Abstract
Background:
The relationship between the features of morphologically unstable plaque and physiological lesion severity remains elusive. We aimed to investigate this relationship using optical coherence tomography (OCT)-derived high-risk plaque characteristics and fractional flow reserve (FFR) as the degree of anatomical and physiological stenosis severity.
Methods and Results:
We investigated 286 de novo intermediate and severe coronary lesions in 248 patients who underwent OCT and FFR examinations. Lesions were divided into tertiles based on either FFR or quantitative coronary angiographic diameter stenosis (QCA-%DS). The OCT findings were compared among the tertiles of FFR and QCA-%DS. FFR and QCA tertiles were defined as follows: FFR-T1 (FFR <0.74), FFR-T2 (0.74≤FFR≤0.81), and FFR-T3 (FFR >0.81); and QCA-T1 (%DS ≥61%), QCA-T2 (51%≤%DS<61%), and QCA-T3 (%DS <51%). The prevalence of thin-cap fibroatheroma (TCFA) was significantly greater in FFR-T1 (20.0%) than in FFR-T2 and FFR-T3 (7.0%, P=0.03 and 7.7%, P=0.04, respectively), although no significant differences were observed among the QCA tertiles.
Conclusions:
Physiological severity of coronary stenosis evaluated by FFR correlated with plaque instability in terms of TCFA. Preferable clinical outcomes for lesions with negative FFR based on the existing clinical evidence might be attributable to less likelihood of TCFA.
Fractional flow reserve (FFR) is the standard in decision-making for revascularization in the catheter laboratory and has become part of the clinical guidelines for the assessment of the physiological significance of epicardial coronary stenosis, as it is based on sound concepts and randomized clinical trials.1–4
The FAME 2 trial demonstrated that deferring percutaneous coronary intervention (PCI) in lesions with physiologically significant FFR values resulted in higher rates of progressive symptoms, unstable angina, and myocardial infarction within the follow-up period of 2 years,4
emphasizing the importance of other factors beyond anatomical stenosis severity. Although FFR identifies physiologically significant lesions, it is still unknown why FFR is likely to be associated with adverse outcomes or risk for acute coronary syndrome (ACS) resulting from plaque rupture and thrombosis.5–7
Optical coherence tomography (OCT) can identify the high-risk anatomical plaque features that have been established as fundamental to the processes of ACS and sudden cardiac death.8
To date, the precise relationships between high-risk or unstable plaque features and lesion-specific functional ischemia remain to be determined. We therefore hypothesized that FFR-defined lesion-specific physiological properties are associated with OCT-identified unstable plaque features, such as thin-cap fibroatheroma (TCFA), in patients with coronary artery disease. To test this hypothesis, we evaluated the relationship between FFR values and the lesion-specific characteristics obtained by coronary angiography (CAG) and OCT.
Methods
Study Population
The institutional database of cardiac catheterization cases at Tsuchiura Kyodo General Hospital between September 2011 and August 2016 was screened to identify patients with coronary stenosis for whom OCT imaging and FFR measurement had been performed during the same procedure. Patients were eligible for the analysis if they fulfilled the following criteria: >20 years old; stable angina pectoris; ACS with non-culprit lesion observations; physiological assessment by pressure wire for de novo coronary lesions that indicated intermediate-to-obstructive stenosis, visually estimated on the angiogram; and OCT imaging performed for the lesions. Culprit lesions of ACS were excluded. A total of 280 patients with 313 lesions were identified for the analysis. We excluded patients with angiographically significant left main disease, a history of coronary artery bypass surgery, renal insufficiency with a baseline serum creatinine level >2.0 mg/dL, or congestive heart failure. Lesions requiring balloon angioplasty prior to OCT imaging, tandem lesions, and lesions with insufficient OCT and FFR data acquisition were excluded. Therefore, the final dataset for this study included 286 coronary lesions from 248 patients (Figure 1). Baseline patient characteristics were collected by reviewing medical charts. Dyslipidemia was defined as low-density lipoprotein cholesterol >140 mg/dL, high-density lipoprotein cholesterol <40 mg/dL or triglyceride >150 mg/dL. The study protocol was approved by the institutional review board, and all patients provided written informed consent prior to catheterization.
Each patient initially underwent standard selective CAG for the assessment of coronary anatomy via the radial artery using a 6F system. Coronary angiograms were analyzed quantitatively using a CMS-MEDIS quantitative CAG (QCA) system (Medis Medical Imaging Systems, Leiden, The Netherlands) to measure lesion length, minimum lumen diameter, reference lumen diameter, and percent diameter stenosis (%DS) at the target lesion. All patients received a bolus injection of heparin (5,000 IU) before the procedure, and an additional bolus injection of 2,000 IU was administered every hour as needed to maintain an activated clotting time >250 s. An intracoronary bolus injection of nitroglycerin (0.2 mg) was administered at the start of the procedure and repeated every 30 min. QCA measurements were performed in diastolic frames from orthogonal projections. The OCT imaging and FFR measurements were performed prior to PCI for the target lesion or after the PCI procedure for non-target intermediate lesions in patients treated by PCI or after diagnostic CAG in patients with deferral of PCI.
FFR Measurements
For FFR measurements, a RadiAnalyzer Xpress console with PressureWire Certus (St. Jude Medical, St Paul, MN, USA) was used to measure the distal coronary pressure. FFR was measured in vessels that were deemed clinically indicated for evaluation, presenting an angiographic stenosis between 20% and 90%. After administration of nitroglycerin, a pressure-monitoring guide wire was distally advanced to the stenosis. Hyperemia was induced by intravenous infusion of adenosine 5'-triphosphate at a rate of 160 μg･kg−1･min−1. FFR was calculated by dividing the mean distal pressure by the mean aortic pressure during hyperemia.
OCT Image Acquisition and Analysis
OCT images were acquired prior to any interventional procedures using frequency-domain OCT systems (ILUMIEN®, St. Jude Medical Inc. LUNAWAVE®, Terumo, Tokyo, Japan). The technique of OCT image acquisition has been described elsewhere.9,10
The OCT imaging data were digitally stored and analyzed offline. All OCT images were analyzed using proprietary software (LightLab Imaging or Terumo) based on expert consensus documents at the Tsuchiura Kyodo Hospital OCT Laboratory.9,10
The lumen contour was semi-automatically traced with proprietary software on the cross-sectional images, and the contour was manually corrected by the investigator if needed. The cross-section with the smallest luminal area value was defined as the minimal lumen area (MLA). Qualitative parameters were assessed by 2 experienced investigators (E.U. and T.Y.) who were blinded to the angiographic data and clinical characteristics. Discordance between the 2 investigators was resolved by consensus reading. Plaques were classified into 2 categories: fibrous (homogeneous and high-backscattering region) or lipid (low-signal region with a diffuse border).9
The lipid angle was determined on every cross-section, and the largest obtained value was considered the maximal lipid arc. A plaque with a maximal lipid arc >90 degrees was defined as a fibroatheroma. Thereafter, the lipid arc was recorded at 1-mm intervals throughout the entire length of the lesion, and the average value was defined as the mean lipid arc.11,12
The lipid length was determined by measuring the longitudinal length of the contiguous cross-sections that conformed with the definition of fibroatheroma. A lipid volume index was calculated as the mean lipid arc multiplied by the lipid length, as previously reported.11
The fibrous cap thickness was measured 3 times at its thinnest part, and the average value was calculated for the analysis. A TCFA was defined as a fibroatheroma with a fibrous cap thickness <80 µm.13
The presence or absence of plaque rupture, intraluminal thrombus, macrophage accumulation, microchannels, calcified plaque, and calcified nodule were also recorded according to previous reports (Figure 2).9,10,14–16
The calcification angle was determined on every cross-section, and the largest obtained value was considered the maximal calcification arc. The calcification length was determined by measuring the longitudinal length of the contiguous cross-sections that conformed with the definition of calcified plaque.
A total of 286 lesions were divided into FFR tertiles, and the OCT findings were compared among these tertiles. For comparison, we also divided all studied lesions into tertiles on the basis of the QCA results, and the OCT findings were compared among QCA tertiles. Furthermore, we evaluated the predictive factors for the presence of TCFA, considering the FFR values as continuous measures.
Statistical Analysis
Statistical analyses were performed using SPSS version 23.0 (SPSS, Inc., Chicago, IL, USA), R statistics version 3.2.3 (The R foundations for Statistical Computing, Vienna, Austria), or MedCalc version 16 (MedCalc Software, version 11.6, Mariakerke, Belgium), depending on the statistical method. Patient demographics are presented as n (%) where appropriate. Categorical data are expressed as numbers and percentages and compared using χ2
or Fisher exact tests as appropriate. Continuous variables are expressed as the mean±standard deviation for normally distributed variables or as median (25–75th percentile) for non-normally distributed variables and compared using Student’s t tests and Mann-Whitney U tests, respectively. Differences in each variable among the tertiles were analyzed by MedCalc using the Kruskal-Wallis test, followed by post-hoc comparisons using pairwise comparisons of subgroups or analysis of variance as appropriate. We further evaluated the relationship between the presence of TCFA and clinical parameters, angiographic parameters, FFR values, OCT MLA, and other potential confounders by multivariate logistic regression analyses (stepwise-forward method). The associated variables in univariate analyses (P<0.10) were included in the model. A generalized estimating equations approach was used to consider within-subject correlation because of multiple vessels being analyzed within a single patient. A receiver-operating characteristic (ROC) curve analysis was performed to identify the optimal cutoff value of FFR to predict the presence of TCFA. The optimal cutoff point in the ROC analysis was defined as the value with the highest sum of sensitivity and specificity. The statistical analysis was performed on a per-lesion basis. P<0.05 indicated statistical significance.
Results
Patients’ Characteristics, Angiographic Data, and FFR Values
The baseline characteristics of the 248 patients in this study are summarized in
Table 1. For a total of 286 lesions, the mean FFR value, median FFR value, and mean %DS were 0.76±0.11, 0.78 [IQR 0.70–0.84], and 56.7±11.7%, respectively. The distributions of the FFR values and %DS are shown in
Figure 3. FFR tertiles were defined as follows: 1st tertile (FFR-T1; FFR <0.74, n=95), 2nd tertile (FFR-T2; 0.74≤FFR≤0.81, n=100), and 3rd tertile (FFR-T3; FFR >0.81, n=91). QCA tertiles were defined as follows: 1st tertile (QCA-T1); %DS ≥61% (n=98), 2nd tertile (QCA-T2); 51%≤%DS<61% (n=96), and 3rd tertile (QCA-T3); %DS <51% (n=92).
Table 1.
Characteristics of Study Patients With Coronary Lesions
| |
Total
(n=248) |
| Age, years |
66 (60–72) |
| Female sex, n (%) |
46 (18.5) |
| Hypertension, n (%) |
169 (68.1) |
| Dyslipidemia, n (%) |
167 (67.3) |
| Diabetes, n (%) |
91 (36.7) |
| Previous MI, n (%) |
89 (35.9) |
| Total cholesterol, mg/dL |
168 (147–197) |
| LDL cholesterol, mg/dL |
95 (75–117) |
| HDL cholesterol, mg/dL |
45 (38–54) |
| Medications, n (%) |
| ACEI or ARB |
91 (36.8) |
| β-blocker |
119 (48.2) |
| Statin |
167 (67.3) |
| Echocardiographic LVEF, % |
65 (59–69) |
Values are n (%), median (25–75th percentile). ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; MI, myocardial infarction.
Table 2
shows the baseline and OCT findings according to FFR tertiles. OCT-defined MLA showed graded differences that were in proportion to the FFR tertiles (Table 2). The prevalence of TCFA was approximately 3-fold higher in the lowest FFR tertile (FFR-T1, functionally the most severe FFR tertile) than in the other 2 tertiles (P<0.01). Similar results were obtained when other definitions of TCFA were used in the analysis (Table S1). The fibrous cap thickness tended to be thinner in the FFR-T1 tertile than in the other 2 tertiles (P=0.06). Lipid volume index was significantly higher in the FFR-T1 tertile than in the other 2 tertiles (P<0.01). Lipid arc, lipid length, and macrophage accumulation also showed a trend towards being higher in FFR-T1. The baseline clinical characteristics and OCT findings according to QCA-%DS tertile are also presented in
Table 2. No significant difference in the frequency of TCFA and degree of lipid volume index among the %DS tertiles was detected.
Figure 4
shows the relative prevalence of TCFA and median lipid volume index according to FFR and %DS tertiles.
Table 2.
OCT Findings According to FFR and %DS Tertiles
| |
Total
(n=286) |
FFR <0.74
(T1, n=95) |
0.74≤FFR≤0.81
(T2, n=100) |
FFR >0.81
(T3, n=91) |
P value |
P value |
| T1 vs. T2 |
T1 vs. T3 |
T2 vs. T3 |
| MLA, mm2 |
1.43
(1.00–2.00) |
1.00
(0.73–1.36) |
1.44
(1.18–1.94) |
1.96
(1.45–2.50) |
<0.01 |
<0.01 |
<0.01 |
<0.01 |
| TCFA, n (%) |
35 (12.2) |
19 (20.0) |
7 (7.0) |
7 (7.7) |
<0.01 |
0.03 |
0.04 |
1.00 |
| Ruptured plaque, n (%) |
29 (10.1) |
12 (12.6) |
6 (6.0) |
11 (12.1) |
0.23 |
|
|
|
| Thrombus, n (%) |
16 (5.6) |
6 (6.3) |
2 (2.0) |
8 (8.8) |
0.12 |
|
|
|
| Microchannels, n (%) |
97 (33.9) |
35 (36.8) |
31 (31.0) |
31 (34.1) |
0.69 |
|
|
|
| Macrophage accumulation, n (%) |
111 (38.8) |
43 (45.3) |
28 (28.0) |
40 (44.0) |
<0.01 |
0.05 |
0.88 |
0.05 |
| Calcified plaque, n (%) |
99 (34.6) |
31 (34.1) |
40 (40.0) |
28 (29.5) |
0.30 |
|
|
|
| Calcified nodule, n (%) |
19 (6.6) |
4 (4.2) |
10 (10.0) |
5 (5.5) |
0.23 |
|
|
|
| Calcification arc, degrees |
59.2±90.8 |
46.8±78.7 |
74.2±100.8 |
55.6±89.6 |
0.09 |
|
|
|
| Calcification length, mm |
4.10±7.27 |
3.81±7.50 |
5.21±7.86 |
3.17±6.20 |
0.13 |
|
|
|
Minimum fibrous cap
thickness, μm |
140
(93–215) |
130
(84–188) |
163
(107–210) |
150
(108–280) |
0.06 |
|
|
|
| Lipid length, mm |
6.5
(4.0–9.8) |
7.0
(5.0–11.5) |
6.0
(4.0–9.5) |
6.0
(4.0–9.0) |
0.02 |
0.09 |
0.03 |
0.77 |
| Max lipid arc, degrees |
195±58 |
209±56 |
179±57 |
197±59 |
<0.01 |
<0.01 |
0.22 |
0.32 |
| Lipid volume index |
987
(594–1,628) |
1,116
(496–1,678) |
652
(199–1,295) |
628
(0–1,140) |
<0.01 |
0.02 |
<0.01 |
0.86 |
| |
Total
(n=286) |
%DS ≥61%
(T1, n=98) |
51%≤%DS<61%
(T2, n=96) |
%DS <51%
(T3, n=92) |
P value |
P value |
| T1 vs. T2 |
T1 vs. T3 |
T2 vs. T3 |
| MLA, mm2 |
1.44
(1.00–2.00) |
1.13
(0.78–1.56) |
1.44
(1.08–1.98) |
1.70
(1.27–2.38) |
<0.01 |
<0.01 |
<0.01 |
<0.01 |
| TCFA, n (%) |
33 (11.5) |
14 (14.3) |
12 (12.5) |
7 (7.6) |
0.33 |
|
|
|
| Ruptured plaque, n (%) |
29 (10.1) |
12 (12.2) |
10 (10.4) |
7 (7.6) |
0.57 |
|
|
|
| Thrombus, n (%) |
16 (5.6) |
6 (6.1) |
4 (4.2) |
6 (6.5) |
0.75 |
|
|
|
| Microchannels, n (%) |
97 (33.9) |
41 (41.8) |
27 (28.1) |
29 (31.5) |
0.11 |
|
|
|
| Macrophage accumulation, n (%) |
111 (38.8) |
44 (44.9) |
37 (38.5) |
30 (32.6) |
0.22 |
|
|
|
| Calcified plaque, n (%) |
99 (34.6) |
28 (28.6) |
34 (35.4) |
37 (40.2) |
0.24 |
|
|
|
| Calcified nodule, n (%) |
19 (6.6) |
9 (9.2) |
5 (5.2) |
5 (5.4) |
0.46 |
|
|
|
| Calcification arc, degrees |
59.2±90.8 |
42.1±76.6 |
69.2±99.6 |
66.9±93.3 |
0.10 |
|
|
|
| Calcification length, mm |
4.10±7.27 |
3.17±6.16 |
4.13±7.49 |
5.05±8.04 |
0.17 |
|
|
|
Minimum fibrous cap
thickness, μm |
140
(93–215) |
137
(91–192) |
143
(90–225) |
142
(100–228) |
0.49 |
|
|
|
| Lipid length, mm |
6.5
(4.0–9.8) |
6.3
(4.0–9.9) |
6.0
(4.0–10.5) |
7.0
(4.5–9.0) |
0.78 |
|
|
|
| Max lipid arc, degrees |
195±58 |
206±60 |
185±60 |
193±54 |
0.08 |
|
|
|
| Lipid volume index |
987
(594–1,628) |
732
(317–1,607) |
663
(70–1,441) |
885
(274–1,303) |
0.59 |
|
|
|
Values are n (%), mean±standard deviation, or median (25–75th percentile). DS, diameter stenosis; FFR, fractional flow reserve; MLA, minimal luminal area; OCT, optical coherence tomography; TCFA, thin-cap fibroatheroma.
Comparisons of the anatomical and physiological findings according to the presence or absence of TCFA are shown in
Table 3. FFR and %DS were significantly associated with the presence of TCFA. In the univariate analysis with generalized estimating equations approach, these 2 factors, a history of dyslipidemia and the echocardiographic left ventricular ejection fraction (LVEF) were associated with the presence of TCFA (P<0.10). None of the other clinical factors were associated with TCFA (Table S2). In the multivariate analysis, a history of dyslipidemia, the echocardiographic LVEF, and FFR remained as significant and independent predictors of TCFA (P<0.05) (Table 4). ROC analysis indicated that an FFR value of 0.74 was the best cutoff value for predicting the presence of TCFA (sensitivity 60.6%, specificity 66.4%, negative predictive value 92.8%, positive predictive value 19.0%, accuracy 65.7%, area under the curve 0.631). Our results indicated that FFR >0.74 could preclude the presence of TCFA in a lesion with a probability >92%, although the specificity was low.
Table 3.
Anatomical and Physiological Findings According to the Presence or Absence of TCFA
| |
Total
(n=286) |
TCFA (+)
(n=33) |
TCFA (−)
(n=253) |
P value |
| Lesion location, n (%) |
|
|
|
0.63 |
| LAD |
168 (58.7) |
18 (54.5) |
150 (59.3) |
|
| RCA |
76 (26.6) |
11 (33.3) |
65 (25.7) |
|
| LCX |
42 (14.7) |
4 (12.1) |
38 (15.0) |
|
| MLD, mm |
1.18 (0.92–1.42) |
1.16 (0.85–1.33) |
1.18 (0.92–1.44) |
0.35 |
| Reference diameter, mm |
2.68 (2.27–3.08) |
2.67 (2.28–3.12) |
2.68 (2.27–3.07) |
0.69 |
| Stenosis, % |
56.7±11.7 |
60.6±12.5 |
56.1±11.6 |
0.04 |
| Lesion length, mm |
11.8 (8.61–16.1) |
11.2 (7.96–15.7) |
11.8 (8.73–16.2) |
0.79 |
| OCT-derived MLA, mm2 |
1.43 (1.00–2.00) |
1.23 (0.86–1.82) |
1.44 (1.03–2.04) |
0.19 |
| FFR |
0.78 (0.70–0.84) |
0.73 (0.60–0.80) |
0.78 (0.72–0.84) |
0.01 |
Values are n (%), mean±standard deviation, or median (25–75th percentile). LAD, left anterior descending artery; LCX, left circumflex artery; MLD, minimal luminal diameter; RCA, right coronary artery. Other abbreviations as in Table 2.
Table 4.
Univariate and Multivariate Logistic Regression Analyses for Prediction of TCFA
| |
Univariate logistic regression |
Multivariate logistic regression |
| OR |
95% CI |
P value |
OR |
95% CI |
P value |
| Dyslipidemia |
2.316 |
0.918–5.853 |
0.075 |
2.848 |
1.091–7.433 |
0.033 |
| Echocardiographic LVEF, % |
0.968 |
0.937–0.999 |
0.040 |
0.963 |
0.932–0.994 |
0.022 |
| Angiographic DS, % |
1.033 |
1.001–1.066 |
0.041 |
– |
– |
– |
| FFR |
0.018 |
0.001–0.341 |
0.007 |
0.029 |
0.001–0.846 |
0.040 |
CI, confidence interval; OR, odds ratio. Other abbreviations as in Tables 1,2.
Discussion
The main findings of the present study were: (1) physiological severity represented by FFR was significantly associated with the frequency of TCFA in angiographically intermediate-to-obstructive stenosis; (2) angiographic stenosis represented by DS showed a non-significant association with the frequency of TCFA in the present cohort; and (3) FFR value >0.74 precluded the presence of TCFA with a negative predictive value of 92%; in contrast, lesions with FFR <0.74 had an almost 3-fold higher risk for the presence of TCFA relative to lesions showing FFR ≥0.74.
To the best of our knowledge, this is the first study to demonstrate a significant association between features of coronary vulnerable plaque assessed by OCT, especially TCFA and lipid volume index, and physiological severity determined by FFR, in angiographically intermediate-to-obstructive stenoses. Although a number of studies have investigated the correlation between MLA as measured by OCT and physiological significance as assessed by FFR,17,18
little is known regarding the effect of plaque morphology on physiological disturbances. A recent intravascular study using OCT failed to show a significant association between FFR and OCT-derived morphologic plaque characteristics, although the study population was small (n=110) and there were only 12 TCFA patients in the total study cohort.19
Nevertheless, previous studies using coronary computed tomography have demonstrated significant associations of high-risk plaque features with FFR-positive ischemia.20–22
Specifically, the presence of a large plaque volume, the volume of a low-attenuation plaque, and a greater remodeling index were shown to be predictive of lower FFR, regardless of stenotic area,20
in accord with our results. Those results suggest that local impairment of coronary endothelial function by large, lipid-rich plaques is a potential explanation for the discordance between anatomical stenosis severity and functional disturbance. In histopathological and OCT studies, a significant association between a thin fibrous cap and a large necrotic core has been reported,23,24
and plaques with a necrotic core may have abundant oxidative stress and intimal inflammation, with impaired vasodilatory ability.25
This mechanism may be related to endothelial dysfunction, resulting in functional stenosis with inability to dilate under hyperemic induction.26
Theoretically, a pressure gradient across the stenosis is related to viscous friction, separation, and turbulence.27
Therefore, qualitative lesion characteristics, such as lipid-rich plaques with TCFA and microstructures, may affect FFR by producing greater flow resistance and causing energy loss of fluid. As contrasted with the association between coronary vulnerable plaque features, especially TCFA and lipid volume index, and physiological severity determined by FFR, the prevalence of macrophage accumulation showed no graded propensity according to FFR tertiles. Although several previous OCT studies demonstrated data suggesting an association of macrophage accumulation with vulnerable plaque,11,28
macrophages can infiltrate the coronary arterial wall in earlier stages of atherosclerosis as described in a histopathological study.8
In other words, macrophage accumulation may not necessarily indicate the presence of a large necrotic core within the plaque. Our results suggest that only advanced atherosclerosis with a large necrotic core and thin fibrous cap can disturb coronary flow.
Tian et al have reported a significant association between the prevalence of OCT-defined TCFA and %DS as assessed by QCA,29
whereas the present study showed only a non-significant trend towards more frequent TCFA as %DS increased (Table 2). Discordant results between the 2 studies may be attributed to multiple causes. Importantly, the patient population in the present study predominantly consisted of those with stable angina pectoris and included 12.2% of ACS non-target/non-culprit lesions, whereas the other study included a significant proportion of non-target/non-culprit lesions in ACS patients (59.0%). In general, patients with ACS tend to have more vulnerable plaque features, even in non-culprit lesions, than stable patients.11
Therefore, the frequency of TCFA was significantly higher in the previous study than in the present study. Nevertheless, our results showed a weak, non-significant trend towards an increased prevalence of TCFA in accordance with %DS severity, and %DS remained as a significant factor for the prevalence of TCFA in our univariate analysis, in accord with previous work (Table 4). In addition, in the study by Tian et al, both time-domain and frequency-domain OCT systems were used, whereas we used only frequency-domain OCT. This difference may have affected the measurements of the fibrous cap and consequently may have produced the discrepancies in the thickness of the TCFA (55 μm vs. 62 μm) and the prevalence of TCFA (%DS <70%: 18% vs. 10%) between the 2 studies.
In this study, the presence of TCFA was significantly associated with FFR, supporting the conclusion of a recent study reporting that FFR has an independent relationship with subsequent outcomes30
because TCFA and its rupture is the major cause of coronary thrombosis, which results in myocardial infarction and sudden cardiac death.5,6
In the present study, the frequency of TCFA and the degree of the lipid volume index were clearly distinct between the 1st and 2nd tertiles of FFR, whereas %DS by QCA was unable to specify a group with a significantly high frequency of TCFA (Figure 4). The ROC curve suggested FFR ≤0.74 (sensitivity: 60.6%; specificity: 66.4%; positive predictive value: 19.0%; negative predictive value: 92.8%; accuracy: 65.7%) as the best cutoff value for predicting TCFA. Notably, FFR >0.74 showed a high negative predictive value (92.8%) to preclude TCFA, which may explain at least in part why revascularization may be safely deferred in the absence of FFR ≤0.80, even in lesions with angiographically severe stenosis.3
In patients with stable CAD, FFR may preclude patients at high risk for future adverse cardiac events caused by TCFA and its rupture and identify patients who harbor vulnerable plaques and are at high risk for future ACS.
Study Limitations
The results of the present study should be interpreted in light of certain limitations. First, this study included subjects from a single center and was retrospective, making selection bias unavoidable. Second, the decision to perform FFR measurements was at the discretion of the operator. Third, although the prevalence of thrombus was relatively low in the present study (5.6%), other plaque features, such as macrophages and microchannels, were not thoroughly validated and precluded the interpretation of lesion characteristics. Fourth, the definition of OCT-defined TCFA is somewhat different from the histological definition. Previous studies used several OCT definitions of TCFA. We also analyzed and compared the prevalence of TCFA on the basis of other definitions among FFR tertiles and QCA-%DS tertiles (Table S1). As a result, the prevalence of TCFA was 2–3-fold greater in physiologically significant lesions than in less-severe lesions, although no significant differences were observed among the QCA tertiles. Although our major findings were not critically affected by the change in the definition of TCFA, a minor loss of significant differences between FFR-T1 and FFR-T3 were observed. Finally, we analyzed only proximally located single lesions per vessel, and the plaque component at non-target lesions in the same vessel might have affected the FFR value; however, we carefully performed a pressure pull-back procedure to exclude tandem lesions.
Conclusions
Physiological severity of coronary stenosis evaluated by FFR correlated with plaque instability in terms of TCFA. Preferable clinical outcomes for lesions with negative FFR in the previous clinical trials might be attributable to less likelihood of TCFA.
Supplementary Files
Supplementary File 1
Table S1.
Prevalence of TCFA stratified with FFR and %DS tertiles based on several definitions
Table S2.
Univariate logistic regression analyses for prediction of TCFA
Please find supplementary file(s);
http://dx.doi.org/10.1253/circj.CJ-17-0122
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