Related Experiment Video
Updated: Jul 12, 2026
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Comprehensive risk prediction of acute coronary syndrome by integrating coronary plaque morphology and hemodynamic
Danling Guo1, Guanzuan Wu1, Huaifeng Li1
1Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
Insights
Computational fluid dynamics (CFD) combined with coronary computed tomography angiography (CCTA) significantly improves acute coronary syndrome (ACS) prediction. Integrating CFD-derived wall shear stress (WSS) enhances the identification of high-risk lesions, aiding in better ACS risk stratification.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Acute coronary syndrome (ACS) is a severe manifestation of coronary artery disease (CAD).
- Coronary computed tomography angiography (CCTA) assesses plaque anatomy but has limited predictive accuracy for ACS.
- Integrating computational fluid dynamics (CFD) with CCTA may improve ACS prediction.
Purpose of the Study:
- To investigate the combined value of CCTA-derived plaque characteristics and CFD-derived local wall shear stress (WSS) for predicting ACS.
- To evaluate the predictive and reclassification performance of models incorporating hemodynamic parameters.
Main Methods:
- Retrospective analysis of 216 lesions in 85 patients who underwent CCTA.
- Comparison of CCTA-derived morphological features and CFD-calculated WSS between culprit and non-culprit lesions.
- Multivariate logistic regression and performance evaluation using C-index, NRI, and IDI.
Main Results:
- Culprit lesions showed more severe stenosis, longer length, and higher prevalence of high-risk plaque (HRP).
- CFD-derived WSS parameters, particularly WSSprox, were significantly higher in culprit lesions.
- The addition of hemodynamic parameters improved ACS prediction and reclassification, with WSSprox showing the highest predictive value.
Conclusions:
- CFD-derived non-invasive hemodynamic assessment, specifically WSS, significantly enhances ACS prediction models.
- Integrating hemodynamic parameters can improve the identification of culprit lesions associated with future ACS.
- This approach has potential diagnostic and therapeutic implications for managing ACS risk.
Background:
Acute coronary syndrome (ACS) represents a severe manifestation of coronary artery disease (CAD). Although coronary computed tomography angiography (CCTA) enables the effective assessment of the anatomical characteristics of plaque and high-risk morphological features, its predictive accuracy for ACS remains limited. This study aimed to integrate computational fluid dynamics (CFD) techniques with CCTA image data to investigate the combined value of CCTA-derived plaque characteristics and CFD-derived local wall shear stress (WSS) for predicting ACS.
Methods:
This retrospective study included 85 patients who underwent CCTA between January 1, 2022, and January 31, 2024, and subsequently developed ACS, including acute myocardial infarction or unstable angina with plaque rupture. A total of 216 lesions were analyzed and classified as culprit or non-culprit lesions based on treatment status. CCTA-derived morphological features and CFD-calculated WSS parameters were compared between the 83 culprit and 133 non-culprit lesions. Multivariate logistic regression was used to identify independent predictors of ACS. Different predictive models were constructed, and their discriminative and reclassification performances were evaluated using the Harrell's concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI).
Results:
Morphologically, culprit lesions exhibited more severe stenosis (70.6%±8.5% vs. 66.4%±7.8%, P<0.001), longer lesion length (15.8±8.4 vs. 12.1±7.4 mm, P=0.002), and a higher prevalence of high-risk plaque (HRP) (51.4% vs. 48.6%, P=0.02). Hemodynamically, the culprit lesions exhibited significantly higher total WSS {17.3 [interquartile range (IQR), 11.7-24.1] vs. 14.9 (IQR, 9.6-20.2) Pa, P=0.045} and proximal WSS (WSSprox) [10.3 (4.4-17.1) vs. 6.4 (3.4-11.4) Pa, P<0.001]. Stenosis, lesion length, HRP, and hemodynamic parameters were associated with the subsequent occurrence of ACS. The addition of hemodynamic parameters improved the ability of the model to predict and reclassify ACS. Among the individual indicators added to the model, WSSprox showed the highest C-index (0.767 vs. 0.733; P=0.048), as well as the greatest incremental and net reclassification improvement (NRI: 0.369, P<0.007; relative IDI: 0.048, P<0.001).
Conclusions:
CFD-derived non-invasive hemodynamic assessment, particularly WSS, significantly enhanced the ability of the model to predict ACS. The integration of non-invasive hemodynamic parameters may improve the identification of culprit lesions associated with future ACS, with relevant diagnostic and therapeutic implications.
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Coronary Artery Disease I: Introduction
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:26Coronary Artery Disease II: Pathophysiology
01:26Coronary Artery Disease IV: Preventive Measures
01:19Acute Coronary Syndrome II: Pathophysiology and Clinical Manifestations