Choosing the optimal wall shear parameter for the prediction of plaque location—A patient-specific computational study in human right coronary arteries
Introduction
Spatial and temporal variations of arterial endothelial wall shear–stress (WSS) are believed to influence the location of potential atherosclerotic plaque formation [1], [2], [3]. While the correlation between WSS and plaque location is not fully understood, several parameters that are derived from WSS distribution, such as average wall shear–stress (AWSS), average wall shear–stress gradient (AWSSG), oscillatory shear index (OSI) and relative residence time (RRT) have been identified as possible indicators for atherosclerotic lesion prone sites [4], [5], [6], [7]. WSS is the direct result of blood flow and, hence, subject specific variations in arterial anatomy will influence those parameters [8]. Computational fluid dynamics (CFD) has proven to be the method of choice for obtaining accurate readings of flow and WSS distribution in subject specific arterial geometries. However, the process beginning with acquisition of the vessel anatomy using imaging techniques such as computed tomography (CT) to the processing of the CFD results, with numerous steps in between, is complex, costly and time consuming. Most previous CFD studies of the coronary arteries have been limited to a small number of vessel geometries (1–7) [9], [10], [11], [12], [13] or have used an averaged anatomy [14]. Another limitation of previous studies has been their attempt to correlate subject specific WSS parameters to common plaque locations such as vessel bifurcations, rather than with actual lesion sites of the respective subject [15].
We present herein an analysis of the hemodynamic parameters AWSS, AWSSG, OSI and RRT obtained through a CFD study on the right coronary arteries of 30 patients. The plaques in the arteries were virtually removed to replicate the healthy state of the vessels prior to the onset of atherosclerosis. We correlate these parameters to each patient's specific plaque profile, and determine the sensitivity and positive predictive value of each parameter with respect to predicting the particular plaque locations.
Section snippets
Methods
Our patient population consisted of 80% (24/30) males with an average age and body mass index (BMI) of 67.1 ± 9.2 years [range 47–84] and 26.3 ± 4.3 kg/m2 [range 15.6–36.3], respectively. Demographic data and clinical characteristics of the patients are summarized in Table 1. All plaques studied were stenotic.
Results
In the 30 patients, we found with the reference standard a total of 44, 45 and 31 plaques in segments 1, 2 and 3 of the RCA, respectively. The sensitivity and PPV of correctly identified plaques for the wall shear parameters over the entire RCA are listed in Table 2A. We found AWSS to be significantly more sensitive in predicting plaque locations than OSI (p < 0.05, Table 2B). OSI proved to have a significantly higher PPV than AWSS (p < 0.001) and AWSSG (p < 0.001), as did RRT (AWSS p < 0.05) and AWSSG
Discussion
Although low AWSS, high AWSSG, high OSI and high RRT are believed to correlate to atherosclerotic lesion prone sites, to our knowledge, the work at hand is the first larger patient study to compare these parameters in patient-specific representations of healthy RCA anatomies to the same patients’ detailed plaque profiles once the vessels have become atherosclerotic.
The prevailing hypotheses that link non-uniform flow as mechanisms for the initiation of abnormal physical and biological events
Conclusion
We have shown a statistically significant difference between AWSS and OSI in sensitivity and PPV for the identification of atherosclerotic lesion prone sites in the RCA. The higher PPV of OSI and its tendency to pinpoint the precise location of plaque initiation warrant further research. Likewise, further studies should investigate RRT and its higher PPV, yet tendency to locate more of the entire plaque region. The high sensitivity of AWSS and its simple calculation would make it the prime
Conflicts of interest
We have no conflicts of interest.
Acknowledgements
We kindly acknowledge the financial support of the Swiss National Science Foundation through the National Center of Competence in Research in Computer Aided and Image Guided Medical Interventions (NCCR Co-Me).
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