Faculty of Medicine and Life Sciences, Hasselt University, LCRC, Agoralaan, Diepenbeek, 3590, Belgium.
Department of Radiology, Jessa Hospital, LCRC, Stadsomvaart 11, Hasselt, 3500, Belgium.
Int J Cardiovasc Imaging. 2024 Sep;40(9):1875-1880. doi: 10.1007/s10554-024-03173-0. Epub 2024 Jul 4.
Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFR) to computational fluid dynamics CT-derived FFR (FFR) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFR model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFR and FFR measurements in this retrospective proof of concept study. FFR was compared with FFR regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFR in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFR were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFR and FFR (p = 0.12). FFR performed similarly to FFR for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFR as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.
冠状动脉计算机断层扫描血管造影术（CCTA）结合无创性血流储备分数（FFR）与有创性 FFR 相比可计算病变特异性缺血，根据最近的指南，可用于稳定型胸痛和中度狭窄的患者。本研究的目的是比较一种新的基于 CCTA 的人工智能深度学习模型用于 FFR 预测（FFR）与基于计算流体力学 CT 的 FFR（FFR），以 FFR 作为参考标准，比较中等程度冠状动脉狭窄患者的 FFR。FFR 模型使用 413 名患者 500 个狭窄血管的曲面多平面重建 CCTA 图像进行训练，使用 FFR 测量值作为真实值。我们纳入了 37 名患者，他们在 CCTA 和冠状动脉造影中有 39 个中度狭窄病变，并且有 FFR 和 FFR 测量值。本回顾性概念验证研究比较了 FFR 与 FFR 对预测 FFR≤0.80 的敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和诊断准确性。FFR 预测 FFR≤0.80 的敏感性、特异性、PPV、NPV 和诊断准确性分别为 91%（11/12）、82%（23/28）、67%（10/15）、96%（23/24）和 85%（33/39），FFR 预测 FFR≤0.80 的相应值分别为 82%（9/11）、75%（21/28）、56%（9/16）、91%（21/23）和 77%（30/39）。FFR 与 FFR 之间的诊断准确性无显著差异（p=0.12）。FFR 与 FFR 预测 FFR≤0.80 的中度冠状动脉狭窄具有相似的性能。这些发现表明 FFR 可能成为指导这些狭窄治疗管理的一种潜在的无创成像工具。
