Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia

Table 2 Findings of cCTA, CT-FFR_ML and stress perfusion CMR

Parameter	Mean value ± standard deviation or frequency (%)
cCTA
Agatston score^a	657 ± 808
Agatston score interquartile range	759
No. of patients Agatston score > 400	74 (54%)
Luminal stenosis ≥ 50%	129 (91%)
Mean No. of vessels per patient	1.7 ± 0.9
Luminal stenosis ≥ 70%	28 (20%)
Mean No. of vessels per patient	0.3 ± 0.6
CT-FFR_ML
CT-FFR_ML ≤ 0.8	27 (19%)
Stress perfusion CMR
Significant perfusion deficit^b	17 (12%)

Unless otherwise specified, data are numbers of patient, with percentages in parentheses. Data are mean ± standard deviation (SD) or frequency (%). ^aAgatston score measured in 138 patients. ^bDefined as two neighboring slices or in midventricular or basal part more than 60 degrees or in apical part more than 90 degrees or a transmural defect irrespective of location. (37) cCTA = coronary CT angiography; CT-FFR_ML = fractional flow reserve derived from coronary computed tomography angiography based on machine learning algorithm; CMR = cardiovascular magnetic resonance imaging

ISSN: 1471-2261