Comprehensive risk prediction of acute coronary syndrome by integrating coronary plaque morphology and hemodynamic characteristics

Introduction

Acute coronary syndrome (ACS), one of the most severe clinical manifestations of coronary artery disease (CAD), is primarily triggered by the rupture of vulnerable plaques. This well-established pathophysiological mechanism accounts for more than two-thirds of ACS events (1).

Coronary computed tomography angiography (CCTA) is a critical non-invasive modality for the evaluation of coronary atherosclerotic plaques (2,3). Quantitative plaque parameters derived from CCTA have been shown to significantly improve CAD risk stratification (2-4). Moreover, high-risk plaque (HRP) features detected by CCTA are strongly correlated with histopathological findings and enhance the prediction of future ACS events (5). Plaque rupture is a complex biomechanical process influenced by plaque composition, structural integrity, and external hemodynamic forces. Recent advances in CCTA and computational fluid dynamics (CFD) have enabled the integrated assessment of lesion severity, HRP characteristics, and patient-specific hemodynamic forces (6,7).

Local hemodynamic factors, particularly wall shear stress (WSS), play crucial regulatory roles in atherosclerosis progression: normal WSS exerts a protective effect on atherosclerosis, while abnormal WSS promotes endothelial dysfunction and plaque progression (8-10). Early hemodynamic research has produced conflicting evidence regarding whether high or low WSS predominantly drives plaque progression. Low WSS has been shown to lead to the activation of endothelial pro-inflammatory, pro-coagulant, and pro-apoptotic factors, resulting in the degradation of the elastic membrane, destruction of the intima, and formation of thin-cap fibroatheroma. These factors collectively contribute to the progression of atherosclerotic plaques (8,11). Conversely, other clinical studies have reported that high WSS is associated with plaque progression via matrix metalloproteinase activation, which contributes to unstable plaque phenotypes (10,12). These discrepancies may arise from spatial heterogeneity in WSS across the vascular wall, which is influenced by arterial geometry and transient flow dynamics.

Previous studies have not comprehensively examined the relationship between the spatial distribution of WSS and the clinical outcomes of CAD, particularly in the context of ACS. This study aimed to assess the association among CCTA-derived plaque characteristics, CFD-derived WSS, and ACS, and to further elucidate the role of spatial WSS distribution in enhancing the predictive performance of models for ACS. We present this article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2702/rc).

Methods

Study population and inclusion criteria

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Academic Ethics Committee of Shaoxing People’s Hospital (No. 2023-012-01), and the requirement of individual consent for this retrospective analysis was waived.

A retrospective analysis was conducted of patients with stable CAD who underwent CCTA at Shaoxing People’s Hospital between January 1, 2022, and January 31, 2024 and subsequently developed ACS, including acute myocardial infarction or unstable angina with plaque rupture. Based on the diagnostic criteria, 85 patients with ACS were enrolled in the study.

The inclusion criteria were as follows: (I) CCTA performed 1 month to 1 year prior to ACS onset; (II) CCTA demonstrating coronary stenosis >30% and a plaque diameter >2 mm; (III) availability of high-quality digital imaging and communications in medicine (DICOM) data suitable for vascular reconstruction and CFD simulation; and (IV) complete clinical records.

The exclusion criteria were as follows: (I) a CCTA to ACS interval exceeding 1 year; (II) prior coronary artery bypass grafting or in-stent restenosis-related ACS; (III) myocardial infarction secondary to non-atherosclerotic cause; (IV) a single-plaque Agatston calcium score ≥100 (to minimize calcification-related artifacts); and/or (V) poor CCTA image quality precluding analysis.

CCTA scanning

Imaging was performed using a Canon Aquilion ONE (Toshiba Medical Systems, Tokyo, Japan), equipped with 320 detector rows, each 0.5 mm in width. The scanning range extended from the tracheal prominence to the diaphragmatic level of the heart. The computed tomography (CT) parameters used for scanning were as follows: retrospective electrocardiogram-gated axial scanning; gantry speed, 0.625 s/rotation; tube voltage, 120 kV; and tube current, 280 mA. Sublingual nitroglycerin (1.0 mg) was administered 1–2 minutes before scanning. All patients underwent breath-hold training and were monitored by electrocardiography during the examination. β-blockers were administered before CCTA in patients with heart rates >75 beats/min. A total of 60–70 mL of intravenous iodinated contrast medium (iohexol, 370 mg/mL; Omnipaque, GE Healthcare, Shanghai) was injected through an indwelling needle placed in the median cubital vein at a rate of 5 mL/s, followed by 30 mL of saline. Image acquisition was automatically triggered by an attenuation of 100 Hounsfield units (HU) in the aortic arch.

CCTA image analysis

The images were analyzed using Vitrea 4.0 (Canon Medical Informatics, Minnetonka, MN, USA), a semi-automated postprocessing software platform. Axial images, cross-sectional views, curved planar reformations (CPRs), multi-planar reformations, volume-rendered images, and maximum intensity projection (MIP) images were used for plaque analysis.

The radiologist identified the inner and outer walls of the blood vessels, and the plaque margins by adjusting the window and level settings, and determined the plaque location and type using axial, MIP, and CPR images. If the artery wall was not visible or its measured diameter was <1.0 mm, the artery was classified as plaque-free. Each artery was assessed from 2 cm proximal to 2 cm distal to the bifurcation to determine the degree of stenosis, using CAD-Reporting and Data System (CAD-RADS^TM) 2.0 (2022 CAD-RADS criteria) (13). Total lesion length was measured on CPR images and was defined as the length from the proximal to the distal shoulder of the lesion, where no plaque could be detected. Combining the source axial and sagittal images, the maximum plaque thickness was approximated perpendicular to the long axis of the vessel in the narrowest portion of the coronary artery. The site of maximal stenosis was identified using a digital calliper.

HRP was defined as the presence of at least two of the following features (5): (I) positive remodeling (remodeling index >1.1, calculated as the ratio of the vessel diameter at the plaque site to the mean reference diameters proximal and distal to the lesion); (II) low-attenuation plaque (LAP) (CT attenuation <30 HU); (III) spotty calcification (calcified nodule diameter <3 mm); and (IV) the napkin-ring sign (characterized by a central low-attenuation necrotic core surrounded by ring-like fibrous tissue with higher attenuation and peripheral contrast enhancement).

Vascular hemodynamic model and parameters

The hemodynamic simulation software DetecFluid version 1.5 (Boea Wisdom, Hangzhou, China) was used to perform patient-specific CFD simulations. The thin-slice axial CCTA images were imported into a dedicated computer in standard STL (STereoLithography) format for three-dimensional (3D) reconstruction and hemodynamic parameter analysis. The calculation method was as follows:

• Model reconstruction and meshing: patient-specific 3D cardiovascular models were automatically segmented from the CCTA images using a convolutional neural network-based algorithm. The reconstructed geometry extended from the aortic root to the distal coronary artery segments with a minimum lumen diameter of 1.5 mm. To ensure simulation accuracy and accurately capture near-wall hemodynamics, tetrahedral meshes were generated. Three meshes with increasing element counts (coarse: ~0.6 million; medium: ~1.16 million; fine: ~2.3 million) were generated and compared. The resulting finite-volume mesh had an average element count of approximately 1.16 million, with a core region element size of 0.2 mm³. The difference in mean WSS between the medium and fine meshes was <5%, confirming that the current mesh setting achieved adequate solution independence.
• Generation of the coronary artery centerline: using a surface mesh skeletonization algorithm for the coronary arteries, the centerline was generated, and the coronary artery outlets were automatically detected and cut.
• Boundary conditions: patient-specific boundary conditions were derived from individual clinical data. The inlet flow boundary condition at the coronary ostium was calculated based on the left ventricular volume and ejection fraction. The left ventricular volume was estimated from the volumetric measurements obtained from CCTA. It was assumed that 4% of the cardiac output goes to the coronaries with the rest going to the aorta (14). For outlet conditions, the aortic outlet was coupled with a three-element Windkessel (3EWK) model to simulate aortic compliance and downstream vascular resistance (15). The 3EWK model comprises proximal resistance R_p, representing the viscous resistance of the arterial vasculature immediately downstream of the model, capacitance C, representing the compliance of all downstream vessels, and distal resistance R_d, representing the resistance of the capillaries and venous circulation. These parameters were tuned to ensure that the simulated pressure matched the patient-specific physiological conditions. Coronary branch outlets were coupled with a lumped-parameter coronary circuit model to represent distal coronary resistance (14-17). A schematic diagram of the coronary lumped model (Figure 1) illustrates the model structure, and detailed parameter derivation steps are provided therein. The cardiac cycle length was individualized based on each patient’s heart rate recorded during CCTA acquisition. The inlet flow rate and the intramyocardial pressure values were extracted from the literature (15), while the flow rate and flow peak were scaled according to each patient’s stroke volume and heart rate derived from CCTA. Based on the existing literature, differences in WSS distribution between flat and parabolic inlet velocity profiles are extremely small. Accordingly, a flat (uniform) velocity profile perpendicular to the inlet boundary was applied at the coronary ostium (aortic root) (14-16).
• Plaque definition and hemodynamic parameter acquisition: blood was modelled as an incompressible Newtonian fluid with a density of 1,060 kg/m³ and a dynamic viscosity of 0.0040 Pa·s (15). The governing Navier-Stokes equations for incompressible flow were solved using a finite volume-based solver (DetecFluid) (16). Transient simulations were performed to accurately capture coronary hemodynamics, with a time step size of 0.01 seconds. For each patient, three cardiac cycles were simulated to ensure periodic stability. Convergence was defined as the point at which all normalized residuals of the momentum and continuity equations fell below 10⁻³, at which stage the simulation was terminated. All hemodynamic parameters were then extracted from the final converged time step, including the time-averaged WSS (TAWSS) over the cardiac cycle.

WSS was defined as the tangential stress resulting from the friction between blood flow and the endothelial surface of the vessel wall, and was computed as follows:

$$\tau w=\mu \frac{\partial u_{\parallel }}{\partial d_{\perp }}$$[1]

Figure 1 A schematic diagram of the coronary lumped model. A-k and m indicate the numbers of the vessel ports. C, capacitance; R_d, distal resistance; R_p, proximal resistance.

The TAWSS during the cardiac cycle was calculated as follows:

TAWSS=1T∫0T|μ∂u∥∂d⊥|dt[2]

where T represents the cardiac cycle; µ is the dynamic viscosity of the fluid; µ_|| denotes the component of blood flow velocity parallel to the wall; and d_⊥ represents the normal distance from the centroid of the boundary element to the wall.

Five segments of interest, along with the lesion itself, were identified for further analysis: the proximal, middle, and distal thirds of the lesion, and 5-mm segments proximal and distal to the lesion. In each segment, the mean WSS was calculated, and these values were used to examine the association between WSS and the patient outcomes.

Two cardiovascular radiologists, each with 5 years of experience in cardiac imaging and blinded to the clinical histories, CCTA plaque characteristics, and hemodynamic parameters, independently analyzed the data. Inter-rater reliability for qualitative assessments was evaluated using Cohen’s kappa coefficient (κ). Any discrepancies were resolved by consensus, and the mean values of the measurements were used for final analysis.

Statistical analysis

The statistical analyses were performed using SPSS (version 27.0, IBM Corp., Armonk, NY, USA) and R software (version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria). A two-tailed P<0.05 was considered statistically significant. Normality was assessed using the Shapiro-Wilk test. Continuous variables were presented as mean ± standard deviation, or median and interquartile range (IQR), and compared using Student’s t-test or the Mann-Whitney U test, as appropriate. Categorical variables were expressed as number (percentage) and compared using the Chi-squared (χ²) or Fisher’s exact test. Inter-rater reliability for qualitative assessments was determined using Cohen’s κ, with κ>0.80 indicating excellent agreement, 0.61–0.80 substantial agreement, 0.41–0.60 moderate agreement, and ≤0.40 fair to poor agreement. Missing data were handled using complete-case analysis; only patients with complete data for all variables in a given analysis were included. Variables with P<0.05 in the univariate analysis were entered into multivariate logistic regression with backward stepwise selection to identify independent predictors of culprit lesions. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). To evaluate the incremental value of WSS, four predictive models were constructed. Discriminative performance was assessed using the Harrell’s concordance index (C-index), and model comparisons were performed using DeLong’s method. Reclassification improvement was quantified using the net reclassification index (NRI) and integrated discrimination improvement (IDI).

Results

All WSS data reported in the following results represent TAWSS over the cardiac cycle, unless otherwise specified.

After stepwise screening, 85 patients with a total of 216 lesions were ultimately included in the analysis. These lesions were classified as culprit lesions (n=83) and non-culprit lesions (n=133) based on whether they had been treated clinically. The study screening process and specific reasons for exclusion are detailed in Figure 2.

Figure 2 Study flowchart. ACS, acute coronary syndrome; CABG, coronary artery bypass grafting; CCTA, coronary computed tomography angiography; CFD, computational fluid dynamics; CT, computed tomography; CTA, computed tomography angiography.

A total of 85 patients with ACS were included in the study based on the study criteria. The mean age of the patients was 64.4±1.0 years, and 70.6% of the patients had hypertension, while 47.1% reported a history of prior chest pain. Baseline clinical characteristics are presented in Table 1.

Comparison of the anatomical severity, HRP, and hemodynamic parameters of culprit and non-culprit lesions

The inter-rater agreement between the two radiologists was excellent. The Cohen’s κ values for the assessed parameters, including plaque presence, composition, and stenosis severity, ranged from 0.82 to 0.86, indicating a high level of consistency. Of the total 216 lesions analyzed, 122 (56.5%) were located in the left anterior descending (LAD) artery, 38 (17.6%) in the left circumflex artery, and 56 (25.9%) in the right coronary artery. Compared with the non-culprit lesions, the culprit lesions associated with subsequent ACS exhibited more severe stenosis (70.6%±8.5% vs. 66.4%±7.8%; P<0.001), and a longer lesion length (15.8±8.4 vs. 12.1±7.4 mm, P=0.002).

Regarding the HRP features, the culprit lesions were more frequently associated with HRP, positive remodeling, and LAPs compared with the non-culprit lesions (all P<0.05). No significant differences were observed in the napkin-ring sign or punctate calcification between the groups.

Regarding the hemodynamic parameters, compared with the non-culprit lesions, the culprit lesions exhibited higher total WSS [17.3 (IQR, 11.7–24.1) vs. 14.9 (IQR, 9.6–20.2) Pa, P=0.045]. Further analysis of WSS in different regions of the plaque revealed that the proximal WSS (WSS_prox) was significantly higher in the culprit lesions than in the non-culprit lesions [10.3 (IQR, 4.4–17.1) vs. 6.4 (IQR, 3.4–11.4) Pa, P<0.001]. No significant differences were observed in the upstream, distal, or downstream WSS segments. The anatomical severity, HRP features, and hemodynamic parameters of the culprit and non-culprit lesions are presented in Tables 2,3. Representative images are shown in Figures 3,4.

Figure 3 Imaging and hemodynamic assessment of a culprit lesion in a 65-year-old male patient with ACS. The patient presented with recurrent chest pain for more than three days. (A) CCTA demonstrated a non-calcified plaque with severe luminal stenosis in the mid-LAD artery (approximately 85%) (arrows). (B) Coronary arteriography revealed 90% luminal stenosis in the mid-LAD artery (arrow). (C) Abnormal WSS was observed in the mid-LAD artery segment (arrow). (D-H) WSS was quantified at five distinct regions relative to the lesion: proximal, mid-lesion, distal, upstream, and downstream segments. ACS, acute coronary syndrome; CCTA, coronary computed tomography angiography; LAD, left anterior descending; WSS, wall shear stress.

Figure 4 Imaging and hemodynamic assessment of a non-culprit lesion in a 59-year-old male with ACS. The patient reported recurrent chest tightness and shortness of breath for more than one week. (A) CCTA demonstrated a mixed plaque in the proximal segment of the LAD artery, with approximately 75% luminal stenosis in the mid-segment (arrows). (B) Coronary arteriography showed approximately 70% luminal stenosis in the proximal LAD artery (arrow). (C) Abnormal WSS was observed in the proximal segment of the LAD artery (arrow). (D-H) WSS was measured at five defined regions relative to the lesion: proximal, mid-lesion, distal, upstream, and downstream segments. ACS, acute coronary syndrome; CCTA, coronary computed tomography angiography; LAD, left anterior descending; WSS, wall shear stress.

Predictive models combining CCTA and hemodynamic parameters

Multivariate logistic regression identified stenosis severity, lesion length, HRP features, total WSS, and WSS_prox as independent predictors of ACS (Table 4). To evaluate the incremental discriminatory and reclassification value of the hemodynamic parameters in addition to anatomical severity and HRP features as predictors of ACS, five analytic models were constructed—Model 1: lesion length + stenosis; Model 2: Model 1 + HRP; Model 3: Model 2 + total WSS; Model 4: Model 2 + WSS_prox; Model 5: Model 3 + WSS_prox. Model performance was assessed using Harrell’s C-index, the unclassified NRI, and IDI.

Compared with Model 1, Model 2 showed improved discriminatory performance (C-index 0.733 vs. 0.719) and a better reclassification ability for identifying subsequent ACS culprit lesions (NRI: 0.305, P=0.01; relative IDI: 0.02, P=0.03). After adding the total WSS hemodynamic parameter to Model 2, Model 3 demonstrated improved discriminatory performance (C-index 0.744 vs. 0.733) and an incrementally better reclassification ability (NRI: 0.152, P<0.276; relative IDI: 0.016, P<0.063), but overall, there was no statistical difference between the two models (all P>0.05). Adding WSS_prox as a single indicator to Model 2 yielded the highest discriminatory performance among the models (C-index 0.767 vs. 0.733; P=0.048) and improved the reclassification ability (NRI: 0.369, P<0.007; relative IDI: 0.048, P<0.001). Additionally, adding WSS_prox to the model that already included WSS further significantly improved discrimination (C-index 0.789 vs. 0.733; P<0.025) and reclassification (NRI: 0.709, P<0.0001; relative IDI: 0.095, P<0.0001). A 1,000-iteration permutation test with 5-fold cross-validation showed that the progressive addition of adverse plaques characteristics and hemodynamic parameters to the prediction model improved both discrimination and reclassification performance (Table 5 and Figure 5).

Figure 5 Comparison of the discriminative and reclassification performance of the predictive models. Model 1: %DS and lesion length. Model 2: Model 1 + HRP. Model 3: Model 2 + total WSS. Model 4: Model 2 + WSS_prox. Model 5: Model 3 + WSS_prox. %DS, percent diameter stenosis; HRP, high-risk plaque; WSS, wall shear stress; WSS_prox, proximal wall shear stress.

Discussion

This study investigated the role of plaque morphological features identified by CCTA combined with non-invasive hemodynamic assessment in predicting the occurrence of ACS. Notably, in this study, all the coronary artery lesions identified on CCTA were detected in patients who subsequently developed ACS, enabling the detailed characterization of lesions associated with acute events. The results showed that the culprit lesions exhibited more severe stenosis, a longer lesion length, and a higher proportion of HRPs and LAP features than the non-culprit lesions. Hemodynamically, the culprit lesions demonstrated higher total WSS and WSS_prox. Notably, the incorporation of WSS_prox into the predictive model significantly improved both its discriminatory performance and reclassification ability.

Plaque morphological characteristics are typically considered important determinants of coronary heart disease risk (2,3). The morphological characteristics of plaques, such as large plaque size and plaque vulnerability features, constitute the morphological basis of plaque instability (3). In this study, by analyzing CCTA scans performed before the documented ACS event, we observed that culprit lesions exhibited more severe luminal stenosis, longer lesion lengths, and smaller minimum lumen areas than the non-culprit lesions. This aligns with the expectation that larger plaques result in reduced luminal patency and increased plaque burden, potentially impairing vascular endothelial function and exacerbating plaque vulnerability (2-5). Further, the analysis of CCTA-based plaque vulnerability features revealed a significantly higher proportion of HRPs and LAPs in culprit lesions. The LAPs identified on CCTA are thought to correspond histologically to lipid-rich necrotic cores, and may aid in the early identification of high-risk patients (18). Notably, adding plaque vulnerability features into a model based solely on anatomical stenosis severity significantly improved the identification of subsequent ACS culprit lesions. This underscores that the comprehensive assessment of both coronary anatomy and plaque composition is fundamental in risk stratification.

In clinical practice, it has often been observed that some lesions with only mild stenosis on angiography ultimately progress to ACS, while some lesions with severe stenosis remain stable over long periods (19). This phenomenon suggests that the occurrence of ACS depends not only on the static morphological characteristics of the plaque but also on the dynamic hemodynamic environment. Among these factors, changes in the coronary WSS are considered key pathophysiological mechanisms linking plaque vulnerability to acute events. Researchers have continuously explored new technical methods to achieve the non-invasive quantification of WSS (8-10,20). Park et al. (20) used CFD to reconstruct patient CCTA data, demonstrating the feasibility of CCTA-based CFD methods for non-invasive WSS assessment. This breakthrough enabled the translation of CFD-derived WSS parameters into clinical indicators reflecting vascular endothelial biological responses, thereby laying the foundation for subsequent research.

Using CTA-based CFD for non-invasive WSS measurements, we found that culprit lesions exhibited significantly higher total WSS. This aligns with the findings of a previous study of 411 patients with stable CAD, which identified a higher baseline WSS as an independent predictor of future myocardial infarction (21). Similarly, Hakim et al. (22) found that, among plaques with comparable lumen stenosis, those with erosion exhibited significantly higher WSS, WSS gradients, and plaque slope than stable plaques. These findings underscore the critical role of WSS in plaque erosion. Mechanistically, supraphysiological levels of high WSS may directly act on vascular endothelial cells, enhancing the phosphorylation of stress-related kinases, such as p38, c-Jun, and activating transcription factor 2 (ATF2), thereby weakening plaque stability and promoting a shift toward a vulnerable phenotype (9,10,23). This may explain why segments chronically exposed to a high-WSS environment tend to develop plaques with a higher-risk phenotype, and establishes high WSS as an independent risk predictor of plaque rupture and subsequent ACS. These findings also provide insights into why some non-obstructive lesions identified on invasive angiography still result in ACS in clinical practice.

In reality, WSS and plaques exhibit bidirectional, dynamic interactions throughout the entire process of atherosclerotic plaque formation, progression, and destabilization (6,8,24). This process is crucial to understand the mechanisms underlying ACS. In the early stages of atherosclerosis, WSS contributes to endothelial dysfunction and lipid deposition, laying the structural foundation for vulnerable plaque destabilization (21,24). As atherosclerosis progresses, this interaction exhibits bidirectional reinforcing characteristics. High WSS induces endothelial dysfunction and local inflammatory responses, stimulating the expansion of the atherosclerotic plaque’s lipid core and the enlargement of the necrotic core, which often manifests as increased LAPs on imaging (24). This may explain the higher frequency of LAPs observed in the culprit lesions in the present study.

Although human studies are relatively limited, the existing evidence strongly supports an association between high WSS and plaque lipid accumulation. Previous studies (23-25) have shown that increased lipid deposition in plaques during the follow-up period tends to occur in areas with high WSS. Conversely, plaque formation and progression alter local vascular geometry, leading to disturbed blood flow patterns. As the plaque enlarges and the luminal geometry changes, worsening local stenosis may further increase WSS. This elevated WSS exerts mechanical stress on the junction between the fibrous cap and the lipid core, inducing stress concentration. In addition, these geometric irregularities produce spatial heterogeneity in WSS, which plays a critical role in endothelial dysfunction and early immune cell recruitment during atherogenesis (26). This vicious cycle continuously deteriorates the local plaque microenvironment and may ultimately culminate in plaque rupture. In summary, high WSS is not merely a marker of plaque vulnerability but also an active driver of plaque progression and destabilization.

Studies indicate that the biological response of endothelial cells to WSS depends not only on the magnitude of shear stress, but also on its spatial distribution along the plaque (8,9,22,27). Based on this, we further analyzed WSS across different plaque segments (upstream, downstream, and proximal/mid/distal regions within the plaque) and found a particularly significant difference in WSS_prox between the culprit and non-culprit lesions; however, no significant difference was observed in the distal segments. Kumar et al. (21) confirmed that higher WSS in the proximal lesion segment independently predicted subsequent myocardial infarction in patients with stable CAD, which is consistent with our observations.

Histological analyses have shown that the proximal segments of coronary lesions often exhibit macrophage-driven inflammation and atherosclerotic progression, predisposing them to rupture (6,22,27). This may be because these proximal vessel locations, due to their complex geometry (including vessel curvature, tapering, and branching), are more prone to inducing disturbed blood flow, leading to elevated local WSS and creating a hemodynamic microenvironment conducive to plaque destabilization (20,22,23,26). Our findings may provide a plausible mechanistic explanation for the well-recognized clinical observation that ACS events occur disproportionately in the proximal segments of coronary arteries, especially at bifurcation sites, such as the LAD artery diagonal branch or right coronary artery marginal branch junctions (28).

Another key finding is that WSS_prox demonstrated superior predictive performance. Adding WSS_prox to the traditional model significantly increased the C-index from 0.733 to 0.767 (P=0.048) and yielded a significant improvement in reclassification (relative IDI: 0.048, P<0.001). This incremental value has potential implications for patient management. Currently, while treating patients with multiple plaques, clinicians often face difficult decisions regarding revascularization or intensified medical therapy for specific lesions. Our findings suggest that, among morphologically similar plaques, those exhibiting elevated WSS_prox may warrant closer monitoring or more aggressive medical therapy.

This study had several limitations. First, it was a single-center retrospective study with a relatively small sample size, which may introduce selection bias and limit the generalizability of the findings. In addition, the current study compared plaque morphology and hemodynamic parameters between culprit and non-culprit lesions within the same patients. Thus, large-scale prospective studies incorporating an internal control group may be warranted to validate these findings. Second, although WSS distribution can be accurately computed on the luminal surface, a gap remains between the computed WSS values and the actual biological response of the vessel wall due to the inherent technical constraints. The limited spatial resolution of CCTA precludes the precise characterization of subtle vascular geometries, such as endothelial surface microstructures, which may influence local flow patterns and WSS calculations. Further, the CFD simulations in this study assumed rigid and smooth vessel walls without accounting for vessel dynamic deformation or internal stress distribution within plaque components. Additionally, the convergence residual target of 10⁻³ and the time step size of 0.01 s are less stringent than the optimal values (10⁻⁵ to 10⁻⁴) commonly recommended in CFD studies. In our future research, we will adopt stricter convergence criteria and smaller time steps to further improve numerical accuracy. Third, while the study identified independent predictors of ACS, the causal chain from WSS to endothelial dysfunction and plaque vulnerability involves complex biological responses, including endothelial dysfunction, microcirculatory function, mechanosensing, and signal transduction mechanisms, which were not directly assessed in this study. Finally, the present study used plaque morphology and hemodynamic forces for the prediction of ACS risk. Certain plaque components, such as thin-cap fibroatheroma and other high-risk features, cannot be directly assessed due to the resolution limits of computed tomography, which may affect the comprehensive evaluation of plaque vulnerability. Future research should focus on multimodal imaging approaches, such as integration with optical coherence tomography, as well as individualized biomechanical modelling, to further explore the synergistic effects of WSS with other factors (e.g., fractional flow reserve and the degree of plaque calcification) to improve precise ACS prediction and enable early intervention.

Conclusions

This study demonstrated that combining CCTA-derived plaque morphological features with CFD-derived hemodynamic parameters, particularly WSS_prox, significantly enhanced the prediction of ACS risk. These study findings extend our understanding of the role of hemodynamics in plaque destabilization mechanisms and provide a potential novel biomarker for identifying high-risk lesions and optimizing intervention strategies in clinical practice.

Acknowledgments

We have benefited from the presence of our teachers and colleagues in writing this paper. They generously helped us collect the needed materials and made many invaluable suggestions. At this moment, we extend our thanks to them for their kind help, without which the paper would not have been what it is.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2702/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2702/dss

Funding: This study was supported by the Zhejiang Medicine and Health Science and Technology Project (No. 2024KY475) and Shaoxing Medicine and Health Science and Technology Project (Nos. 2022KY026 and 2024SKY016).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2702/coif). Y.L. is the employee of Boea Wisdom (Hangzhou) Network Technology Co. Ltd. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Academic Ethics Committee of Shaoxing People’s Hospital (No. 2023-012-01) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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