among 10 724 initially healthy American nondiabetic men who were followed up prospectively over a median period of 10.8 years, we compared the test characteristics of global model fit, discrimination, calibration, and reclassification in 2 prediction models for incident cardiovascular events, one based on age, blood pressure, smoking status, total cholesterol, and high-density lipoprotein cholesterol (traditional model) and the other based on these risk factors plus high-sensitivity C-reactive protein and parental history of myocardial infarction before age 60 years (Reynolds Risk Score for men). A total of 1294 cardiovascular events accrued during study follow-up. Compared with the traditional model, the Reynolds Risk Score had better global fit (likelihood ratio test P<0.001), a superior (lower) Bayes information criterion, and a larger C-index (P<0.001). For the end point of all cardiovascular events, the Reynolds Risk Score for men reclassified 17.8% (1904/10 724) of the study population (and 20.2% [1392/6884] of those at 5% to 20% 10-year risk) into higher- or lower-risk categories, with markedly improved accuracy among those reclassified. For this model comparison, the net reclassification index was 5.3%, and the clinical net reclassification index was 14.2% (both P<0.001). In models based on the Adult Treatment Panel III preferred end point of coronary heart disease and limited to men not taking lipid-lowering therapy, 16.7% of the study population (and 20.1% of those at 5% to 20% 10-year risk) were reclassified to higher- or lower-risk groups, again with significantly improved global fit, larger C-index (P<0.001), and markedly improved accuracy among those reclassified. For this model, the net reclassification index was 8.4% and the clinical net reclassification index was 15.8% (both P<0.001). Conclusions— As previously shown in women, a prediction model in men that incorporates high-sensitivity C-reactive protein and parental history significantly improves global cardiovascular risk prediction.

[1]  Anders Larsson,et al.  Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. , 2008, The New England journal of medicine.

[2]  Nancy R Cook,et al.  Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929) , 2008, Statistics in medicine.

[3]  N. Cook Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. , 2008, Clinical chemistry.

[4]  M. Woodward,et al.  Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC) , 2005, Heart.

[5]  S. Shaughnessy,et al.  Do No Harm: Health Systems’ Duty to Promote Clinician Well-Being , 2022, American Journal of Hospital Medicine.

[6]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[7]  Neil J Stone,et al.  Implications of Recent Clinical Trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines , 2004, Circulation.

[8]  C. Hennekens,et al.  Design of Physicians' Health Study II--a randomized trial of beta-carotene, vitamins E and C, and multivitamins, in prevention of cancer, cardiovascular disease, and eye disease, and review of results of completed trials. , 2000, Annals of epidemiology.

[9]  J. Ware,et al.  Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929) , 2008, Statistics in medicine.

[10]  S. Grundy,et al.  Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. , 2004, Arteriosclerosis, thrombosis, and vascular biology.

[11]  G. Mancia,et al.  Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and other Societies on Coronary Prevention. , 1998, Atherosclerosis.

[12]  E. Rimm,et al.  A Prospective Study of Nutritional Factors and Hypertension Among US Men , 1989, Circulation.

[13]  N. Cook,et al.  Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. , 2007, JAMA.

[14]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[15]  N. Cook Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction , 2007, Circulation.

[16]  M. Pencina,et al.  General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study , 2008, Circulation.

[17]  N. Unwin,et al.  Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Detection, Evaluation, and Treatment of High Blood Cholesterol Education Program (NCEP) Expert Panel on Executive Summary of the Third Report of the National , 2009 .

[18]  Joseph G. Pigeon,et al.  An Improved Goodness of Fit Statistic for Probability Prediction Models , 1999 .

[19]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[20]  Nancy R Cook,et al.  The Effect of Including C-Reactive Protein in Cardiovascular Risk Prediction Models for Women , 2006, Annals of Internal Medicine.

[21]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[22]  R. McPherson,et al.  Canadian Cardiovascular Society position statement--recommendations for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease. , 2006, The Canadian journal of cardiology.

[23]  P. Ridker Rosuvastatin in the primary prevention of cardiovascular disease among patients with low levels of low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: rationale and design of the JUPITER trial. , 2003, Circulation.

[24]  J. Hippisley-Cox,et al.  Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study , 2007, BMJ : British Medical Journal.

[25]  Ralph B. D'Agostino,et al.  Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures , 2003, Advances in Survival Analysis.

[26]  J. Buring,et al.  Development of Predictive Models for Long-Term Cardiovascular Risk Associated With Systolic and Diastolic Blood Pressure , 2002, Hypertension.

[27]  J. Mckenney,et al.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). , 2001, JAMA.