AI-QCT, CT-FFR, and physician visual interpretation in the per-vessel prediction of abnormal invasive adenosine FFR

A comparison of diagnostic performance comparing AI-QCTISCHEMIA, 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. In a single center, 43-month retrospective review of 442 patients referred for CCTA 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 CAC was 623. When analyzing the per vessel ischemia prediction, AI-QCTISCHEMIA had greater specificity, PPV, diagnostic accuracy, and 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 CADRADS ≥3. Plaque characteristics that were different in false positive vs true positive cases for AI-QCTISCHEMIA was max stenosis diameter (50 vs 70%, p = 0.03); for CT-FFR were maximum stenosis diameter (40 vs 70%, p < 0.001), total noncalcified plaque (9 vs 13%, p = 0.02); and for physician visual interpretation CADRADS ≥3 were total noncalcified plaque (8 vs 12%, p = 0.01), lumen volume (681 vs 510mm3, p = 0.03), maximum stenosis diameter (40 vs 60%, p < 0.001), total plaque (19 vs 33%, p = 0.006, total calcified plaque (11 vs 22%, p = 0.008). Regarding per-vessel prediction of FFR ≤ 0.8, AI-QCTISCHEMIA revealed greater specificity, PPV, accuracy, and AUC vs. CT-FFR and physician visual interpretation CADRADS ≥3.