Current Cardiovascular Disease Risk Scoring Systems Effective for Black Patients

The American College of Cardiology/American Heart Association and Framingham Risk Score algorithms are effective for black patients.

Current American College of Cardiology and American Heart Association (ACC/AHA) and Framingham Risk Score (FHS) algorithms for cardiovascular disease (CVD) risk among black individuals are effective and not easily improved on, according to data from the Jackson Heart Study.

The results suggest that a unique risk calculator may not be necessary in this patient population.

The Jackson Heart Study is a longitudinal community-based study that included 5301 black adults from Jackson, Mississippi who were followed for median of 9.1 years. The study was designed to validate risk prediction models for CVD incidence in black individuals.

“Establishing such a risk prediction model in black adults that focuses on all CVD events may allow for the formulation of prevention strategies that may contribute to lowering the burden of disease in this group and address the disparities in morbidity and mortality in the vulnerable segment of the US population,” the authors wrote.

An incident CVD event was defined as the first occurrence of 1 of the 4 major outcomes: myocardial infarction, coronary heart disease death, congestive heart failure, and stroke, or 1 of the 2 non-major outcomes: incident angina or intermittent claudication.

Researchers also compared the model performance of the FHS and ACC/AHA algorithms with the data from the Jackson Heart Study. Model performance was evaluated in the ARIC (Atherosclerosis Risk in Communities) and MESA (Multi-Ethnic Study of Atherosclerosis) cohorts. Four models were considered for evaluation: standard risk factors, standard risk factors and blood biomarkers combined, standard risk factors and measures of subclinical disease combined, and the final model combined all 3 models.

Parsimonious models were developed from analyses of the different tiers.

The standard CVD risk factors model retained age, male sex, systolic blood pressure (BP), antihypertensive therapy, ratio of fasting total cholesterol to high-density lipoprotein cholesterol (HDL-C), estimated glomerular filtration rate, type 2 diabetes status, and smoking status. Triglycerides and diastolic BP were dropped based on statistical significance.

The standard risk factors and blood biomarkers model retained B-type natriuretic peptide (BNP; P<.001), HOMA-IR (homeostasis model assessment of insulin resistance; P=.009), adiponectin (P=.002), and glycated hemoglobin (P=.05).

In the standard risk factors and subclinical disease model, carotid intimal-medial thickness was the only eliminated measure. Left ventricle (LV) systolic dysfunction (P<.001), LV hypertrophy (P=.001), and ABI (P=.005) were included in the final model.

The fourth model that combined all 3 models retained the following variables: age, ratio of fasting total cholesterol to HDL-C, smoking status, BNP, LV ejection fraction, LV hypertrophy, and ABI.

At the 9.1 year median, 270 participants (166 women) had experienced a first CVD event. The combination model with standard CVD factors, BNP, and ABI yielded only modest improvement over a model without these factors (C statistic: 0.79; 95% confidence interval [CI]: 0.75-0.83 [relative integrated discrimination improvement: 0.22; 95% CI: 0.15-0.30]).

Incident myocardial infarction and fatal coronary heart disease occurred in 92 individuals (2.7; 95% CI: 2.2-2.3 events per 1000 person-years), congestive heart failure occurred in 104 individuals (3.1; 95% CI: 2.5-3.7 events per 1000 person-years), and stroke occurred in 75 individuals (2.2; 95% CI: 1.8-2.8 events per 1000 person-years).

However, researchers found that the reclassification improvement was not considerably different between the combined model (standard CVD factors, BNP, and ABI) and the ACC/AHA CVD Pooled Cohort risk equation (C statistic: 0.75; 95% CI: 0.71-0.79) or the FHS equation (C statistic: 0.77; 95% CI: 0.73-0.81).

They concluded that the ACC/AHA and FHS equations are effective tools to calculate risk in black individuals, and that these algorithms are not easily improved on.

“Additional investigation is warranted to identify the comparative accuracy of CVD prediction models incorporating standard risk factors, blood biomarkers, and measures of subclinical disease in other race/ethnicities,” they noted.


Fox ER, Samdarshi TE, Musani SK, et al. Development and validation of risk prediction models for cardiovascular events in black adults: The Jackson Heart Study cohort. JAMA Cardiol. 2016;1(1):15-25. doi: 10.1001/jamacardio.2015.0300.