CCTA-Derived Fat Attenuation Index Predict Future Percutaneous Coronary Intervention

This study aims to investigate the differences in plaque characteristics and fat attenuation index (FAI) between in patients who received revascularization versus those who did not receive revascularization and examine whether the machine-learning (ML) based model constructed by plaque characteristi...

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Bibliographic Details
Published inBritish journal of radiology
Main Authors Wei He, M D, Lu, Yige, Yin, Jiasheng, He, Furong, Zhang, Yaoyi, Qiao, Guanyu, Luan, Jingyang, Yao, Zhifeng, Li, Chenguang, Yang, Shan, Zhao, Shihai, Shen, Li, Guo, Weifeng, Zeng, Mengsu
Format Journal Article
LanguageEnglish
Published England 07.08.2024
Subjects
Coronary Angiography Computed Tomography
Fat attenuation index
Coronary artery disease
Fractional flow reserve
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Summary:This study aims to investigate the differences in plaque characteristics and fat attenuation index (FAI) between in patients who received revascularization versus those who did not receive revascularization and examine whether the machine-learning (ML) based model constructed by plaque characteristics and FAI can predict revascularization. This study was a post hoc analysis of a prospective single-center registry of sequential patients undergoing CCTA, referred from inpatient and emergency department settings (n = 261, 63 years ± 8; 188 men). The primary outcome was revascularization by percutaneous coronary revascularization. The CTA images were analyzed by experienced radiologists using a dedicated workstation in a blinded fashion. The ML-based model was automatically computed. The study cohort consisted of 261 subjects. Revascularization was performed in 105 subjects. Patients receiving revascularization had higher FAI value (67.35±5.49 Hu vs -80.10±7.75 Hu, p < 0.001) as well as higher plaque length, calcified, lipid and fibrous plaque burden and volume. When FAI was incorporated into a ML risk model based on plaque characteristics to predict revascularization, the area under the curve increased from 0.84 (95% CI: 0.68-0.99) to 0.95 (95% CI: 0.88-1.00). ML-algorithms based on FAI and characteristics could help improve the prediction of future revascularization and identify patients likely to receive revascularization. Pre-procedural FAI could help guide revascularization in symptomatic CAD patients.
ISSN:1748-880X