冠心病多维度风险评估：CT-FFR，CACS与FAI联合预测MACE的整合价值
Multidimensional Risk Assessment of Coronary Heart Disease: The Integrated Value of CT-FFR, CACS, and FAI in Jointly Predicting MACE
摘要: 冠心病(coronary artery disease, CAD)风险评估正由传统临床模型向整合影像与功能学的多维度模式演进。冠状动脉CT血流储备分数(computed tomography fractional flow reserve, CT-FFR)通过无创血流动力学评估，对主要心血管不良事件(major adverse cardiovascular events, MACE)具独立预测价值，联合高危斑块特征时风险分层能力显著提升(HR = 6.06)。冠状动脉钙化积分(coronary artery calcium score, CACS)作为动脉粥样硬化负荷的量化指标，虽为指南推荐工具，但对非钙化斑块及易损性评估存在局限。冠周脂肪衰减指数(FAI)及其动态变化(ΔFAI)作为新兴炎症生物标志物，是MACE的强效预测因子(OR = 16.725)。单一指标难以全面涵盖CAD的解剖、功能与炎症病理生理维度。整合CT-FFR、CACS与FAI的多模态评估框架，通过优势互补可实现精准个体化风险分层，优化血运重建与强化药物治疗等临床决策，改善患者预后。未来需开发人工智能驱动的自动化整合算法，并通过大规模前瞻性研究验证其临床效用与卫生经济学价值。
Abstract: The risk assessment of coronary artery disease (CAD) is evolving from traditional clinical models to a multi-dimensional model integrating imaging and functional studies. Coronary computed tomography fractional flow reserve (CT-FFR) has independent predictive value for major adverse cardiovascular events (MACE) through non-invasive hemodynamic assessment, and its risk stratification ability is significantly improved when combined with high-risk plaque characteristics (HR = 6.06). Coronary artery calcium score (CACS), as a quantitative indicator of atherosclerotic burden, is a guideline-recommended tool, but it has limitations in the assessment of non-calcified plaques and vulnerability. Pericoronary fat attenuation index (FAI) and its dynamic changes (ΔFAI), as emerging inflammatory biomarkers, are strong predictors of MACE (OR = 16.725). A single indicator is difficult to fully cover the anatomical, functional, and inflammatory pathophysiological dimensions of CAD. The multimodal assessment framework integrating CT-FFR, CACS, and FAI can achieve precise individualized risk stratification through complementary advantages, optimize clinical decisions such as revascularization and intensive drug therapy, and improve patient prognosis. In the future, it is necessary to develop artificial intelligence-driven automated integration algorithms and verify their clinical utility and health economic value through large-scale prospective studies.
文章引用：李庆雪, 杨全. 冠心病多维度风险评估：CT-FFR，CACS与FAI联合预测MACE的整合价值[J]. 临床医学进展, 2026, 16(7): 181-190. https://doi.org/10.12677/acm.2026.1672512

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