Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
Department of Cardiology, The First Affiliated Hospital of Ningbo University, Zhejiang, China.
Int J Cardiovasc Imaging. 2024 Aug;40(8):1641-1652. doi: 10.1007/s10554-024-03149-0. Epub 2024 Jun 15.
This study investigated the association of anatomic and hemodynamic plaque characteristics based on deep learning coronary computed tomography angiography (CCTA) with high-risk plaques that caused subsequent major adverse cardiovascular events (MACE). A retrospective analysis was conducted on patients who underwent CCTA between 1 month and 3 years prior to the occurrence of a MACE. Deep learning and computational fluid dynamics algorithms based on CCTA were applied to extract adverse plaque characteristics (low-attenuation plaque, positive remodeling, napkin-ring sign, and spotty calcification), and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFR], change in FFR across the lesion [△FFR], wall shear stress [WSS], and axial plaque stress [APS]). Correlation analysis, logistic regression, and Cox proportional risk analysis were conducted to understand the relationship between these measures and the occurrence of MACE and assess the value of hemodynamic parameters in predicting the incidence of MACE events and their prognosis. Our study included 86 patients with a total of 134 vessels exhibiting plaque formation and 83 culprit vessels with a subsequent coronary event. Culprit vessels had percent diameter stenosis [%DS] (0.54 ± 0.16 vs. 0.62 ± 0.13, P = 0.003), larger non-calcified plaque volume (45.8 vs. 101.7, P < 0.001), larger low-attenuation plaque volume (3.6 vs. 14.5, P < 0.001), more lesions with ≥ 3 adverse plaque characteristics (APC) (4 vs.26, P = 0.002), and worse hemodynamic features of adverse plaque. FFR demonstrated better visualization of maximum achievable flow in the presence of coronary stenosis and better correlation with the stenosis severity, while maximum of wall shear stress (WSSmax) was highly correlated with low-attenuation plaques and APC. The inclusion of hemodynamic parameters improved the efficacy of the predictive model, and a high WSS suggested a higher probability of MACE. Hemodynamic parameters based on CCTA are significantly correlated with plaque morphology. Importantly, integrating CCTA-derived parameters can refine the predictive performance of MACE occurrence.
本研究旨在探讨基于深度学习的冠状动脉 CT 血管造影（CCTA）的解剖学和血流动力学斑块特征与导致后续主要不良心血管事件（MACE）的高危斑块之间的关联。对发生 MACE 前 1 个月至 3 年内接受 CCTA 检查的患者进行回顾性分析。应用基于 CCTA 的深度学习和计算流体动力学算法提取不良斑块特征（低衰减斑块、正性重构、napkin-ring 征和点状钙化）和血流动力学参数（基于冠状动脉 CT 血管造影的血流储备分数 [FFR]、病变处 FFR 的变化[△FFR]、壁面切应力[WSS]和轴向斑块应力[APS]）。采用相关分析、逻辑回归和 Cox 比例风险分析来了解这些指标与 MACE 发生之间的关系，并评估血流动力学参数在预测 MACE 事件发生及其预后中的价值。本研究共纳入 86 例患者，共 134 支血管存在斑块形成，83 支血管为随后发生的冠状动脉事件。罪犯血管的狭窄程度百分比（%DS）[（0.54 ± 0.16）%比（0.62 ± 0.13）%，P=0.003]、非钙化斑块体积较大（45.8 比 101.7，P<0.001）、低衰减斑块体积较大（3.6 比 14.5，P<0.001）、具有≥3 个不良斑块特征（APC）的病变较多（4 比 26，P=0.002）和斑块的血流动力学特征较差。FFR 能更好地显示存在冠状动脉狭窄时的最大可获得血流，并与狭窄严重程度更好地相关，而壁面切应力最大值（WSSmax）与低衰减斑块和 APC 高度相关。纳入血流动力学参数提高了预测模型的效能，而高壁面切应力提示 MACE 发生的可能性更高。基于 CCTA 的血流动力学参数与斑块形态显著相关。重要的是，整合 CCTA 衍生参数可以提高 MACE 发生预测的性能。
