Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS study✰

Background: Coronary computed tomographic angiography (CCTA) is a guideline-endorsed tool to evaluate coronary artery disease (CAD) in symptomatic patients. Artificial intelligence enabled quantitative coronary plaque analysis on CCTA (AI-CPA) is a promising strategy for tailored management of atherosclerotic cardiovascular disease (ASCVD). Population-level data are needed on how CCTA-derived plaque analyses can inform lipid-lowering strategies for ASCVD risk reduction. Objectives: To model the utility and efficiency of a total plaque volume (TPV)-based risk staging system in guiding lipid-lowering therapy in patients undergoing clinically-indicated CCTAs for evaluation of stable suspected or known CAD. Methods: for DECIDE stages 1-4, respectively. The primary outcome was the estimated reduction in cardiovascular death or non-fatal MI, and the number needed to treat (NNT) based on AI-CPA, over 10 years. We modeled lipid-lowering therapy utilizing treat-to-target LDL-C goals of