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Background  Patients with stable coronary heart disease ( CHD ) have widely varying prognoses and treatment options. Validated models for risk stratification of patients with  CHD  are needed. We sought to evaluate traditional and novel risk factors as predictors of secondary cardiovascular ( CV ) events, and to develop a prediction model that could be used to risk stratify patients with stable  CHD .    Methods and Results  We used independent derivation (912 participants in the Heart and Soul Study) and validation (2876 participants in the  PEACE  trial) cohorts of patients with stable  CHD  to develop a risk prediction model using Cox proportional hazards models. The outcome was  CV  events, defined as myocardial infarction, stroke, or  CV  death. The annual rate of  CV  events was 3.4% in the derivation cohort and 2.2% in the validation cohort. With the exception of smoking, traditional risk factors (including age, sex, body mass index, hypertension, dyslipidemia, and diabetes) did not emerge as the top predictors of secondary  CV  events. The top 4 predictors of secondary events were the following: N‐terminal pro‐type brain natriuretic peptide, high‐sensitivity cardiac troponin T, urinary albumin:creatinine ratio, and current smoking. The 5‐year C‐index for this 4‐predictor model was 0.73 in the derivation cohort and 0.65 in the validation cohort. As compared with variables in the Framingham secondary events model, the Heart and Soul risk model resulted in net reclassification improvement of 0.47 (95%  CI  0.25 to 0.73) in the derivation cohort and 0.18 (95%  CI  0.01 to 0.40) in the validation cohort.    Conclusions  Novel risk factors are superior to traditional risk factors for predicting 5‐year risk of secondary events in patients with stable  CHD .

journal of the american heart association

Traditional Risk Factors Versus Biomarkers for Prediction of Secondary Events in Patients With Stable Coronary Heart Disease: From the Heart and Soul Study