OBJECTIVES: The aim of this study was to compare diagnostic performance between quantitative flow ratio (QFR) derived from coronary angiography and fractional flow reserve derived from computed tomography (FFRCT) using fractional flow reserve (FFR) as the reference standard. BACKGROUND: QFR and FFRCT are recently developed, less invasive techniques for functional assessment of coronary artery disease. METHODS: QFR, FFRCT, and FFR were measured in 152 patients (233 vessels) with stable coronary artery disease. RESULTS: QFR was highly correlated with FFR (r = 0.78; p < 0.001), whereas FFRCT was moderately correlated with FFR (r = 0.63; p < 0.001). Both QFR and FFRCT showed moderately good agreement with FFR, presenting small values of mean difference but large values of root mean squared deviation (FFR-QFR, 0.02 ± 0.09; FFR-FFRCT, 0.03 ± 0.11). The sensitivity, specificity, positive predictive value, and negative predictive value of QFR ≤0.80 for predicting FFR ≤0.80 were 90%, 82%, 81%, and 90%, respectively. Those of FFRCT ≤0.80 for predicting FFR ≤0.80 were 82%, 70%, 70%, and 82%, respectively. The diagnostic accuracy of QFR ≤0.80 for predicting FFR ≤0.80 was 85% (95% confidence interval [CI]: 81% to 89%), whereas that of FFRCT ≤0.80 for predicting FFR ≤0.80 was 76% (95% CI: 70% to 80%). CONCLUSIONS: QFR and FFRCT showed significant correlation with FFR. Mismatches between QFR and FFR and between FFRCT and FFR were frequent.
OBJECTIVES: The aim of this study was to compare diagnostic performance between quantitative flow ratio (QFR) derived from coronary angiography and fractional flow reserve derived from computed tomography (FFRCT) using fractional flow reserve (FFR) as the reference standard. BACKGROUND: QFR and FFRCT are recently developed, less invasive techniques for functional assessment of coronary artery disease. METHODS: QFR, FFRCT, and FFR were measured in 152 patients (233 vessels) with stable coronary artery disease. RESULTS: QFR was highly correlated with FFR (r = 0.78; p < 0.001), whereas FFRCT was moderately correlated with FFR (r = 0.63; p < 0.001). Both QFR and FFRCT showed moderately good agreement with FFR, presenting small values of mean difference but large values of root mean squared deviation (FFR-QFR, 0.02 ± 0.09; FFR-FFRCT, 0.03 ± 0.11). The sensitivity, specificity, positive predictive value, and negative predictive value of QFR ≤0.80 for predicting FFR ≤0.80 were 90%, 82%, 81%, and 90%, respectively. Those of FFRCT ≤0.80 for predicting FFR ≤0.80 were 82%, 70%, 70%, and 82%, respectively. The diagnostic accuracy of QFR ≤0.80 for predicting FFR ≤0.80 was 85% (95% confidence interval [CI]: 81% to 89%), whereas that of FFRCT ≤0.80 for predicting FFR ≤0.80 was 76% (95% CI: 70% to 80%). CONCLUSIONS: QFR and FFRCT showed significant correlation with FFR. Mismatches between QFR and FFR and between FFRCT and FFR were frequent.