Added value of computed tomography fractional flow reserve in the diagnosis of coronary artery disease

Abstract

Multiple non-invasive tests are performed to diagnose coronary artery disease (CAD), but all are limited to either anatomical or functional assessments. Computed tomography derived Fractional Flow Reserve (CT-FFR) based on patient-specific lumped parameter models is a new test combining both characteristics simulating invasive FFR. This study aims to evaluate the added value of CT-FFR over other non-invasive tests to diagnose CAD. Patients with clinical suspicion of angina pectoris between 2010 and 2011 were included in this cross-sectional study. All underwent stress electrocardiography (X-ECG), SPECT, CT coronary angiography (CCTA) and CT-FFR. Invasive coronary angiography (ICA) and FFR were used as reference standard. Five models mimicking the clinical workflow were fitted and the area under receiver operating characteristic (AUROC) curve was used for comparison. 44% of the patients included in the analysis had a FFR of ≤ 0.80. The basic model including pre-test-likelihood and X-ECG had an AUROC of 0.79. The SPECT-strategy had an AUROC of 0.90 (p = 0.008), CCTA-strategy of 0.88 (p < 0.001), 0.93 when adding CT-FFR (p = 0.40) compared to 0.94 when combining CCTA and SPECT. This study shows adding on-site CT-FFR based on patient-specific lumped parameter models leads to an increased AUROC compared to the basic model. It improves the diagnostic work-up beyond SPECT or CCTA and is non-inferior to the combined strategy of SPECT and CCTA in the diagnosis of hemodynamically relevant CAD.

Figure 1

Study enrollment. CABG coronary artery bypass graft, CCTA coronary computed tomography angiography, ICA invasive coronary angiography, FFR fractional flow reserve, CT-FFR computed tomography fractional flow reserve, PCI percutaneous coronary intervention, SPECT single photon emission computed tomography, X-ECG exercise electrocardiogram.

Figure 2

Areas under the Receiver Operator Characteristics curve for all diagnostic model. The basic multivariable model has an AUC of 0.790 and increased to 0.897 upon extension with SPECT. An extension of the basic model with CCTA and CCS increased the AUC to 0.876 and the addition of CT-FFR leads to an AUC of 0.929. The basic model extended with SPECT, CCS and CCTA yielded the highest AUC of 0.94.

Figure 3

Calibration plots of the diagnostic models. All models show a moderate to good calibration. (A) Model 1(basic model) = pretest likelihood of CAD + X-ECG. (B) Model 2 = basic model + SPECT. (C) Model 3 = basic model + CCS + CCTA. (D) Model 4 = basic model + CCS + CCTA + CT-FFR. (E) Model 5 = basic model + SPECT + CCS + CCTA.