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Wall shear stress estimated by 3D-QCA can predict cardiovascular events in lesions with borderline negative
Vincenzo Tufaro1, Hannah Safi2, Ryo Torii3
1Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.
Insights
Three-dimensional quantitative coronary angiography (3D-QCA) derived wall shear stress (WSS) can identify high-risk coronary lesions with borderline fractional flow reserve (FFR). These WSS values predict adverse cardiovascular events, aiding in risk stratification.
Area of Science:
- Cardiovascular Imaging
- Biomedical Engineering
- Interventional Cardiology
Background:
- Wall shear stress (WSS) from 3D-QCA models shows potential in predicting cardiovascular events.
- Investigating WSS efficacy in identifying borderline fractional flow reserve (FFR) lesions that progress to adverse outcomes.
Purpose of the Study:
- To evaluate the predictive value of 3D-QCA-derived WSS for cardiovascular events in lesions with FFR between 0.81-0.85.
- To determine if WSS can differentiate lesions likely to progress and cause events.
Main Methods:
- Retrospective cohort study of 293 lesions from 286 patients with FFR 0.81-0.85.
- 3D-QCA reconstruction and blood flow simulation to analyze WSS distribution.
- Primary endpoint: composite of cardiac death, target lesion myocardial infarction, or revascularization.
Main Results:
- Culprit lesions showed higher area stenosis (AS) and maximum WSS compared to quiescent lesions.
- Multivariable analysis identified AS and maximum WSS as independent predictors of adverse events.
- Lesions with high AS and high WSS had a significantly higher event rate (27.8%).
Conclusions:
- 3D-QCA-derived WSS is a valuable tool for detecting high-risk lesions among those with borderline negative FFR.
- WSS analysis can improve risk stratification and guide clinical management for these borderline lesions.
Background And Aims:
There is some evidence of the implications of wall shear stress (WSS) derived from three-dimensional quantitative coronary angiography (3D-QCA) models in predicting adverse cardiovascular events. This study investigates the efficacy of 3D-QCA-derived WSS in detecting lesions with a borderline negative fractional flow reserve (FFR: 0.81-0.85) that progressed and caused events.
Methods:
In this retrospective cohort study, we identified 548 patients who had at least one lesion with an FFR 0.81-0.85 and complete follow-up data; 293 lesions (286 patients) with suitable angiographic characteristics were reconstructed using a dedicated 3D-QCA software and included in the analysis. In the reconstructed models blood flow simulation was performed and the value of 3D-QCA variables and WSS distribution in predicting events was examined. The primary endpoint of the study was the composite of cardiac death, target lesion related myocardial infarction or clinically indicated target lesion revascularization.
Results:
During a median follow-up of 49.4 months, 37 events were reported. Culprit lesions had a greater area stenosis [(AS), 66.1% (59.5-72.3) vs 54.8% (46.5-63.2), p<0.001], smaller minimum lumen area [(MLA), 1.66 mm2 (1.45-2.30) vs 2.10 mm2 (1.69-2.70), p=0.011] and higher maximum WSS [9.0 Pa (5.10-12.46) vs 5.0 Pa (3.37-7.54), p < 0.001] than those that remained quiescent. In multivariable analysis, AS [hazard ratio (HR): 1.06, 95% confidence interval (CI): 1.03-1.10, p=0.001] and maximum WSS (HR: 1.08, 95% CI: 1.02-1.14, p=0.012) were the only independent predictors of the primary endpoint. Lesions with an increased AS (≥58.6%) that were exposed to high WSS (≥7.69Pa) were more likely to progress and cause events (27.8%) than those with a low AS exposed to high WSS (7.4%) or those exposed to low WSS that had increased (12.8%) or low AS (2.7%, p<0.001).
Conclusions:
This study for the first time highlights the potential value of 3D-QCA-derived WSS in detecting, among lesions with a borderline negative FFR, those that cause cardiovascular events.
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