Predictive Models of a Future Coronary Artery Calcium Score in a Black Cohort: The GENOA Study

This retrospective study of a Black cohort sought to create predictive models to calculate the probability of a coronary artery calcium score (CACS) about one decade after obtaining cardiovascular risk measures.

Participants (n = 656) in GENOA had CV risk variables measured (1995-2000) and a CACS about one decade later (2009-2011). Using multivariate regression, computer models were written to calculate the probability of a future CACS of zero, ≥ 10, and ≥ 100. ROC values were 0.78, 0.77, and 0.76, respectively. Machine learning models did not perform any better than multivariate regression.

Age, height, smoking duration, sex, and hypertension were significant for all three models in predicting a future CACS. Height was inversely related to future CACS, but weight and BMI were not contributory to the models. Lipid-lowering medications and exercise were associated with an increased CACS, the so-called CACS "paradox."

Predictive models of a future CACS such as these may help in identifying important risk factors for a future CACS. By identifying these risk factors and implementing early modification of CV risk factors, the development of CV disease may be slowed.