ArticleAmerican journal of preventive cardiology2026
Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS study
Article in American journal of preventive cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- The coming age of coronary atherosclerosis-guided cardiovascular risk prediction and management.American journal of preventive cardiology · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
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: 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 Results: 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. Conclusions: 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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Registered trials
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