This multi-site study will test whether an opportunistic AI-based CAC screening and notification intervention can improve cholesterol treatment and lower cholesterol levels in adults. The study uses artificial intelligence to detect calcium buildup in heart arteries (coronary artery calcium or CAC) on chest CT scans that patients have already had for other reasons. The study will focus on adults who either have known atherosclerotic cardiovascular disease (ASCVD) or have significant calcium buildup (a CAC score of 100 or higher), and whose cholesterol is not well controlled. It will also evaluate how well this approach can be implemented at scale across multiple health systems. The main questions it aims to answer are: Does notifying patients and their clinicians about incidental CAC increase lipid-lowering therapy(LLT) initiation or intensification? Does the intervention improve Low-Density Lipoprotein(LDL)-cholesterol control and related lipid testing? How does the intervention affect downstream care (e.g., clinic visits, cardiology referrals, and cardiac testing)? Researchers will use an FDA-cleared AI algorithm to quantify CAC on previously performed non-gated chest CT scans and identify eligible participants through the electronic health record. Participants will be randomized to receive CAC notification either right away or after a 6-month delay.
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Lipid-lowering therapy initiation or intensification (LLTI)
Timeframe: 6 months after participant randomization