Abstract Body (Do not enter title and authors here): Background:
Quantitative coronary plaque analysis from coronary computed tomographic angiography (CCTA) is a promising strategy for individualized cardiovascular disease (CVD) prevention. More population-level data is needed on how plaque burden can inform lipid lowering strategies to reduce CVD risk reduction.

Objectives:
To evaluate the prognostic utility of a total plaque volume (TPV)-based risk staging system and model its use in guiding lipid-lowering therapy in real-world patients undergoing clinically indicated CCTAs for evaluation of chest pain.

Methods:
We analyzed adult patients across a single-center NHS site who underwent clinically indicated CCTA with available AI-based quantitative plaque analysis. TPV was categorized into four risk stages (DECIDE 1–4) using predefined thresholds (Table 1). The primary outcome was cardiac death or non-fatal MI. Secondary analyses reclassified prior myocardial infarction (MI) or early revascularization into DECIDE Stage 4. We modeled lipid-lowering strategies using both fixed-intensity treatment by DECIDE stage and stage-specific LDL-C goals to estimate risk reduction and number needed to treat (NNT) over 3-to-10-year durations.

Results:
Among the 2,827 patients, mean (SD) age was 58 (13) years, and 51.1% were female. Higher TPV stages were associated with progressively increased risk of CV death or MI (1.7%, 4.9%, 7.4%, 11.1% for stages 1–4, respectively) (Figure 1). The fixed intensity strategy yielded a 10-year NNT of 52, which improved to 42 when using a stage-specific LDL-C goal strategy guided by plaque burden.

Conclusions:
Quantitative plaque burden measured by AI-enabled CCTA identifies patients at elevated long-term cardiovascular risk and may inform a personalized lipid-lowering strategy to mitigate risk.