Key result
Modeled AI-guided lipid-lowering therapy reduces estimated 10-year cardiovascular risk by ~19% overall.
Why the study?
Population-level data were needed on how CCTA-derived plaque analyses can inform lipid-lowering strategies for ASCVD risk reduction.
Does AI-enabled total plaque volume risk staging guide effective lipid-lowering therapy to reduce cardiovascular death or non-fatal MI in symptomatic patients undergoing CCTA?
Does AI-enabled total plaque volume risk staging guide effective lipid-lowering therapy to reduce cardiovascular death or non-fatal MI in symptomatic patients undergoing CCTA?
AI-enabled quantitative coronary plaque analysis on CCTA can identify patients at elevated long-term cardiovascular risk and efficiently guide personalized lipid-lowering therapy to reduce events.
May support AI plaque staging to guide lipid therapy post-CCTA; hypothesis-generating pending randomized outcome trials.
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. 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. We analyzed adult patients from the Computed Tomography Angiography Helps/Hinders Improve Patient care and Societal costs (FISH&CHIPS) across 2 sites in the UK who underwent a clinically-indicated CCTA with AI-based quantitative plaque analysis. TPV was categorized into four risk stages using pre-defined thresholds of 1–100, 101–250, 251–750, and >750 mm 3 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 <100, <70, <55, and <40 mg/dL for DECIDE stages 1–4, respectively, to estimate risk reduction and NNT over 10 years. The study population included 7899 total symptomatic participants undergoing CCTA and AI-CPA. Of these, 6054 patients had any plaque and were included in the final cohort; the mean age was 59.4 ± 11.7 years and 42.7% were women. Among the full cohort, the 10-year modeled relative risk reduction using a TPV treat-to-target LDL-C was 19.1% with NNT of 61. The 10-year relative risk reduction and NNT by DECIDE stages 1–4 was 1.5% (NNT = 1686), 18.2% (NNT = 59), 24.2% (NNT = 27), and 33.8% (NNT = 11), respectively. Quantitative TPV measured by AI-CPA identifies symptomatic patients at elevated long-term cardiovascular risk and may efficiently inform implementation of personalized lipid-lowering strategies to reduce cardiovascular events.
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Parsa et al. (2026) studied this question. AI-quantified total plaque volume guided lipid-lowering therapy reduced 10-year cardiovascular risk by 19.1% overall, with NNT of 61, improving to 33.8% risk reduction and NNT of 11 at highest risk st.
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