Relationship between high shear stress and OCT-verified thin-cap fibroatheroma in patients with coronary artery disease.

High-risk coronary plaques have been considered predictive of adverse cardiac events. Both wall shear stress (WSS) in patients with hemodynamically significant lesions and optical coherence tomography (OCT) -verified thin-cap fibroatheroma (TCFA) are associated with plaque rupture, the most common underlying mechanism of acute coronary syndrome. The aim of the study was to test the hypothesis that invasive coronary angiography-based high WSS is associated with the presence of TCFA detected by OCT in obstructive lesions. From a prospective study of patients who underwent OCT examination for angiographically obstructive lesions (Yellow II), we selected patients who had two angiographic projections to create a 3-dimensional reconstruction model to allow assessment of WSS. The patients were divided into 2 groups according to the presence and absence of TCFA. Mean WSS was assessed in the whole lesion and in the proximal, middle and distal segments. Of 70 patients, TCFA was observed in 13 (19%) patients. WSS in the proximal segment (WSSproximal) (10.20 [5.01, 16.93Pa]) and the whole lesion (WSSlesion) (12.37 [6.36, 14.55Pa]) were significantly higher in lesions with TCFA compared to WSSproximal (5.84 [3.74, 8.29Pa], p = 0.02) and WSSlesion (6.95 [4.41, 11.60], p = 0.04) in lesions without TCFA. After multivariate analysis, WSSproximal was independently associated with the presence of TCFA (Odds ratio 1.105; 95%CI 1.007-1.213, p = 0.04). The optimal cutoff value of WSSproximal to predict TCFA was 6.79 Pa (AUC: 0.71; sensitivity: 0.77; specificity: 0.63 p = 0.02). Our results demonstrate that high WSS in the proximal segments of obstructive lesions is an independent predictor of OCT-verified TCFA.

Introduction

Wall shear stress (WSS) is the tangential force produced by the luminal blood flow on the vascular intima. Whereas physiological WSS facilitates atheroprotective signals, low WSS contributes to endothelial inflammation and proatherogenic milieu, and high WSS leads to matrix metalloproteinase activation with attenuation of fibrous cap and enlargement of necrotic core with expansive remodeling [1-5]. What is not very clear is whether high WSS induces high-risk plaques feature or vice versa. Nonetheless, high WSS may underlie plaque rupture or erosion and result in an acute coronary event [6-8]. In patients with hemodynamically significant lesions from FAME II trial, angiography-based higher WSS in the proximal segments of the lesions was a predictor of subsequent myocardial infarction (MI) within 3 years [9]. Correlation between angiography-based WSS and fibrous cap thickness in obstructive lesions has not been previously described. In the present study, we identified angiography-based WSS using computational fluid dynamics (CFD) and correlated it to OCT-verified thin-cap fibroatheroma (TCFA) in obstructive lesions from patients with coronary artery disease (CAD). We hypothesized that the angiography-based high WSS will be associated with the presence of high-risk plaques detected by OCT.

Materials and methods

Study population and design

The protocol of YELLOW II study was approved by the Institutional Review Board of the Mount Sinai School of Medicine. All patients provided written informed consent. YELLOW II study design and results have been previously described [10]. Briefly, in the prospective study, 85 patients with multivessel stable CAD requiring staged percutaneous coronary intervention (culprit lesion initially, obstructive non-culprit lesion later) underwent multimodality intravascular imaging with OCT, intravascular ultrasound (IVUS) and near-infrared spectroscopy (NIRS) of the obstructive non-culprit lesion before and after 8–12 week intensive statin therapy. There were 85 lesions in 85 patients; all lesions were stented during follow-up procedure. 85 patients, 70 patients were selected who had 2 angiographic projections at least 25 degree apart at baseline to allow accurate coronary geometry reconstruction and WSS assessment. The patients were divided into 2 groups according to the presence and absence of TCFA.

OCT and NIRS/IVUS image acquisition and analysis

OCT examination was performed using C7-XRTM OCT Intravascular Imaging System and DragonflyTM imaging catheter (Abbott Vascular, Santa Clare, CA). The OCT catheter was inserted at least 10 mm distally to the study target lesion. Commercially available TVC (True Vessel Characterization) imaging system with the TVC insight catheter (Infraredx, Burlington, Massachusetts) was used to acquire combined NIRS and IVUS image for the same lesion.

OCT images were analyzed according to the current consensus standards [11] as previously described in YELLOW II study [10]. Lipid core was identified as a signal-poor region with poorly delineated borders, little or no signal backscattering, and an overlying signal-rich layer, the fibrous cap (S1 Fig). Lipid arc was measured at 1-mm interval to obtain the maximum and average values and calculate lipid volume index as a product of the average lipid arc and lipid length. Fibrous cap thickness (FCT) was measured 3 times at its thinnest part and the average value was calculated. OCT defined lipid rich plaque (LRP) was defined as lipid plaque with maximal lipid arc more than 90°. TCFA was defined as a LRP with FCT less than 65 μm.

Raw spectroscopic information from NIRS chemograms was transformed into a probability of lipid that was mapped to a red-to-yellow colour scale, with a low probability of lipid shown as red and a high probability of lipid shown as yellow (S1 Fig). Lipid core burden index (LCBI) was calculated by dividing the number of yellow pixels (probability ≥0.6) by the total number of viable pixels within the plaque multiplied by 1000. Maximal LCBI for the whole lesion (maxLCBIlesion), within any 10-mm (maxLCBI10mm) or 4-mm segment (maxLCBI4mm) were calculated for each lesion [12]. IVUS images were analyzed off-line using computerized planimetry software (echoPlaque 4.0, INDEC Medical Systems, Inc, Santa Clara, CA) according to the current guidelines as previously described [12, 13]. Quantitative analysis included measurements of the external elastic membrane (EEM) and lumen cross-sectional areas (CSA) at 1-mm interval. Plaque+media CSA was calculated as EEM minus lumen CSA, and plaque burden was calculated as [plaque+media/EEM CSA*100]. Simpson’s rule was used to estimate EEM volume and total atheroma volume (TAV); percent atheroma volume (PAV) was calculated as TAV divided by EEM volume. OCT and NIRS/IVUS image analysis was performed at the Cardiovascular Research Foundation (New York, NY), which had no knowledge of patients clinical presentation.

Angiographic reconstruction and computational fluid dynamics

Three-dimensional quantitative coronary angiography (3D-QCA) and vessel reconstructions were performed using two end-diastolic projections at least 25° apart with commercially available software QAngio XA 3D RE (Medis, Leiden, The Netherlands) followed by automatic quantification of 3D lesion length, minimal and reference diameters, and percent diameter stenosis (DS) (Fig 1A) [14]. Patient-specific vessel geometries were exported to ANSYS Fluent 19.0 (ANSYS Inc. Pennsylvania, USA) for CFD simulations (Fig 1B). Patient-specific inlet velocities were calculated using frame counts ([segment length assessed by angiography]*[acquisition speed]/[last frame number–first frame number]) and applied as inlet boundary conditions with a flat profile as previously described [9]. For bifurcation lesions, outlet boundary conditions were computed according to Murray’s law [15]. The density and viscosity of blood were set to 1,050 kg/m3 and 0.0035kg/m·sec respectively. No-slip boundary condition was applied at the vessel wall and blood flow was assumed to be steady. Each angiography-defined lesion was divided into 3 equal parts: proximal, middle and distal following the segment definitions introduced in the WSS FAME II sub-study [9]. Upstream and downstream segments were defined as segments proximal or distal to the lesion. Mean and maximal WSS were calculated for each segment and the whole lesion and compared between TCFA and non-TCFA lesions detected by OCT (Fig 1C). The optimal mesh size was determined based on a mesh refinement study to ensure that the results are not affected by the element size. The average number of finite elements in the study was 287,265, and the mean surface area was 0.021 mm2. Three-dimensional geometry reconstruction and CFD calculations were done at the Mount Sinai Intravascular Imaging Laboratory (New York, NY).

Fig 1

Angiography-based assessment of wall shear in obstructive coronary lesions.

CFD analysis was performed in models reconstructed from two angiographic projections (A); Mean WSS was calculated in the whole lesion and three lesion sub-segments: proximal, middle and distal. OCT imaging was performed in the same lesion to identify TCFA lesions.

Statistical analysis

Continuous variables are presented as a mean ± standard deviation or median (interquartile range) depending on data distribution. The data was compared with t-test or Mann-Whitney U test. Categorical variables are presented as frequency counts and percentages and were compared with chi-square test or Fisher exact test as appropriate. Logistic regression analysis was performed to detect the predictors of the presence of TCFA. A receiver operating characteristics curve was used to detect the optimal cutoff value for detecting TCFA. P value less than 0.05 was considered to be statistically significant and all statistical analyses were performed using SPSS 24.0 (IBM Corp. Armonk, NY).

Results

Patient characteristics

OCT-verified TCFA was observed in 13 (19%) of the patients. Table 1 summarizes patient baseline characteristics according to the presence or absence of TCFA lesion. Age, gender, the prevalence of hypertension, hypercholesterolemia and diabetes mellitus were comparable for the 2 groups. Statin was similarly used and laboratory data including LDL-cholesterol were not different between the groups.

Table 1

Patient baseline characteristics.

	TCFA (n = 13)	No TCFA (n = 57)	P-value
Age	63.69 ± 11.45	63.12 ± 10.34	0.86
Male	7 (53.8)	39 (68.4)	0.25
Hypertension	11 (84.6)	51 (89.5)	0.32
Hypercholesterolemia	10 (76.9)	50 (87.7)	0.17
Diabetes mellitus	6 (46.2)	24 (42.1)	0.87
Current smoking	1 (7.7)	11 (19.3)	0.25
Prior MI	1 (7.7)	8 (14.0)	0.45
Prior PCI	0 (0)	19 (33.3)	0.01
Statin use	10 (76.9)	47 (82.5)	0.45
Coronary vessel			0.29
LAD	5 (38.5)	25 (43.9)
LCX	2 (15.4)	18 (31.6)
RCA	6 (46.2)	14 (24.6)
Total cholesterol, mg/dl	151.0 (127.0, 188.5)	137.5 (144.8, 165.5)	0.22
LDL cholesterol, mg/dl	84.8 (54.1, 116.6)	74.8 (55.9, 91.3)	0.39
HDL cholesterol, md/dl	41.0 (32.5, 54.0)	39.5 (33.0, 47.5)	0.82
Triglyceride, mg/dl	122.0 (77.5, 156.5)	99.0 (63.0, 144.3)	0.29

Values are mean ± SD, median (interquartile range) or n (%); TCFA = thin-cap fibroatheroma (cap ≤65μm); LAD = left anterior descending artery; LCX = left circumflex coronary artery; RCA = right coronary artery; LDL = low-density lipoproteins; HDL = high-density lipoproteins.

OCT and NIRS/IVUS findings

OCT-verified minimum fibrous cap thickness was 60 μm (interquartile range [IQR]: 50 to 60μm) in TCFA lesions and 100 μm (IQR: 90 to 130μm, p<0.01) in lesions without TCFA (Table 2). TCFA lesions had a higher prevalence of LRP (100%) compared to no TCFA group (73.7%, p = 0.03). Lipid content including lipid arc maximum, lipid length and lipid volume index were higher in TCFA lesions compared lesions without TCFA. The prevalence of calcium and calcium arc were comparable between the groups. There were no differences in IVUS-defined TAV, PAV and plaque burden between lesions with and without TCFA. NIRS-defined maxLCBIlesion, maxLCBI10mm and maxLCBI4mm were significantly higher in TCFA lesions.

Table 2

Intravascular imaging data.

	TCFA (n = 13)	No TCFA (n = 57)	P-value
OCT
Minimum lumen CSA, mm2	1.64 ± 0.69	1.92 ± 0.69	0.19
Lipid rich plaque	13 (100)	42 (73.7)	0.03
Lipid arc maximum, °	189.2 ± 66.7	134.3 ± 74.9	0.02
Lipid arc average, °	123.3 ± 37.3	95.8 ± 54.2	0.04
Lipid length, mm	8.4 (7.3, 10.4)	4.6 (2.3, 7.1)	< 0.01
Lipid volume index, ° x mm	1009.6 (663.5, 1296.0)	432.7 (215.9, 774.2)	< 0.01
Minimum fibrous cap thickness, μm	60 (50, 60)	100 (90, 130)	< 0.01
Macrophage	13 (100)	57 (100)	-
Macrophage arc maximum, °	155.0 (118.5, 221.8)	121.0 (84.0, 158.0)	0.02
Macrophage length, mm	14.1 (10.2, 19.6)	8.0 (5.0, 13.5)	0.02
Thrombus	6 (50.0)	5 (8.8)	< 0.01
Plaque rupture	4 (30.8)	5 (8.8)	0.06
Calcium deposition	11 (84.6)	52 (91.2)	0.39
Calcium arc maximum, °	84.5 (76.8, 205.0)	101.0 (67.0, 161.0)	0.92
IVUS
EEM volume, mm3	348.8 (193.4, 448.8)	289.0 (189.7, 359.0)	0.29
TAV, mm3	214.9 (126.0, 306.4)	164.2 (94.9, 234.0)	0.24
PAV, %	64.70 (60.35, 66.55)	61.10 (54.25, 66.20)	0.15
Plaque burden, %	77.86 ± 6.54	75.77 ± 7.25	0.34
Plaque plus media, mm2	7.80 (5.90, 9.40)	7.60 (5.35, 9.75)	0.95
NIRS
maxLCBIlesion	158.2 (121.3, 256.6)	85.1 (60.1, 121.8)	<0.01
maxLCBI4mm	519.7 (398.1, 721.0)	370.3 (251.9, 472.9)	<0.01
maxLCBI10mm	379.9 (261.8, 560.1)	244.0 (123.3, 339.6)	0.01

Values are mean ± SD, median (interquartile range) or n (%); TCFA = thin-cap fibroatheroma; OCT = optical coherence tomography; CSA = cross-sectional area; IVUS = intravascular ultrasound; EEM = external elastic membrane; TAV = total atheroma volume; PAV = percent atheroma volume; NIRS = near-infrared spectroscopy; maxLCBIlesion, maxLCBI4mm, maxLCBI10mm = maximum lipid core burden index for the whole lesion and within 4-mm and 10-mm segments, respectively.

Wall shear stress calculation and thin-cap fibroatheroma

Angiography and CFD findings are summarized in Table 3. Mean WSSproximal was significantly higher in lesions with TCFA (10.20 [IQR: 5.01 to 16.93Pa]) compared to lesions without TCFA (5.84 [IQR: 3.74 to 8.29Pa], p = 0.02). Similarly, mean WSS in the total lesion (WSSlesion) was higher in TCFA group (12.37 [IQR: 6.36 to 14.55Pa]) versus non-TCFA group (6.95 [IQR: 4.41 to 11.60Pa], p = 0.04). Percent diameter stenosis and lesion length were comparable between the groups. 3D contrast velocity in TCFA group (244±36mm/s) was higher than that in non-TCFA group (211±50mm/s, p = 0.03).

Table 3

Angiographically derived measurements and computational fluid dynamics findings.

	TCFA (n = 13)	No TCFA (n = 57)	P-value
%DS	52.2 ± 9.6	50.0 ± 8.2	0.40
Lesion length, mm	24.1 (13.0, 37.7)	19.9 (14.1, 25.2)	0.22
3D contrast velocity, m/s	244 ± 36	211 ± 50	0.03
Total lesion mean WSS, Pa	12.37 (6.36, 14.55)	6.95 (4.41, 11.60)	0.04
Upstream mean WSS, Pa	3.42 (2.98, 5.49)	2.69 (2.15, 4.44)	0.25
Proximal mean WSS, Pa	10.20 (5.01, 16.93)	5.84 (3.74, 8.29)	0.02
Middle mean WSS, Pa	11.60 (4.82, 19.37)	10.38 (5.20, 16.29)	0.82
Distal mean WSS, Pa	5.67 (3.45, 6.40)	4.15 (2.54, 7.69)	0.61
Downstream mean WSS, Pa	3.85 (2.96, 8.41)	4.30 (3.14, 7.11)	1.00

Values are mean ± SD or median (interquartile range); TCFA = thin-cap fibroatheroma; DS = diameter stenosis; WSS = wall shear stress.

In univariate logistic regression analysis, mean WSSproximal, lesion WSS and 3D contrast velocity were associated with the presence of TCFA (Table 4). After adjustments for the velocity, mean WSSproximal remained an independent predictor of OCT-defined TCFA (odds ratio: 1.105; 95%CI: 1.007–1.213, p = 0.04). The optimal cutoff value of mean WSSproximal to predict TCFA was 6.79Pa (AUC: 0.71; sensitivity: 0.77; specificity: 0.63, p = 0.02) (S2 Fig). Fig 2A demonstrates a representative case with the computed mean WSSproximal of 16.50Pa. OCT-verified TCFA was detected in the middle segment. Fig 2B shows CFD analysis of a case without TCFA. Mean WSS in the proximal segment was 3.81Pa and fibroatheroma with FCT of 170μm was detected in the middle of the lesion. In contrast, the maximal WSS was not an independent predictor of TCFA lesion (S1 and S2 Tables).

Table 4

Relationship between TCFA and hemodynamics.

	Odds ratio	95%CI	P value
Total lesion mean WSS, Pa	1.135	1.007–1.280	0.04
Upstream mean WSS, Pa	1.013	0.800–1.282	0.92
Proximal mean WSS, Pa	1.125	1.020–1.240	0.02
Middle mean WSS, Pa	1.021	0.944–1.123	0.61
Distal mean WSS, Pa	0.997	0.906–1.096	0.95
Downstream mean WSS, Pa	0.998	0.790–1.261	0.99
3D contrast velocity, mm/s	1.014	1.001–1.028	0.04
Total lesion mean WSS, Pa (adjusted for 3D contrast velocity)	1.090	0.950–1.251	0.22
Proximal mean WSS, Pa (adjusted for 3D contrast velocity)	1.105	1.007–1.213	0.04

TCFA = thin-cap fibroatheroma; CI = confidence Interval, WSS = wall shear stress.

Fig 2

High mean wall shear stress in the proximal segment of coronary lesions is associated with the presence thin-cap fibroatheroma verified by optical coherence tomography.

Color-coded WSS maps for two representative cases from patients with (A) thin-cap fibroatheroma lesion identified by OCT with minimal fibrous cap thickness 60 μm (A, inset) and WSS measured in the proximal segment of the lesion 16.5 Pa and (B) lesion with thick fibrous cap of 170 μm (B, inset) and proximal WSS 3.81 Pa. FCT = fibrous cap thickness; TCFA = thin-cap fibroatheroma; WSS = wall shear stress.

Discussion

The study evaluated the relationship between angiography-based WSS and the presence of OCT-verified TCFA in angiographically obstructive lesions in patients with stable CAD. The main findings of the study are: 1) Mean WSS in the proximal segment of the lesion was an independent predictor of TCFA; 2) the optimal cutoff value of WSSproximal to predict TCFA was 6.79Pa (AUC: 0.71; sensitivity: 0.77; specificity: 0.63, p = 0.02). In addition, TCFA lesions with high WSS had higher lipid and macrophage content assessed by OCT and NIRS. To our knowledge, the study is the first report showing association between angiography-based WSS and the presence of OCT-confirmed TCFA in obstructive coronary lesions.

During the development of atherosclerosis, the plaques expand outward to preserve luminal patency. When the plaques grow and the further outward expansion is limited, plaque progression necessarily encroaches on the lumen and causes luminal narrowing [16, 17]. In the advanced stages of atherosclerosis, both low [18-20] and high [5–9, 21–26] wall shear stress have been associated with plaque vulnerability and clinical events. Although some MI may be caused by rupture of plaques with a mild degree of lumen narrowing, in the majority of cases there is a rapid progression of high-risk plaques with lumen narrowing before MI [17]. As plaques develop with lumen narrowing over time, high WSS occurs at the stenotic lesion. Experimental studies have shown that high WSS modifies gene expression and activates matrix metalloproteinases, which lead to increased inflammation, collagen and elastin degradation, vascular smooth muscle cell apoptosis and progression of the atheromatous lipid core [3, 27, 28]. In line with the experimental studies, IVUS study has demonstrated that high WSS was associated with development of necrotic core and regression of fibrous and fibro fatty tissue, suggesting transformation to more vulnerable plaques [5]. In addition, localized elevation of shear stress has been shown to be a trigger of fibrous cap rupture [6]. Consistent with our findings, high WSS was strongly associated with TCFA and lipid-rich plaques in an OCT based CFD analysis of intermediate stenoses [24]. Lipid-rich plaques detected by NIRS have been shown to co-localize with high WSS in an IVUS based CFD study in non-culprit lesions of patients presenting with ACs [23]. In a recent 3D QCA based CFD analysis, a combination of high WSS and high-risk plaque morphology provided an accurate identification of non-obstructive lesions responsible for the future events. Based on the recent findings, high WSS has been recently proposed as a possible causative factor promoting the development of high risk plaques responsible for the majority of acute coronary events. The association between high WSS and the presence of OCT-defined TCFA lesions with greater lipid arc and higher macrophage content observed in the study further supports the hypothesis for WSS as an independent mechanism underlying development of vulnerable plaques.

A post hoc analysis of FAME II trial had recently demonstrated that angiography-based high WSS in the proximal segments of obstructive lesions was a predictor of MI in patients with stable CAD. The intravascular imaging was not performed in the study [9]. TCFA is pathologically characterized by a necrotic core covered by a thin fibrous cap with numerous macrophages [29] and high-resolution OCT imaging can detect TCFA in vivo [30]. The present study demonstrated that the high WSS in the proximal segment was predictive of the presence of OCT-verified TCFA with additional vulnerable plaque features including a greater lipid arc, a longer lipid length and a higher LCBI by NIRS compared to the lesions without TCFA. These multimodality imaging features of plaque vulnerability have been previously identified as predictors of adverse cardiovascular events [31, 32]. In addition, the TCFA lesions had higher content of macrophages, which contribute to plaque destabilization and rupture [33]. Taken together the findings of this and FAME II trial sub analysis provide an explanation for the association between high proximal WSS and myocardial infarction (Fig 2) [9].

In conclusion, angiography-based high WSS in the proximal segments of angiographically obstructive lesions was an independent predictor of OCT-verified TCFA. Intracoronary imaging including OCT and NIRS/IVUS are presently necessary to evaluate the plaque vulnerability, however, an additional invasive procedure is required to obtain the intracoronary images. The study demonstrates a possibility to detect high-risk plaques using angiography only combined with WSS assessment which can potentially be used in clinical practice.

There were several limitations in the study. First, the number of study population was small. Second, OCT and IVUS was not used to perform CFD and combined models might be more accurate, however what is novel in the study is that local WSS computed based on angiography which is usually performed in daily clinical practice was predictive for high-risk plaques in obstructive lesions in patients with CAD.

Representative multimodality images of a study lesion.

OCT detected TCFA with minimal fibrous cap thickness 50 μm (inset) and a 305° lipid arc (A); large attenuated plaque detected by IVUS with maximal lipid core burden index (LCBI) within a 4 mm segment 465 detected by NIRS.

(DOCX)

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Receiver-operating characteristic curve to predict thin-cap fibroatheroma lesions using angiography-based CFD analysis of wall shear stress in obstructive coronary lesions.

(DOCX)

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Angiographically derived measurements and computational fluid dynamics: Maximal WSS.

(DOCX)

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Relationship between TCFA and maximal WSS.

(DOCX)

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1 Oct 2020

PONE-D-20-22797

Relationship between high shear stress and OCT-verified thin-cap fibroatheroma in patients with coronary artery disease

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Reviewer #1: This manuscript addresses an important question, the relationship between blood shear stress and thin cap fibroatheroma. The results are suggestive but the results are presented in a cursory manner with important details. Additional information is needed about the following:

1. The authors need to verify that they have performed the appropriate analysis to optimize the meshes for the numerical simulation and provide details on the mesh sizes and number of elements used in these validations. Indicate spatial resolution of shear stress.

2. Show representative inlet flow waveforms and explain how they were obtained.

3. Provide details on wall shear stress calculation over the plaque and how averaging over the cardiac cycle was performed. The lengths of lesion used to calculate proximal, middle and distal WSS need to be defined.

4. Since different methods were used to measure the dimensions of the fiberoatheromatous plaque and reconstruct the vessel geometry, describe the agreement in plaque dimensions using angiography and OCT and NIRS/IVUS.

5. Clarify whether the odds ratio means the likelihood of finding a fiberoatheromatous lesion based on a given shear stress or if a lesion is located in a region with a given proximal wall shear stress the odds of it being fiberoatheromatous.

A minor point to correct is the following.

Figure 2. Panels A, B, and C are not described.

Reviewer #2: The study aims to test the hypothesis that invasive coronary angiography-based high WSS is associated with the presence of TCFA detected by OCT in obstructive lesions. This manuscript selected patients who had two angiographic projections to create a 3D reconstruction model to assess WSS. The results demonstrated that high WSS in the proximal segments of obstructive lesions is an independent predictor of OCT-verified TCFA.

The paper is well written. Research was carefully designed and performed. This is a very important study and should be published with some minor changes.

1. Introduction.

The current manuscript is short of past publications on high WSS research. Some published studies on high WSS should be included and identify the research gaps that current research is trying to address.

2. Method.

For the convenience of understanding and reading, the definition of some plaque morphology parameters (such as LCBI etc.) should be expressed by some figures or formulas.

3. Result.

The study aims to test the hypothesis that invasive coronary angiography-based high WSS is associated with the presence of TCFA detected by OCT in obstructive lesions. Use of mean WSS is fine, but maximum WSS could also be considered which could be a more sensitive measure? There is a chance that mean WSS could get some important findings “averaged out”. The author needs to consider the maximum WSS to explain whether the mean WSS in the manuscript is a better measure to test the hypothesis?

4. Table3. Please give the explanation of DS (diameter stenosis).

5. “The optimal cutoff value of WSSproximal to predict TCFA was 6.79Pa (AUC: 0.710; sensitivity: 76.9%; specificity: 63.2%, p=0.02).” A figure with the ROC curve is needed to show where the values come from.

**********

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Reviewer #1: Yes: George A. Truskey

Reviewer #2: No

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5 Nov 2020

Response to Reviewers

Additional Editor Comments:

Although the topic is interesting, as you will gather from the reviews, the referees identified substantive methodological problems, statistical analysis, and data presentation as well as the recruitment of all subjects.　 The editorial broad member also concurs. You may resubmit a revised version but it will be re-reviewed and there exists no guarantee that even with revision it will necessarily be accepted.

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Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1

This manuscript addresses an important question, the relationship between blood shear stress and thin cap fibroatheroma. The results are suggestive but the results are presented in a cursory manner with important details. Additional information is needed about the following:

1. The authors need to verify that they have performed the appropriate analysis to optimize the meshes for the numerical simulation and provide details on the mesh sizes and number of elements used in these validations. Indicate spatial resolution of shear stress.

Response:

We greatly appreciate the reviewer’s comments and add additional information on mesh and solution convergence analyses, boundary conditions and WSS averaging to the revised manuscript as described below (pages 2-4).

The appropriate elements size for the models was determined as a results of a mesh refinement study to ensure that WSS values are size independent. The discrepancy in the mean WSS values for each segment of 3% of less was acceptable. The average length of the study vessels was 22 mm and the largest (proximal) diameter was 3.1 mm. As a result of the mesh convergence analyses, we used the following settings for mesh generating: max face size 0.2 mm, min size 0.02 mm. The average number of elements in the study was 287,265 ± 114,712 (surface area 0.021±0.010 mm2 and min edge length 0.044±0.026 mm). Each simulation was monitored for convergence using absolute criteria with 10-6 as a threshold for the residual error. Several models required local refinement of the mesh at the bifurcation segment in order to achieve convergent solution. We added the following details to the revised manuscript:

Methods, page 6: The optimal mesh size was determined based on a mesh refinement study to ensure that the results are not affected by the element size. The average number of finite elements in the study was 287,265, and the mean surface area was 0.021 mm2.

2. Show representative inlet flow waveforms and explain how they were obtained.

Response: We calculated velocity of the contrast in angiography images for each vessel using a frame count method with the first frame showing the front of contrast bolus entering analyzed segment and the last frame in which contrast reaches the end of the segment. The velocity was calculated as follows:

[segment length assessed by angiography, mm]*[acquisition speed, frames/sec]/[last frame number – first frame number, frames] as described in the Fame II post-hoc analysis by Kumar and co-authors (Ref #9). Similar to the study, the patient-specific calculated velocity was applied as an inlet boundary condition with a flat profile. We added the following details to the revised manuscript:

Materials and Methods (Page 6, new text is underlined): Patient-specific inlet velocities were calculated using flame counts ([segment length assessed by angiography]*[acquisition speed]/[last frame number – first frame number, frames]) and applied as inlet boundary conditions with a flat profile as previously described (9).

3. Provide details on wall shear stress calculation over the plaque and how averaging over the cardiac cycle was performed. The lengths of lesion used to calculate proximal, middle and distal WSS need to be defined.

Response:

CFD simulations with the pulsatile flow were not performed in the study, blood flow was assumed to be steady. Each angiography-defined lesion was divided into 3 equal parts: proximal, middle and distal in the same manner as in the FAME II sub study (ref #9), since we wanted to test the hypothesis that the high WSS is associated with vulnerable plaques by OCT to provide an explanation for the association of high WSS in the proximal segment with future MIs reported in the study. In the original version of the manuscript, we calculated mean WSS in the whole lesion and each sub segment. For the revised paper, we performed additional analyses for the maximal WSS per suggestion of the Reviewer #2, but the mean WSS remained the most sensitive predictor of the presence of TCFA lesion. Considering that the average lesion length in our study was 22 mm (Table 3), each lesion sub segment was around 7.3 mm depending on the case. We’ve added the following clarification to the revised manuscript (page 5, new text is underlined):

Materials and Methods (Page 6, new text is underlined): No-slip boundary condition was applied at the vessel wall and blood flow was assumed to be steady Each angiography-defined lesion was divided into 3 equal parts: proximal, middle and distal following the segment definitions introduced in the WSS FAME II sub-study (9).

4. Since different methods were used to measure the dimensions of the fiberoatheromatous plaque and reconstruct the vessel geometry, describe the agreement in plaque dimensions using angiography and OCT and NIRS/IVUS.

Response: The vessel geometries were reconstructed based on angiography images using two end-diastolic projections more than 25 degrees apart using validated software QAngio XA 3D. Intravascular imaging (OCT and NIRS/IVUS) was used only to characterize plaque morphology for each lesion. Anatomical landmarks (side branches) visible in both angiography and OCT images were used to identify the location of TCFA lesions within the angiography-based models.

5. Clarify whether the odds ratio means the likelihood of finding a fiberoatheromatous lesion based on a given shear stress or if a lesion is located in a region with a given proximal wall shear stress the odds of it being fiberoatheromatous.

Response: We apologize for the confusion. Our data showed the association of high WSS with the presence of OCT-defined TCFA rather than the co-localization of the two measures. While we did mention this in the abstract, it was not clearly described throughout the text. We corrected the wording in several places including pages 3, 11, 13, 15 as follows : ”The study evaluated the relationship between angiography-based WSS and the presence of OCT-verified TCFA in angiographically obstructive lesions in patients with stable CAD”

A minor point to correct is the following.

Figure 2. Panels A, B, and C are not described.

Response: We added Figure 2 panels’ description to the revised manuscript and re-arranged Figures 1 and 2 to better describe the study methods and results.

Reviewer #2

The study aims to test the hypothesis that invasive coronary angiography-based high WSS is associated with the presence of TCFA detected by OCT in obstructive lesions. This manuscript selected patients who had two angiographic projections to create a 3D reconstruction model to assess WSS. The results demonstrated that high WSS in the proximal segments of obstructive lesions is an independent predictor of OCT-verified TCFA.

The paper is well written. Research was carefully designed and performed. This is a very important study and should be published with some minor changes.

1. Introduction.

The current manuscript is short of past publications on high WSS research. Some published studies on high WSS should be included and identify the research gaps that current research is trying to address.

Response: We greatly appreciate the reviewer’s comments and add some of the most recent reports on the association of high WSS with lesion vulnerability (please see below). While majority of the studies used intravascular imaging or CTA to create 3D CFD models (1-5), 3D-QCA-derived high WSS in combination with lesion morphology was able to identify plaques prone to future MACE in the last month report from Bourantas and co-authors (6).

1. Hartman EMJ, De Nisco G, Kok AM et al. Lipid-rich Plaques Detected by Near-infrared Spectroscopy Are More Frequently Exposed to High Shear Stress. Journal of cardiovascular translational research. 2020. Epub 2020/10/10.

2. Toba T, Otake H, Choi G et al. Wall Shear Stress and Plaque Vulnerability: Computational Fluid Dynamics Analysis Derived from cCTA and OCT. JACC Cardiovascular imaging. 2020. Epub 2020/09/21.

3. Lee JM, Choi G, Koo BK et al. Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics. JACC Cardiovascular imaging. 2019;12(6):1032-43. Epub 2018/03/20.

4. Yamamoto E, Thondapu V, Poon E et al. Endothelial Shear Stress and Plaque Erosion: A Computational Fluid Dynamics and Optical Coherence Tomography Study. JACC Cardiovascular imaging. 2019;12(2):374-5. Epub 2018/10/22.

5. Murata N, Hiro T, Takayama T et al. High shear stress on the coronary arterial wall is related to computed tomography-derived high-risk plaque: a three-dimensional computed tomography and color-coded tissue-characterizing intravascular ultrasonography study. Heart and vessels. 2019;34(9):1429-39. Epub 2019/04/13.

6. Bourantas CV, Zanchin T, Torii R et al. Shear Stress Estimated by Quantitative Coronary Angiography Predicts Plaques Prone to Progress and Cause Events. JACC Cardiovascular imaging. 2020;13(10):2206-19. Epub 2020/05/18.

In addition, the following summary was included in the revised Discussion (page 14): “Consistent with our findings, high WSS was strongly associated with TCFA and lipid-rich plaques in an OCT based CFD analysis of intermediate stenoses [24]. Lipid-rich plaques detected by NIRS have been shown to co-localize with high WSS in an IVUS based CFD study in non-culprit lesions of patients presenting with ACs [23]. In a recent 3D QCA based CFD analysis, a combination of high WSS and high-risk plaque morphology provided an accurate identification of non-obstructive lesions responsible for the future events.”

2. Method. For the convenience of understanding and reading, the definition of some plaque morphology parameters (such as LCBI etc.) should be expressed by some figures or formulas.

Response: We appreciate the suggestion and provide definitions for the imaging characteristics used in the study in the Methods and a new Supporting Figure 1 (S1 Fig) showing representative images of a lesion acquired with different image modalities.

Materials and Methods. Page 4, 5 (new text is underlined): “Lipid core was identified as a signal-poor region with poorly delineated borders, little or no signal backscattering, and an overlying signal-rich layer, the fibrous cap (S1 Fig) … Raw spectroscopic information from NIRS chemograms was transformed into a probability of lipid that was mapped to a red-to-yellow colour scale, with a low probability of lipid shown as red and a high probability of lipid shown as yellow (S1 Fig). Lipid core burden index (LCBI) was calculated by dividing the number of yellow pixels (probability ≥0.6) by the total number of viable pixels within the plaque multiplied by 1000.”

S1 Figure. Representative multimodality images of a study lesion: OCT detected TCFA with minimal fibrous cap thickness 50 µm (inset) and a 305° lipid arc (A); large attenuated plaque detected by IVUS with maximal lipid core burden index (LCBI) within a 4 mm segment 465 detected by NIRS (yellow area).

3. Result.

The study aims to test the hypothesis that invasive coronary angiography-based high WSS is associated with the presence of TCFA detected by OCT in obstructive lesions. Use of mean WSS is fine, but maximum WSS could also be considered which could be a more sensitive measure? There is a chance that mean WSS could get some important findings “averaged out”. The author needs to consider the maximum WSS to explain whether the mean WSS in the manuscript is a better measure to test the hypothesis?

Response: Thank you for the interesting suggestion. We performed additional analyses to test whether the maximal WSS would be a more sensitive predictor of the presence of TCFA compared to the mean WSS. The results are shown in new S1 and S2 Tables and below. While proximal WSS (max) was significantly higher in TCFA lesions compared to non TCFA lesions (S1 Table), it was not an independent predictor of TCFA by univariate logistic regression analysis (S2 Table) with or without adjustment for 3D contrast velocity.

Supporting Table 1. Angiographically derived measurements and computational fluid dynamics: maximal WSS

TCFA (n=13) No TCFA (n=57) P-value

Total lesion WSS (max), Pa 65.8 (31.3, 98.7) 44.2 (23.7, 67.6) 0.04

Upstream WSS (max), Pa 8.5 (6.2, 21.5) 8.7 (4.4, 21.7) 0.63

Proximal WSS (max), Pa 34.6 (14.7, 54.5) 13.6 (8.6, 29.6) 0.04

Middle WSS (max), Pa 60.5 (25.1, 91.3) 34.9 (18.9, 58.8) 0.12

Distal WSS (max), Pa 24.6 (13.3, 48.9) 16.4 (8.8, 34.0) 0.25

Downstream WSS (max), Pa 14.7 (6.0, 42.7) 13.8 (7.0, 30.1) 0.88

Supporting Table 2. Relationship between TCFA and maximal WSS

Odds ratio 95%CI P value

Total lesion WSS (max), Pa 1.017 1.001-1.034 0.04

Upstream WSS (max), Pa 0.996 0.958-1.035 0.84

Proximal WSS (max), Pa 1.014 0.993-1.036 0.20

Middle WSS (max), Pa 1.013 0.995-1.032 0.16

Distal WSS (max), Pa 1.010 0.990-1.031 0.32

Downstream WSS (max), Pa 0.903 0.968-1.038 0.90

Total lesion WSS (max), Pa (adjusted for 3D contrast velocity) 1.012 0.988-1.035 0.33

Proximal WSS (max), Pa (adjusted for 3D contrast velocity) 1.015 0.992-1.037 0.83

4. Table3. Please give the explanation of DS (diameter stenosis).

Response: Percent diameter stenosis or diameter stenosis is defined as the difference between the reference vessel diameter and minimal luminal diameter, divided by the reference and multiplied by 100. All the measurements were performed automatically by QAngio XA 3D RE software in the study. We added the following clarification to the revised manuscript:

Page 5 (new text is underlined): “Three-dimensional quantitative coronary angiography (3D-QCA) and vessel reconstructions were performed using two end-diastolic projections at least 25° apart with commercially available software QAngio XA 3D RE (Medis, Leiden, The Netherlands) followed by automatic quantification of 3D lesion length, minimal and reference diameters, and percent diameter stenosis (DS) (Figure 1A) (14)”.

5. “The optimal cutoff value of WSSproximal to predict TCFA was 6.79Pa (AUC: 0.710; sensitivity: 76.9%; specificity: 63.2%, p=0.02).” A figure with the ROC curve is needed to show where the values come from.

Response: We apologize for the oversight, the ROC figure below was added to the revised manuscript

________________________________________

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Reviewer #1: Yes: George A. Truskey

Reviewer #2: No

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Submitted filename: Response to Reviewers.docx

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23 Nov 2020

PONE-D-20-22797R1

Relationship between high shear stress and OCT-verified thin-cap fibroatheroma in patients with coronary artery disease

PLOS ONE

Dear Dr Kini

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Reviewers' comments:

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The issues raised in the review of the original manuscript were addressed. There are a few minor details to address.

Specific Comments

p. 6, line 7, “flame counts”. Should be “frame counts”

p. 6, lines 4-15 “Mean and maximal WSS was calculated …” should be “Mean and

maximal WSS were calculated …”.

Table 2. Explain why the lipid arc maximum is presented rather than the average lipid arc.

Reviewer #2: (No Response)

**********

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Reviewer #1: Yes: George A Truskey

Reviewer #2: No

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23 Nov 2020

Reviewer #1: The issues raised in the review of the original manuscript were addressed. There are a few minor details to address.

Specific Comments

p. 6, line 7, “flame counts”. Should be “frame counts”

p. 6, lines 4-15 “Mean and maximal WSS was calculated …” should be “Mean and

maximal WSS were calculated …”.

Response: We corrected both typos in the revised manuscript, thank you.

Table 2. Explain why the lipid arc maximum is presented rather than the average lipid arc.

Response: We apologize for the oversight; the average lipid arc has been added to the Table 2 of the revised manuscript

Reviewer #2: (No Response)

Submitted filename: Response to Reviewers R2.docx

Click here for additional data file.

2 Dec 2020

Relationship between high shear stress and OCT-verified thin-cap fibroatheroma in patients with coronary artery disease

PONE-D-20-22797R2

Dear Dr. Kini

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Xianwu Cheng, M.D., Ph.D., FAHA

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

None.

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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Reviewer #2: (No Response)

**********

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Reviewer #1: (No Response)

Reviewer #2: (No Response)

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Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

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Reviewer #1: Yes: George A. Truskey

Reviewer #2: No

9 Dec 2020

PONE-D-20-22797R2

Relationship between high shear stress and OCT-verified thin-cap fibroatheroma in patients with coronary artery disease

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