Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
Br J Radiol. 2024 Nov 1;97(1163):1782-1790. doi: 10.1093/bjr/tqae135.
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-centre registry of sequential patients undergoing coronary computed tomography angiography, referred from inpatient and emergency department settings (n = 261, 63 years ± 8; 188 men). The primary outcome was revascularization by percutaneous coronary revascularization. The computed tomography angiography (CTA) images were analysed 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 vs -80.10 ± 7.75 Hu, P < .001) as well as higher plaque length, calcified, lipid, and fibrous plaque burden and volume. When FAI was incorporated into an 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 coronary artery disease patients.
本研究旨在探讨接受血管重建与未接受血管重建的患者斑块特征和脂肪衰减指数（FAI）的差异，并研究基于斑块特征和 FAI 的机器学习（ML）模型是否能预测血管重建。
这是一项回顾性分析，纳入了 261 名连续行冠状动脉计算机断层扫描血管造影（CTA）检查的患者，这些患者来自住院部和急诊部（年龄 63±8 岁，男性 188 人）。主要终点是经皮冠状动脉血运重建后的血运重建。由经验丰富的放射科医生在专用工作站上对 CTA 图像进行分析，以盲法进行。ML 基于模型自动计算。
研究队列包括 261 名患者。105 名患者接受了血运重建。接受血运重建的患者 FAI 值更高（67.35±5.49 比-80.10±7.75 Hu，P<0.001），斑块长度、钙化、脂质和纤维斑块负荷和体积也更高。当 FAI 被纳入基于斑块特征的 ML 风险模型以预测血运重建时，曲线下面积从 0.84（95%CI，0.68-0.99）增加到 0.95（95%CI，0.88-1.00）。
基于 FAI 和特征的 ML 算法可以帮助提高对未来血运重建的预测，并识别可能需要血运重建的患者。
在有症状的冠心病患者中，术前 FAI 可帮助指导血运重建。
