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Updated: Oct 12, 2025
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Risk of myocardial infarction based on endothelial shear stress analysis using coronary angiography
Alessandro Candreva1, Mattia Pagnoni2, Maurizio Lodi Rizzini3
1Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium; Dept. of Cardiology, Zurich University Hospital, Zurich, Switzerland; Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
Insights
Wall shear stress (WSS) analysis from coronary angiography can identify heart attack-causing lesions. A novel WSS descriptor, TSVI, demonstrated strong predictive power for future myocardial infarction (MI).
Area of Science:
- Cardiovascular imaging and hemodynamics
- Biomedical engineering and fluid dynamics
- Interventional cardiology and atherosclerosis research
Background:
- Wall shear stress (WSS) is implicated in the development and progression of atherosclerosis.
- Detecting vulnerable atherosclerotic plaques is crucial for preventing myocardial infarction (MI).
- Conventional angiography lacks detailed hemodynamic information to assess plaque vulnerability.
Purpose of the Study:
- To evaluate the utility of WSS analysis from 3D quantitative coronary angiography (3DQCA) in identifying lesions that cause future MI.
- To compare the predictive performance of different WSS descriptors, including the novel topological shear variation index (TSVI).
- To assess the combined predictive value of WSS with traditional stenosis measures and pressure gradients.
Main Methods:
- Three-dimensional quantitative coronary angiography (3DQCA) was employed to compute WSS and pressure drop in 80 patients.
- WSS parameters, including time-averaged WSS (TAWSS) and TSVI, were compared between culprit lesions (n=80) and non-culprit lesions (n=108).
- Computational fluid dynamics were used to analyze endothelium-blood flow interaction, and predictive models were developed.
Main Results:
- Culprit lesions exhibited significantly higher percent area stenosis (%AS), translesional vFFR difference (ΔvFFR), TAWSS, and TSVI compared to non-culprit lesions.
- TSVI demonstrated superior predictive capability for MI compared to TAWSS (AUC-TSVI=0.77 vs. AUC-TAWSS=0.61).
- Incorporating TSVI into a model with %AS and ΔvFFR significantly improved prediction and reclassification of MI events.
Conclusions:
- 3DQCA-based WSS analysis is a feasible method for identifying MI-culprit lesions.
- The combination of anatomical stenosis, pressure gradients, and WSS analysis effectively predicts MI occurrence.
- The novel TSVI descriptor shows significant potential for detecting high-risk lesions prone to causing MI.
Background And Aims:
Wall shear stress (WSS) has been associated with atherogenesis and plaque progression. The present study assessed the value of WSS analysis derived from conventional coronary angiography to detect lesions culprit for future myocardial infarction (MI).
Methods And Results:
Three-dimensional quantitative coronary angiography (3DQCA), was used to calculate WSS and pressure drop in 80 patients. WSS descriptors were compared between 80 lesions culprit of future MI and 108 non-culprit lesions (controls). Endothelium-blood flow interaction was assessed by computational fluid dynamics (10.8 ± 1.41 min per vessel). Median time between baseline angiography and MI was 25.9 (21.9-29.8) months. Mean patient age was 70.3 ± 12.7. Clinical presentation was STEMI in 35% and NSTEMI in 65%. Culprit lesions showed higher percent area stenosis (%AS), translesional vFFR difference (ΔvFFR), time-averaged WSS (TAWSS) and topological shear variation index (TSVI) compared to non-culprit lesions (p < 0.05 for all). TSVI was superior to TAWSS in predicting MI (AUC-TSVI = 0.77, 95%CI 0.71-0.84 vs. AUC-TAWSS = 0.61, 95%CI 0.53-0.69, p < 0.001). The addition of TSVI increased predictive and reclassification abilities compared to a model based on %AS and ΔvFFR (NRI = 1.04, p < 0.001, IDI = 0.22, p < 0.001).
Conclusions:
A 3DQCA-based WSS analysis was feasible and can identify lesions culprit for future MI. The combination of area stenoses, pressure gradients and WSS predicted the occurrence of MI. TSVI, a novel WSS descriptor, showed strong predictive capacity to detect lesions prone to cause MI.
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