Opportunistic Assessment of Coronary Artery Calcium Volume and Density From Non-Electrocardiogram-Gated Chest CT Using Artificial Intelligence: Prognostic Implications in a Screening Cohort

Abstract

Objective: The prognostic value of coronary artery calcium (CAC) volume and density was derived from an automated artificial intelligence (AI)-based analysis of non-electrocardiogram-gated chest CT. Materials and Methods: In this retrospective study, 7,552 asymptomatic adults who underwent chest CT as part of a national health screening program between 2007 and 2014 at two tertiary hospitals were examined for eligibility, of whom 1,109 with detectable CAC were analyzed. CAC density was derived by back-calculation from the Agatston score and CAC volume, both of which were obtained using AI software on chest CT. Differences in the probability of being free from major adverse cardiovascular events (MACE) across the four combined CAC volume-density groups were assessed using Kaplan-Meier curves and restricted mean survival time (RMST). Multivariable Cox proportional hazards models were used to assess the association between CAC volume and density and MACE. Results: Among the 1,109 participants with nonzero CAC (median age, 60.3 years; 87% men), 207 experienced MACE during a median follow-up of 7.7 years. Ten-year RMSTs were 9.45 years in the low-volume-high-density group, 9.07 years in the lowvolume-low-density group, 8.03 years in the high-volume-high-density group, and 7.68 years in the high-volume-low-density group. Differences in time to MACE were predominantly driven by CAC volume, with no significant density-related differences within the volume strata. CAC density demonstrated a significant, independent, inverse association after adjusting for CAC volume and clinical covariates (hazard ratio [HR] per increase by standard deviation [SD], 0.786; 95% confidence interval [CI], 0.659-0.936; P = 0.007). CAC volume also remained independently associated with an increased risk of MACE (HR per increase Conclusion: CAC density derived from chest CT using automated AI quantification was independently and inversely associated with MACE, providing additional prognostic value when added to CAC volume.