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Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine Learning Approach in the MESA Study.

Background: Coronary artery calcium (CAC) has been widely recognized as an important predictor of cardiovascular disease (CVD). Given the finite resources, it is important to identify individuals who would receive the most benefit from detecting positive CAC by screening. However, the evidence is limited as to whether the burden of positive CAC on CVD differs by multi-dimensional individual characteristics. We sought to investigate the heterogeneity in the association between positive coronary artery calcium (CAC) and incident cardiovascular disease (CVD). Methods: This cohort study included adults aged ≥45 years free of cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis. After propensity score matching in a 1:1 ratio, we applied a machine-learning causal forest model to (i) evaluate the heterogeneity in the association between positive CAC and incident CVD and (ii) predict the increase in CVD risk at 10-year when CAC>0 (vs. CAC=0) at the individual level. We then compared the estimated increase in CVD risk when CAC>0 to the absolute 10-year atherosclerotic CVD (ASCVD) risk calculated by the 2013 ACC/AHA pooled cohort equations. Results: Across 3,328 adults in our propensity score-matched analysis, our causal forest model showed the heterogeneity in the association between CAC>0 and incident CVD. We found a dose-response relationship of the estimated increase in CVD risk when CAC>0 with higher 10-year ASCVD risk. Almost all individuals (2293/2428 [94.4%]) with borderline or higher ASCVD risk showed ≥2.5% increase in CVD risk when CAC>0. Even among 900 adults with low ASCVD risk, 689 (69.2%) showed ≥2.5% increase in CVD risk when CAC>0; these individuals were more likely to be male, Hispanic, and have unfavorable CVD risk factors than others. Conclusions: The expected increases in CVD risk when CAC>0 were heterogeneous across individuals. Moreover, nearly 70% of people with low ASCVD risk showed a large increase in CVD risk when CAC>0, highlighting the need for CAC screening among such low-risk individuals. Future studies are needed to assess whether targeting individuals for CAC measurements based on not only the absolute ASCVD risk but also the expected increase in CVD risk when CAC>0 improves cardiovascular outcomes.