OPPORTUNISTIC ASSESSMENT OF CARDIOVASCULAR RISK USING AI-DERIVED STRUCTURAL AORTIC AND CARDIAC PHENOTYPES FROM NON-CONTRAST CHEST COMPUTED TOMOGRAPHY
Background Primary prevention of cardiovascular disease relies on accurate risk assessment using scores such as the Pooled Cohort Equations (PCE) and PREVENT. However, necessary input variables for these scores are often unavailable in the electronic health record (EHR), and information from routinely collected data (e.g., non-contrast chest CT) may further improve performance. Here, we test whether a risk prediction model based on structural features of the heart and aorta from chest CT has added value to existing clinical algorithms for predicting major adverse cardiovascular events (MACE). Methods We developed a LASSO model to predict fatal MACE over 12 years of follow-up using structural radiomics features describing cardiac chamber and aorta segmentations from 13,437 lung cancer screening chest CTs from the National Lung Screening Trial. We compared this radiomics model to the PCE and PREVENT scores in an external testing set of 4,303 individuals who had a chest CT at a Mass Gener
