Evaluating the Implications of a Coronary Artery Calcium Score of Zero (CAC = 0) in Modern Risk Prediction: Is Zero Truly Zero?

Coronary artery calcium (CAC) scoring is a well-validated screening tool used to calculate the amount of calcified plaque deposited in coronary arteries from a computed tomography (CT) scan. It stratifies patients by risk to predict their future probability of cardiovascular disease and helps establish the ideal preventive therapies. Considering these factors, the purpose of this narrative review is to evaluate the latest research on the effectiveness of CAC scoring, explore the various limitations and challenges faced in utilizing this tool, and discuss alternative investigations commonly used to supplement it for risk stratification. To achieve this, a narrative review was conducted by searching recent literature through databases such as PubMed, Cochrane, and Google Scholar. Identified literature included large population cohort studies and systematic reviews from the last five years, focusing on CAC scoring, cardiovascular risk prediction, ethnicity, artificial intelligence (AI) integration, and secondary prevention. The literature identified generally shows that the validity of CAC scoring is strongly debated due to its variable efficacy in symptomatic versus asymptomatic patients and in the context of ethnic variations, with many studies having supported the validity of this scoring tool, but others challenging its ability to prognosticate cardiovascular disease due to the presence of these external factors, which could lead to an inaccurate representation of the score. As a result, a major recommendation has been to combine the calculated score with pre-existing patient risk factors when making clinical judgments to guide prompt, individualized primary and secondary preventive care. Studies have shown that patients with varying ethnic backgrounds and also those who are symptomatic for stable cardiovascular disease have confounding risk factors that can lead to a false representation of their score and could potentially be at a higher than predicted risk of major cardiovascular events even with a score of zero. In conclusion, the use of CAC scoring remains a valuable prognostic tool for predicting a patient's cardiovascular prognosis; however, its interpretation must consider correlation with clinical, biochemical, and demographic contexts to optimize decision-making. The literature has also identified the potential for improving the precision and effectiveness of major adverse cardiovascular event (MACE) prediction using traditional scoring methods by incorporating AI, including automated scoring tools and calcium-omics models, into CAC scoring.