Translesional Fractional Flow Reserve is Related to Plaque Components in Coronary Artery Disease: A Study Combining Pressure Wire and NIRS-IVUS Analysis

Objectives. It remains unclear whether atherosclerotic plaque structure or composition is related to translesional biomechanical stresses in coronary artery disease. The aim of this study was to evaluate the association between translesional pressure parameters (using a pressure wire) and plaque characteristics (using a combined near-infrared spectroscopy [NIRS] and intravascular ultrasound [IVUS] imaging catheter). Methods. Fractional flow reserve (FFR), delta (Δ) FFR, and Δ pressure were obtained during adenosine-induced maximum hyperemic status. Lipid core burden index (LCBI) and maximum LCBI within 2 mm (maxLCBI_2mm) and tomographic anatomy were evaluated by NIRS-IVUS. Results. Sixty-six lesions from 57 patients were analyzed (57 lesions for FFR, 45 lesions for ΔFFR). There was a negative correlation between FFR and maxLCBI_2mm (r=-0.264; P=.049) and a positive correlation between ΔFFR and maxLCBI_2mm (r=0.299; P=.049). ΔFFR of lesions with maxLCBI_2mm ≥500 was significantly higher than maxLCBI_2mm <500 (0.159 ± 0.085 vs 0.104 ± 0.075, respectively; P=.04). By receiver-operating characteristic curve analysis, ΔFFR ≥0.1 was a predictor for maxLCBI_2mm ≥500 (area under curve, 0.707; 95% confidence interval, 0.552-0.862; P=.03). On multivariate analysis, ΔFFR was the only predictor of maxLCBI_2mm (β=0.347; P=.03). Conclusion. ΔFFR across a coronary artery lesion is related to lipid core burden assessed using NIRS-IVUS and might be a meaningful predictor of high-risk plaque (plaque with high lipid content).

Key words: coronary artery disease, fractional flow reserve, intravascular ultrasound, near-infrared spectroscopy, plaque composition

Atherosclerosis complications are influenced by endothelial insults, vascular inflammation, altered hemodynamics, vascular remodeling, plaque rupture, and (finally) thrombus formation.1 Wall shear stress, among various hemodynamic forces, has been proposed as a hemodynamic force both in the development of atherosclerosis and in the transformation of plaque from stable to vulnerable.2 However, wall shear stress can be assessed only indirectly via complex computational technology and only in specialized institutions. Furthermore, it remains unclear whether plaque structure and/or plaque composition are related to biomechanical forces in coronary artery disease.

Fractional flow reserve (FFR) has been studied as a predictor of myocardial ischemia and in relationship with lesion characteristics, such as minimal lumen area (MLA), plaque burden, and lesion length. However, the relationship between FFR and plaque components has not been clear. Intravascular ultrasound (IVUS) provides tomographic and longitudinal information about coronary artery lesion morphometry. Near-infrared spectroscopy (NIRS) is a catheter-based technique for identifying lipid core containing plaques within the vessel wall.3 Recently, IVUS and NIRS have been combined in a single catheter so that the two modalities can be measured simultaneously.4

The aim of this study was to assess the impact of external hemodynamic forces on the plaque characteristics using FFR-derived translesional pressure parameters vs NIRS-IVUS derived plaque morphology and composition.

Study population and target-vessel selection. At our institution, patients who were studied with NIRS-IVUS were enrolled in a dedicated registry. Out of 295 patients studied between February 2015 and August 2016, there were 57 patients with 66 lesions who had a >50% angiographic diameter stenosis and were also evaluated using a pressure wire. Patients with acute myocardial infarction were excluded. Only 1 lesion was analyzed in any given vessel. If a vessel had multiple lesions, the lesion with the smallest MLA was selected for this study.

Quantitative coronary angiography. Coronary angiography was obtained using standard techniques, and all angiograms were analyzed in a blinded fashion at the Ajou University imaging analysis core laboratory using standard methods and the Cardiovascular Angiography Analysis System II (Pie Medical).

Hemodynamic measurements. A pressure-monitoring guidewire (either PressureWire Certus [St. Jude Medical Systems] or ComboWire [Volcano Corporation]) was used to obtain FFR, delta (Δ) FFR, and proximal and distal pressures across the target lesion. After administration of intracoronary nitrates, the pressure wire was inserted distally into the vessel; and hyperemia was induced with intravenous administration of adenosine at a rate of 140-240 μg/kg/min.

FFR was defined as the ratio between the mean distal coronary pressure (Pd) and the mean aortic pressure (Pa), both measured simultaneously at maximal hyperemia (Figure 1A and Figure  1B).

The pressure wire was then pulled back to the ostium during maximal hyperemia and the values of pressure were obtained at the distal site (Py) and the proximal site (Px) across the target lesion (Figure 1A and Figure 1B).

Change in pressure (ΔP) was calculated by subtracting the distal pressure from the pressure proximal to the target lesion:

ΔP = Px – Py

ΔFFR was calculated by subtracting the distal FFR value from the FFR value proximal to the target lesion (Figure 1A and Figure 1B):

ΔFFR = Px / Pa – Py / Pa = ΔP / Pa

Intracoronary imaging and analysis. The NIRS-IVUS catheter (TVC Imaging System; Infraredx) was advanced into the target vessel and automatic motorized pullback was performed at 0.5 mm/s.

Quantitative IVUS analysis was performed along the entire length of the vessel on cross-sectional images spaced 1 mm apart. External elastic membrane (EEM) cross-section area (CSA), lumen CSA, and MLA were measured at the smallest lumen CSA site.

Plaque burden (Figure 1C) was defined as:

plaque CSA (EEM CSA – lumen CSA) / EEM CSA x 100

Lesion length was calculated from IVUS frame counts using automatic transducer pullback (Figure 1C and Figure 1D).

NIRS images were interpreted offline and were considered uninterpretable and excluded from further analysis if the corresponding block chemogram was black in color, indicating the absence of reliable data. To provide a quantitative estimate of the amount of lipid present in the segments of the target artery, the lipid core burden index (LCBI) was calculated, defined as the fraction of pixels indicating lipid within a region multiplied by 1000. LCBI_lesion was defined as the LCBI value of the entire target lesion. Lesions were also scanned for the maximum LCBI in any 2 mm segment (maxLCBI_2mm) or any 4 mm segment (maxLCBI_4mm) (Figure 1E).

Statistical analysis. Categorical variables are expressed as numbers or percentages and continuous variables are presented as mean ± standard deviation. Data were analyzed on a per-patient and per-lesion basis. For the per-patient analysis, continuous variables were compared using the unpaired t -test or non-parametric Mann-Whitney test; categorical variables were compared with the Chi-square test or Fisher’s exact test. For the per-lesion analysis, a logistic generalized estimated equation model with robust standard errors that accounted for the clustering between lesions in the same subject was created. To ascertain independent predictors of maxLCBI_2mm as both a continuous and a binary variable (maxLCBI_2mm ≥500 or maxLCBI_2mm ≥400), linear mixed and multivariable logistic generalized estimated equation models with robust standard errors were used. Receiver operating characteristic (ROC) curves were analyzed to assess the best cutoff values of pressure parameters to predict maxLCBI_2mm ≥500 with maximal accuracy. All statistical analyses were performed using SPSS, version 22 for Windows (SPSS, Inc). A P-value <.05 was considered statistically significant.

Baseline characteristics. A total of 57 patients were enrolled between February 2015 and August 2016 (Table 1). Mean age was 56.8 ± 11.2 years and 45 patients (78.9%) were men. Diabetes mellitus, hypertension, hypercholesterolemia, and smoking were documented in 12 (21.1%), 27 (47.4%), 19 (33.3%), and 27 (47.4%) patients, respectively. Overall, 22 patients (38.6%) presented with stable angina and 35 patients (61.4%) presented with acute coronary syndromes.

Coronary angiography, pressure measurements, and NIRS-IVUS findings. Only 1 vessel was analyzed for each patient. Among the 66 eligible target lesions (57 patients, 57 vessels), the lesion with the smallest MLA was selected for the analysis with FFR when there were multiple lesions within 1 vessel. Continuous pullback pressure measurements — which are necessary to obtain ΔFFR and ΔP — were available in 45 lesions (36 patients). Angiography, pressure measurements, and NIRS-IVUS findings are summarized in Table 2. The most commonly involved artery was the left anterior descending (LAD) (50 lesions [87.7%] for analysis of FFR and 40 lesions [88.9%] for analysis of ΔFFR and ΔP). FFR measured 0.77 ± 0.10, ΔP measured 11.4 ± 7.3 mm Hg, and LCBI_lesion measured 151 ± 120 in lesions with FFR-only data and 146 ± 105 in lesions that also had data on ΔFFR and ΔP. A representative case is shown in Figure 1.

Relationship between pressure parameters and NIRS-IVUS lesion characteristics. As shown in Table 3 and Figure 2, FFR was positively correlated with MLA (r=0.304; P=.02) and negatively correlated with plaque burden (r=-0.399; P<.01) and lesion length (r=-0.376; P<.01). ΔFFR showed significantly positive correlations with plaque burden (r=0.366; P=.01) and lesion length (r=0.419; P<.01) with a trend toward a negative correlation with MLA (r=-0.266; P=.08). ΔP was correlated with lesion length (r=0.389; P<.01) and tended to be correlated with plaque burden (r=0.280; P=.06). FFR was inversely correlated with maxLCBI_2mm (r=-0.264; P=.049), and ΔFFR was positively correlated with maxLCBI_2mm (r=0.299; P=.049). However, FFR was not correlated with maxLCBI_4mm, with only a trend for ΔFFR to be correlated with maxLCBI_4mm (r=0.266; P=.08). ΔP was not correlated with NIRS assessment of lipidic plaque.

Predictors of NIRS-lipid core burden. Using maxLCBI_2mm ≥500 to indicate a high-risk lesion, ΔFFR was significantly higher in lesions with vs without maxLCBI_2mm ≥500 (0.159 ± 0.085 vs 0.104 ± 0.075, respectively; P=.04), while the FFR value itself was not significantly different between the two groups (0.780 ± 0.100 vs 0.749 ± 0.082, respectively; P=.26) (Figure 3). The distribution of FFR and ΔFRR according to maxLCBI_2mm ≥400 vs <400 are shown in Supplemental Figure S1. The best ΔFFR cutoff value to predict maxLCBI_2mm ≥500 was 0.10 (area under the curve [AUC], 0.707; 95% confidence interval [CI], 0.552-0.862; P=.03), translating into a sensitivity of 78.6% and specificity of 58.1% (Figure 4A). The best ΔFFR cutoff value to predict maxLCBI_2mm ≥400 is presented in Supplemental Figure S2. Figure 4B shows that lesions with ΔFFR ≥0.1 had a higher proportion of lesions with maxLCBI_2mm ≥500 vs lesions with ΔFFR <0.1 (48.5% vs 14.3%, respectively; odds ratio, 5.077; 95% CI, 1.176-21.92; P=.02). There was no significant difference in the proportion of lesions with maxLCBI_2mm ≥500 comparing lesions with FFR ≥0.8 vs lesions with FFR <0.8 (P=.35). The proportion of maxLCBI_2mm ≥400 lesions according to FFR and ΔFFR is shown in Supplemental Figure S3.

The multivariate analysis revealed that ΔFFR was an independent predictor of maxLCBI_2mm (B ± standard error = 890.2 ± 399.1; β=0.347; P=.03) (Table 4). A maxLCBI_2mm ≥500 and a maxLCBI_2mm ≥400 were also tested and are presented in Supplemental Table S1 and Supplemental Table S2.

To the best of our knowledge, this is the first study that has documented the relationship between translesional FFR (ΔFFR) and plaque components in coronary artery disease. The major finding from the present study is the following: ΔFFR across the lesion assessed by using a pressure wire predicts the lipid core content of the lesion obtained by using a NIRS-IVUS catheter while the absolute FFR in the distal vessel is not related to plaque morphology or composition.

FFR has now been studied extensively in relation to functional ischemia of the myocardium.^5,6 FFR is related to lesion characteristics such as MLA, plaque burden, and lesion length,^7,8 and is a predictor for clinical outcomes.^9-12 However, the relationship between FFR and plaque components is not clear, although recently FFR has been compared to the necrotic core¹³ or to fibrofatty tissue¹⁴ assessed by intracoronary imaging (virtual histology-IVUS). In a study using coronary computed tomographic angiography, lesion ischemia by FFR was related with positive remodeling and low attenuation, but not spotty calcification.¹⁵ However, in these studies, the value of FFR was measured at the distal part of a vessel, which reflects the entire vessel but not the local forces at the site of a specific plaque, especially in a vessel with diffuse disease or tandem lesions.^16,17 In addition to the amount of jeopardized myocardium, complex lesion morphology such as tandem or diffuse lesions have been associated with angiographic-FFR mismatch or reverse mismatch.^18,19

ΔFFR measurements with pullback pressure recording can be helpful to assess the functional severity of a stenosis and identify a functionally significant lesion.²⁰ A recent study showed that ΔFFR derived by coronary computed tomographic angiography can predict the culprit lesion in acute coronary syndrome.²¹ ΔFFR across a lesion may also be a good way to evaluate hemodynamic forces at any particular coronary artery lesion while minimizing the influence of the amount of jeopardized myocardium.

NIRS is an objective, easily automated measure of lipid-core plaque because it does not require subjective operator interpretation or offline analysis.²² Also, NIRS has improved sensitivity and diagnostic accuracy compared with IVUS, especially in calcified plaque and lesions with a small plaque burden.4 The combination of NIRS and IVUS is superior compared with NIRS or IVUS alone for more precise assessment of coronary plaque characteristics.⁴

Previous studies utilizing NIRS showed the following: (1) maxLCBI_4mm >400 was a distinguishing threshold for culprit segments in patients presenting with an acute ST-elevation myocardial infarction;²³ (2) maxLCBI_4mm ≥400 at a non-stented site was associated with cardiovascular events during 2 years of follow-up;²⁴ (3) maxLCBI_4mm ≥500 in a treated lesion was associated with higher prevalence of periprocedural myocardial infarction;²² and pathologic evaluation showed that NIRS combined with IVUS significantly improved the ability to detect more active, potentially vulnerable coronary atheroma.²⁵

Study limitations. Our study has several limitations. First, this is a registry study and included a small number of patients. Second, we only measured lipid contents for evaluation of plaque composition. Third, the pressure gradient across a lesion may affect the plaque characteristics with plaque stress or shear stress, but ΔFFR is an indirect parameter to reflect hemodynamic status of a coronary artery.

ΔFFR across a coronary artery lesion is related to lipid core burden assessed using NIRS-IVUS as well as morphologic characters such as lesion length and plaque burden. ΔFFR might be a meaningful predictor of high-risk plaque with high lipid content.