Abstract

Background: Coronary CT angiography (CCTA) artery plaque characteristics including low attenuation and positive remodeling are associated with future myocardial infarction. Associations between angina symptoms and plaque characteristics are unknown. Objective: To examine relationships between quantitative plaque characteristics and angina in women with nonobstructive plaque using artificial intelligence (AI)-enabled plaque analysis. Methods: A sample of women with angina and CCTA nonobstructive plaque (<50% luminal stenosis) enrolled in the WARRIOR trial (NCT 03417388) was studied with validated AI software (Autoplaque 3.0, Cedars Sinai). Complete coronary plaque composition was expressed as noncalcified (NCP), low attenuation noncalcified plaque (LAP, as NCP<30 Hounsfield Units) and calcified plaque (CP) (Figure). Plaque burden was defined as plaque volume x 100%/vessel volume. Angina was quantified by the Seattle Angina Questionnaire angina frequency domain. Relationships between plaque characteristics and angina frequency at enrollment were evaluated using linear and logistic regression in women with total plaque volume >200mm 3 . Results: Among 72 women (age 61.8±10.1 yrs, BMI 30.3±7.2 kg/m2, LDL-C 86.5±27.9, HDL 55.8±17.3, total cholesterol 164.2±37.7 mg/dl, respectively), total plaque volume was 460.1±192.8mm 3 and NCP burden was 50.3± 7.1%. By linear regression, worsening angina frequency was related to increasing NCP burden (β=-0.76, p=0.011). In a logistic regression model, every unit increase in NCP burden was related to ~11% excess risk (1.108, 1.023-1.2, p=0.012) of monthly angina (angina frequency score ≤99). Conclusion: In women with nonobstructive coronary plaque and angina in the WARRIOR trial, higher CCTA NCP burden is associated with more frequent angina. Further work with quantitative AI analysis of CCTA from the WARRIOR trial will validate these preliminary findings.

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