Coronary Endothelial Wall Shear Stress: Another Piece of the Puzzle?

See also the article by Kalykakis and Antonopoulos et al in this issue.

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Ischemic heart disease is a leading cause of death in the United States and worldwide, posing a burden on society. Extensive research efforts are underway to improve the diagnosis, treatment, and prophylaxis of this disease. One imaging tool, CT angiography, enables the depiction of vessel anatomy and luminal changes with high spatial resolution and confidence. It depicts plaque composition and helps to estimate fractional flow reserves derived from patient-specific computational fluid dynamics to determine the hemodynamic significance of lesions (1). The ability to detect early atherosclerosis and identify factors associated with atherosclerotic plaques would enable early intervention, thereby reducing morbidity and mortality.

However, challenges remain. One of the parameters implicated in atherosclerotic plaque formation is endothelial shear stress (ESS). ESS, also referred to as wall shear stress, is the tangential frictional force generated by blood flow on the endothelial surface. ESS plays a role in vascular remodeling and atherosclerotic plaque evolution (2), yet the in vivo assessment of ESS has proven challenging.

In this issue of Radiology, Kalykakis and Antonopoulos et al (3) reanalyzed a subset of data from the Evaluation of Integrated Cardiac Imaging in Ischemic Heart Disease (EVINCI) multicenter trial. The authors investigated functional correlations of ESS with PET myocardial perfusion imaging (MPI). Specifically, they analyzed ESS estimates derived from computational fluid dynamics modeling of the coronary CT angiography (CCTA) data. By using CCTA, the authors also analyzed measures of stenosis severity, lesion-specific total plaque volume, plaque composition, and abnormal vasodilatory response identified by myocardial blood flow obtained at PET imaging. Identification of high-risk plaques before an acute event is an area of extensive and ongoing research. Studies have suggested that high ESS promotes the development of high-risk plaques (3,4). Several studies (5,6) have evaluated ESS correlation with other imaging parameters, including CCTA-derived fractional flow reserve, and suggested that different parameters can be complementary in risk stratification.

ESS and its association with vasodilatation were first evaluated in preclinical studies more than 3 decades ago. Since then, arterial shear stress has been further explored by using MR angiography and CT angiography in both preclinical models and humans in the context of endothelial dysfunction and atherosclerosis. In vivo measurements of coronary ESS can be derived from MRI and US velocity measurements, but these techniques are prone to errors because of limited spatial resolution, vessel wall motion, and assumptions on the flow profiles, among other reasons. Hence, the use of computational fluid dynamics in patient-specific models, similar in approach to the CCTA flow reserve calculations, is a compelling alternative.

The presence of higher ESS in the area of maximal luminal stenosis has been demonstrated previously. However, the relation between the ESS and plaque formation appears complex as both low (below physiologic) and high ESS are associated with the progression of atherosclerotic plaque (7,8). Identification of high-risk plaques before an acute event is an area of extensive and ongoing research. Kalykakis and Antonopoulos et al (3) found elevated ESS in coronary artery lesions that had more than 50% stenosis and impaired myocardial blood flow. Furthermore, adding ESS to the severity of CCTA stenosis improved the prediction of abnormal results from PET myocardial perfusion imaging.

Interestingly, the authors found that 22% of vessels with stenosis severity less than 50% and high ESS also had an abnormal PET MPI result. This presumably reflects abnormal vasodilatory response. Notably, plaque characteristics at CCTA were not predictors of an abnormal PET MPI finding. Several additional findings are also of interest. There was only a weak correlation of high ESS with volumes of total plaque, noncalcified plaque, and noncalcified plaque components. Moreover, the authors found no difference in plaque composition (necrotic core, fibrous, fibrofatty, and calcified plaque) for low and high ESS subgroups.

The authors concluded that adding ESS to the degree of stenosis at CT angiography improves the prediction of an abnormal PET MPI result over CCTA stenosis severity alone. Patient treatment is often based on MPI results, so adding ESS results to the degree of stenosis reported by CT angiography has the potential to influence management. However, these results require further validation. The ESS value reflects a dynamic process of vascular remodeling that can lead to the development of unfavorable plaque characteristics. Therefore, longitudinal studies, including prospective outcome-based designs, would be of interest. In their 2018 study, Kumar et al (9) reported on the complementary value of adding ESS to CCTA-derived fractional flow reserve in the prediction of myocardial infarction. Association between ESS and clinical events continues to remain an area for future investigation. Because exercise causes higher physiologic shear stress, the effect of exercise on ESS values may also be an interesting area to explore.

Important limitations of this study (correctly raised by authors) included the low number of segments with functionally significant stenosis and high ESS. This reflects the characteristics of the EVINCI study sample that included participants with intermediate pretest probability of coronary artery disease. Confirmation of the results in a different group of study participants would be beneficial. Furthermore, the lesions were classified as low versus high ESS. It has been suggested that further delineation into low, intermediate, and high ESS segments is superior to this type of dichotomic division. Of note, in vivo ESS also lacks a reference standard. Finally, it remains unclear to what degree the limitations of current computational fluid dynamics models, such as static vessel geometries and lack of modeling of fluid tissue interactions, might introduce bias into the results.

Overall, Kalykakis and Antonopoulos et al present a study design that includes not only the correlation of ESS with morphologic plaque characteristics but also hemodynamic function in the form of PET MPI. This integrated approach to plaque characteristics can improve our understanding of factors leading to the progression of atherosclerosis and help to develop appropriate early interventions. Regardless of our preferred modality or methods of imaging, our purpose is to improve patient outcomes. Identification of risk factors before the disease develops and early recognition of pathologic process are crucial for the optimization of individual patient treatment and appropriate use of health resources. Correlative imaging may be a promising path to achieve this goal. With each new data point, as presented in this article, we are one step closer to personalized and optimized patient care.