Abstract 4371006: Identification of High Risk Cases in Coronary Artery Calcium (CAC) Scans based on CAC Score and AI-driven Cardiometabolic Biomarkers: An AI-CVD Study within the Multi-Ethnic Study of Atherosclerosis (MESA)

Introduction: Coronary artery calcium (CAC) scans contain more information than currently reported. The AI-CVD initiative aims to extract actionable opportunististic infomration from a CAC scan to maximize its predictive value beyond the CAC score. We previously reported new AI-CVD algorithms applied to CAC scans for opportunistic measurement of bone mineral density (BMD), cardiac chamber volumes, left ventricular mass, liver steatosis, emphysema and other imaging biomarkers. In this report, we investigate the incremental value of these biomarkers on top of the CAC score for prediction of incident all cardiovascular disease (CVD) events. Methods: We applied AI-CVD to CAC scans from 5798 asymptomatic individuals (52% female, age 62±10 years) in the Multi-Ethnic Study of Atherosclerosis. Liver fat was estimated as the liver attenuation index (LAI) using the percentage of voxels below 40 HU. Phantomless BMD for three consecutive thoracic vertebrae (T2-T4) was calculated using the mean HU. Emphysema was estimated using the percentage of voxels within the entire lung in the field of view below -950 HU. We used Kaplan–Meier cumulative incidence curves to evaluate the incremental prognostic value of opportunistically derived biomarkers beyond the Agatston CAC score, using the highest quartile of risk per predictor. Results: A total of 1173 CVD accrued over 19 years follow-up (median [IQR]: 17.7 [12.9-18.5] years). The top quartile of CAC, LAI, and emphysema were defined as >90, >50.4% voxels below 40 HU, > 4.1% below -950 HU, respectively. The bottom quartile of BMD was defined as <130.1 mg/cc. Individuals in the highest risk quartile of all 4 measures (n=44) experienced 69.0% (95% CI: 57.3%-80.1%) incidence of all CVD events. While individuals with a high Agatston score alone experienced 47.9% (44.6%-51.4%) incidence of CVD events over 19 years. Both low BMD and high LAI revealed incremental CVD risk on top of high CAC scores, while high emphysema measurements did not. Conclusion: Applying AI to CAC scans can extract opportunistic incremental risk information for early detection of patients at risk of CVD events. The clinical utility of incorporating LAI, BMD, and emphysema and other opportunistic findings in CAC scans as part of the AI-CVD initiative to improve CVD risk prediction warrants further investigations.