Introduction:
Coronary artery calcium (CAC) is a robust predictor of coronary events in asymptomatic individuals with latent atherosclerotic cardiovascular disease (CVD). While evidence suggests CAC scoring may augment traditional CVD risk scores in clinical decision making, evidence is limited on the compared ability of CVD risk scores to identify the degree of coronary atherosclerosis as quantified by absolute CAC, particularly in former elite athlete populations. We investigated this in a cohort of retired National Football League (NFL) players.
Methods:
We analyzed data on 752 retired NFL players (aged 55.2 ± 9.0 years, 53.7% African-American] that underwent health screening and CAC scoring with the NFL Player Care Foundation. Three 10-year CVD risk scores were compared: Framingham Coronary Heart Disease (FCHD), Framingham CVD (FCVD), and Atherosclerotic CVD Risk Pooled Cohort Equations (PCE). Receiver operating characteristic curves were fitted in 3 models: FCHD (Model 1), FCVD (Model 2), and PCE (Model 3, used as reference based on 2013 AHA guidelines). Contrast analyses identified the model with highest discriminative ability (c statistic) versus CAC = 0 for each CAC score category: >0 and <100, 100-400, and >400.
Results:
Compared to PCE
,
FCVD exhibited the highest discriminative ability for CAC > 0 and < 100 (
c statistic
0.7071 vs 0.6706, p<0.0001), while FCHD had the lowest for both CAC 100-400 (
c statistic
0.7198 vs 0.7664, p=0.0165) and CAC >400 (
c statistic
0.7728 vs 0.8460, p<0.0001). No additional differences were identified (Figure 1).
Conclusion:
Traditional CVD risk scores differ in performance to predict absolute CAC among retired NFL players, underscoring a need for refinement of coronary event risk prediction models to enhance the ability of such models to identify, specifically, low CAC, as even low CAC burden confers increased risk compared to CAC absence. This may include accounting for elite athlete-specific characteristics.
Value of addition of coronary artery calcium to risk scores in the prediction of major cardiovascular events in patients with type 2 diabetes
