Quantitative plaque characterisation and association with acute coronary syndrome on medium to long term follow up: insights from computed tomography coronary angiography.

Background: Computed tomography coronary angiography (CTCA) is an established imaging modality widely used for diagnosing coronary artery stenosis with expanding potential for comprehensive assessment of coronary artery disease (CAD). Lesion-based analyses of high-risk plaques (HRP) on CTCA may aid further in prognostication presenting with stable chest pain. We conduct qualitative and quantitative assessments to identify HRPs that are associated with acute coronary syndrome (ACS) on a medium to long term follow-up. Methods: Retrospective cohort study of patients who underwent CTCA for suspected CAD. Obstructive stenosis (OS) is defined as ≥50% and the presence of HRP and its constituents: positive-remodelling (PR), low-attenuation-plaque (LAP; <56 HU), very-low-attenuation-plaque (vLAP; <30 HU) and spotty-calcification (SC) were recorded. A cross-sectional quantitative analysis of HRP was performed at the site of minimum-luminal-area (MLA). The primary endpoint was fatal or non-fatal ACS on follow-up. Results: A total of 1,257 patients were included (mean age 61±14 years old and 51% male) with a median follow-up of 7.24 years (interquartile range 5.5 to 7.7 years). The occurrence of ACS was significantly higher in HRP (+) patients compared to HRP (-) patients and patients with no plaques (20.5% vs. 1.6% vs. 0.4%, log-rank test P<0.001). ACS was more frequent in HRP (+)/OS (+) patients (20.7%) compared to HRP (+)/OS (-) patients (8.6%), HRP (-)/OS (+) patients (1.8%) and HRP (-)/OS (-) patients (1.0%). OS, cross-sectional plaque area (PA) and the presence of vLAP identified those HRP lesions that were more likely to cause future ACS. Cross-sectional LAP area (<56 HU) in HRP lesions added incremental prognostic value to OS in predicting ACS (P=0.008). Conclusions: The presence of OS and the LAP area at the site of MLA identify the HRP lesions that have the greatest association with development of future ACS.

Pericoronary Adipose Tissue Attenuation Is Associated with High-Risk Plaque and Subsequent Acute Coronary Syndrome in Patients with Stable Coronary Artery Disease.

BACKGROUND: High-risk plaques (HRP) detected on coronary computed tomography angiography (CTA) confer an increased risk of acute coronary syndrome (ACS). Pericoronary adipose tissue attenuation (PCAT) is a novel biomarker of coronary inflammation. This study aimed to evaluate the association of PCAT with HRP and subsequent ACS development in patients with stable coronary artery disease (CAD). METHODS: Patients with stable CAD who underwent coronary CTA from 2011 to 2016 and had available outcome data were included. We studied 41 patients with HRP propensity matched to 41 controls without HRP (60 ± 10 years, 67% males). PCAT was assessed using semi-automated software on a per-patient basis in the proximal right coronary artery (PCATRCA) and a per-lesion basis (PCATLesion) around HRP in cases and the highest-grade stenosis lesions in controls. RESULTS: PCATRCA and PCATLesion were higher in HRP patients than controls (PCATRCA: -80.7 ± 6.50 HU vs. -84.2 ± 8.09 HU, p = 0.03; PCATLesion: -79.6 ± 7.86 HU vs. -84.2 ± 10.3 HU, p = 0.04), and were also higher in men (PCATRCA: -80.5 ± 7.03 HU vs. -86.1 ± 7.08 HU, p < 0.001; PCATLesion: -79.6 ± 9.06 HU vs. -85.2 ± 7.96 HU, p = 0.02). Median time to ACS was 1.9 years, within a median follow-up of 5.3 years. PCATRCA alone was higher in HRP patients who subsequently presented with ACS (-76.8 ± 5.69 HU vs. -82.0 ± 6.32 HU, p = 0.03). In time-dependent analysis, ACS was associated with HRP and PCATRCA. CONCLUSIONS: PCAT attenuation is increased in stable CAD patients with HRP and is associated with subsequent ACS development. Further investigation is required to determine the clinical implications of these findings.

Performance of computed tomography-derived fractional flow reserve using reduced-order modelling and static computed tomography stress myocardial perfusion imaging for detection of haemodynamically significant coronary stenosis.

Aims: To compare the diagnostic performance of a reduced-order computed tomography-derived fractional flow reserve (CT-FFR) technique derived from luminal deformation and static CT stress myocardial perfusion (CTP). Methods and results: Forty-six patients (84 vessels) with suspected coronary artery disease from a single institution planned for elective coronary angiography prospectively underwent research indicated invasive fractional flow reserve (FFR) and 320-detector CT coronary angiography (CTA) and static CTP. Analyses were performed in separate blinded core laboratories for CT-FFR and CTP. CT-FFR was derived using a reduced-order model with dedicated software on a standard desktop computer. CTP was assessed visually and quantitatively by transmural perfusion ratio (TPR). Invasive FFR was significant in 33% (28/84) of vessels. Overall per-vessel sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for CT-FFR were 81%, 84%, 71%, 90%, and 83%, respectively, those of visual CTP were 54%, 92%, 79%, 77%, and 78%, respectively, and TPR were 64%, 48%, 42%, 70%, and 54%, respectively. Per-vessel receiver operator curve analysis demonstrated a significantly larger area under the curve (AUC) for CT-FFR (0.89) with that for visual CTP (0.72; P = 0.016), TPR (0.55; P < 0.0001), and CTA (0.76; P = 0.04). The addition of CT-FFR to CTA provided superior improvement in performance (AUC 0.93; P < 0.0001) compared with CTA alone, a combination of CTA with visual CTP (AUC 0.82; P = 0.007) and CTA with TPR (AUC 0.78; P = 0.0006). Conclusion: Based on this selected cohort of patients, a reduced-order CT-FFR technique is superior to visual and quantitatively assessed static CTP in detecting haemodynamically significant coronary stenosis as assessed by invasive FFR.

Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis.

OBJECTIVES: This study describes the feasibility and accuracy of a novel computed tomography (CT) fractional flow reserve (FFR) technique based on alternative boundary conditions. BACKGROUND: Techniques used to compute FFR based on images acquired from coronary computed tomography angiography (CTA) are described. Boundary conditions were typically determined by allometric scaling laws and assumptions regarding microvascular resistance. Alternatively, boundary conditions can be derived from the structural deformation of coronary lumen and aorta, although its accuracy remains unknown. METHODS: Forty-two patients (78 vessels) in a single institution prospectively underwent 320-detector coronary CTA and FFR. Deformation of coronary cross-sectional lumen and aorta, computed from coronary CTA images acquired over diastole, was used to determine the boundary conditions based on hierarchical Bayes modeling. CT-FFR was derived using a reduced order model performed using a standard desktop computer and dedicated software. First, 12 patients (20 vessels) formed the derivation cohort to determine optimal CT-FFR threshold with which to detect functional stenosis, defined as FFR of ≤0.8, which was validated in the subsequent 30 patients (58 vessels). RESULTS: Derivation cohort results demonstrated optimal threshold for CT-FFR was 0.8 with 67% sensitivity and 91% specificity. In the validation cohort, CT-FFR was successfully computed in 56 of 58 vessels (97%). Compared with coronary CTA, CT-FFR at ≤0.8 demonstrated a higher specificity (87% vs. 74%, respectively) and positive predictive value (74% vs. 60%, respectively), with comparable sensitivity (78% vs. 79%, respectively), negative predictive value (89% vs. 88%, respectively), and accuracy (area under the curve: 0.88 vs. 0.77, respectively; p = 0.22). Based on Bland-Altman analysis, mean intraobserver and interobserver variability values for CT-FFR were, respectively, -0.02 ± 0.05 (95% limits of agreement: -0.12 to 0.08) and 0.03 ± 0.06 (95% limits: 0.07 to 0.19). Mean time per patient for CT-FFR analysis was 27.07 ± 7.54 min. CONCLUSIONS: CT-FFR based on alternative boundary conditions and reduced-order fluid model is feasible, highly reproducible, and may be accurate in detecting FFR ≤ 0.8. It requires a short processing time and can be completed at point-of-care. Further validation is required in large prospective multicenter settings.