Abstract
Coronary artery calcium (CAC) is a marker of subclinical atherosclerosis that confers increased risk of atherosclerotic cardiovascular disease. Measured by noncontrast cardiac computed tomography, CAC improves risk stratification beyond traditional risk factors and can aid decision-making for allocation of preventive treatments. Although national guidelines recommend consideration of CAC measurement for >17 million individuals in the United States with borderline to intermediate 10-year atherosclerotic cardiovascular disease risk, adoption has been limited. A promising approach to bridge this gap is opportunistic detection of CAC using non-ECG-gated chest computed tomography scans that are performed for a noncardiac indication. Approximately 19 million non-ECG-gated chest computed tomography scans are performed per year, and reporting opportunistic detection of CAC from these scans can enhance atherosclerotic cardiovascular disease risk stratification without additional radiation exposure, cost, or burden. Estimation of risk by traditional risk factor scoring is underused, and reporting of opportunistic detection of CAC has the potential to alert physicians of risk, independent of guideline-recommended risk calculator use. Advancements in artificial intelligence allow integration of automated CAC quantification into clinical practice. Several artificial intelligence algorithms are in use to improve the likelihood of reporting opportunistic detection of CAC and appropriate allocation of preventive therapies. Systematic approaches are needed to ensure appropriate reporting, interpretation, and action while avoiding unnecessary downstream testing. Implementation that includes tailored preventive care and streamlined care pathways involving multidisciplinary clinical teams including radiology, cardiology, and primary care is essential.