Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany.
Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Kerckhoff Heart and Thorax Center, Department of Cardiology, Bad Nauheim, Germany.
Eur J Radiol. 2019 Oct;119:108657. doi: 10.1016/j.ejrad.2019.108657. Epub 2019 Sep 7.
This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFR) for the detection of lesion-specific ischemia.
Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFR and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis.
In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFR reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFR showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFR was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007].
Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
本研究旨在探讨性别差异对基于机器学习的冠状动脉 CT 血管造影（cCTA）衍生的分数流量储备（CT-FFR）检测特定病变缺血的诊断性能的影响。
五个中心纳入了 MACHINE（Machine leArning Based CT angiograpHy derIved FFR：a Multi-ceNtEr）注册研究中的 351 名患者（73.5%为男性）的 525 支血管。CT-FFR 和有创 FFR≤0.80 被认为具有血流动力学意义，而 cCTA 管腔狭窄≥50%被认为是阻塞性病变。在每支血管的基础上评估基于机器学习的 CT-FFR 评估男性和女性特定病变缺血的诊断性能。
共纳入 398 支男性血管和 127 支女性血管。与有创 FFR 相比，CT-FFR 在男性中的灵敏度、特异性、阳性预测值和阴性预测值分别为 78%（95%CI 72-84）、79%（95%CI 73-84）、75%（95%CI 69-79）和 82%（95%CI：76-86），而在女性中的灵敏度、特异性、阳性预测值和阴性预测值分别为 75%（95%CI 58-88）、81%（95%CI 72-89）、61%（95%CI 50-72）和 89%（95%CI 82-94）。CT-FFR 在男性和女性的受试者工作特征曲线（AUC）下面积（AUC）中无统计学差异（AUC：0.83[95%CI 0.79-0.87] vs. 0.83[95%CI 0.75-0.89]，p=0.89）。CT-FFR 并不优于单独的 cCTA[AUC：0.83（95%CI：0.75-0.89）vs. 0.74（95%CI：0.65-0.81），p=0.12]在女性中，但在男性中具有统计学意义的改善[AUC：0.83（95%CI：0.79-0.87）vs. 0.76（95%CI：0.71-0.80），p=0.007]。
基于机器学习的 CT-FFR 在男性和女性中的表现相同，其对特定病变缺血的检测具有优于单独 cCTA 的诊断性能。
