■比较AI-QCTISCHEMIA的诊断性能比较,使用血流储备分数(CT-FFR)的冠状动脉计算机断层扫描血管造影,和医生视觉解释对侵袭性腺苷FFR的预测尚未得到评估。此外,影响这些测试的冠状动脉斑块特征尚未评估.
■在一个中心,43个月回顾性回顾了442例冠状动脉CT血管造影和CT-FFR转诊的患者,44例CT-FFR患者在60天内使用冠状动脉内腺苷FFR评估了54条血管。报告了这三种技术预测FFR≤0.80的诊断性能的比较。研究人群的平均年龄是65岁,76.9%为男性,冠状动脉钙中位数为654。在分析血管缺血预测时,AI-QCTISCHEMIA有更高的特异性,阳性预测值(PPV),诊断准确性,和曲线下面积(AUC)与CT-FFR和医师视觉解释CAD-RADS。AI-QCTISCHEMIA的AUC为0.91vs.CT-FFR为0.76,CAD-RADS≥3为0.62。在假阳性与假阳性中不同的斑块特征AI-QCTISCHEMIA的真阳性病例为最大狭窄直径%(54%vs.67%,P<0.01);CT-FFR为最大狭窄直径%(40%vs.65%,P<0.001),总非钙化斑块(9%vs.13%,P<0.01);对于医师视觉解释,CAD-RADS≥3是总的非钙化斑块(8%vs.12%,P<0.01),管腔体积(681vs.510mm3,P=0.02),最大狭窄直径%(40%vs.62%,P<0.001),总斑块(19%与33%,P=0.002),和总钙化斑块(11%vs.22%,P=0.003)。
■关于FFR≤0.8的每血管预测,AI-QCTISCHEMIA显示出更高的特异性,PPV,准确度,和AUCvs.CT-FFR和医师视觉解释CAD-RADS≥3。
UNASSIGNED: A comparison of diagnostic performance comparing AI-QCTISCHEMIA, coronary computed tomography angiography using fractional flow reserve (CT-FFR), and physician visual interpretation on the prediction of invasive adenosine FFR have not been evaluated. Furthermore, the coronary plaque characteristics impacting these tests have not been assessed.
UNASSIGNED: In a single centre, 43-month retrospective review of 442 patients referred for coronary computed tomography angiography and CT-FFR, 44 patients with CT-FFR had 54 vessels assessed using intracoronary adenosine FFR within 60 days. A comparison of the diagnostic performance among these three techniques for the prediction of FFR ≤ 0.80 was reported. The mean age of the study population was 65 years, 76.9% were male, and the median coronary artery calcium was 654. When analysing the per-vessel ischaemia prediction, AI-QCTISCHEMIA had greater specificity, positive predictive value (PPV), diagnostic accuracy, and area under the curve (AUC) vs. CT-FFR and physician visual interpretation CAD-RADS. The AUC for AI-QCTISCHEMIA was 0.91 vs. 0.76 for CT-FFR and 0.62 for CAD-RADS ≥ 3. Plaque characteristics that were different in false positive vs. true positive cases for AI-QCTISCHEMIA were max stenosis diameter % (54% vs. 67%, P < 0.01); for CT-FFR were maximum stenosis diameter % (40% vs. 65%, P < 0.001), total non-calcified plaque (9% vs. 13%, P < 0.01); and for physician visual interpretation CAD-RADS ≥ 3 were total non-calcified plaque (8% vs. 12%, P < 0.01), lumen volume (681 vs. 510 mm3, P = 0.02), maximum stenosis diameter % (40% vs. 62%, P < 0.001), total plaque (19% vs. 33%, P = 0.002), and total calcified plaque (11% vs. 22%, P = 0.003).
UNASSIGNED: Regarding per-vessel prediction of FFR ≤ 0.8, AI-QCTISCHEMIA revealed greater specificity, PPV, accuracy, and AUC vs. CT-FFR and physician visual interpretation CAD-RADS ≥ 3.