Astronaut Cardiovascular Health and Risk Modification (Astro-CHARM) Coronary Calcium Atherosclerotic Cardiovascular Disease Risk Calculator

BackgroundCoronary artery calcium (CAC) is a powerful novel risk indicator for atherosclerotic cardiovascular disease (ASCVD). Currently, there is no available ASCVD risk prediction tool that integrates traditional risk factors and CAC.MethodsTo develop a CAC ASCVD risk tool for younger individuals in the general population, subjects aged 40 to 65 without prior cardiovascular disease from 3 population-based cohorts were included. Cox proportional hazards models were developed incorporating age, sex, systolic blood pressure, total and high-density lipoprotein cholesterol, smoking, diabetes mellitus, hypertension treatment, family history of myocardial infarction, high-sensitivity C-reactive protein, and CAC scores (Astro-CHARM model [Astronaut Cardiovascular Health and Risk Modification]) as dependent variables and ASCVD (nonfatal/fatal myocardial infarction or stroke) as the outcome. Model performance was assessed internally, and validated externally in a fourth cohort.ResultsThe derivation study comprised 7382 individuals with a mean age 51 years, 45% women, and 55% nonwhite. The median CAC was 0 (25th, 75th [0,9]), and 304 ASCVD events occurred in a median 10.9 years of follow-up. The c-statistic was 0.784 for the risk factor model, and 0.817 for Astro-CHARM ( P<0.0001). In comparison with the risk factor model, the Astro-CHARM model resulted in integrated discrimination improvement (0.0252), and net reclassification improvement (0.121; P<0.0001), as well. The Astro-CHARM model demonstrated good discrimination (c=0.78) and calibration (Nam-D'Agostino χ2, 13.2; P=0.16) in the validation cohort (n=2057; 55 events). A mobile application and web-based tool were developed to facilitate clinical application of this tool ( www.AstroCHARM.org ).ConclusionThe Astro-CHARM tool is the first integrated ASCVD risk calculator to incorporate risk factors, including high-sensitivity C-reactive protein and family history, and CAC data. It improves risk prediction in comparison with traditional risk factor equations and could be useful in risk-based decision making for cardiovascular disease prevention in the middle-aged general population