An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data — Vincenzo Tufaro (2022) | RDL Network
An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data
International Journal of Cardiology 357: 14-19
Article 2022 English
Authors
VT
Vincenzo Tufaro
RT
Ryo Torii
EE
Emrah Erdoğan
Abstract
1 min read
Background
Wall shear stress (WSS) estimated in 3D-quantitative coronary angiography (QCA) models appears to provide useful prognostic information and identifies high-risk patients and lesions. However, conventional computational fluid dynamics (CFD) analysis is cumbersome limiting its application in the clinical arena. This report introduces a user-friendly software that allows real-time WSS computation and examines its reproducibility and accuracy in assessing WSS distribution against conventional CFD analysis.
Methods
From a registry of 414 patients with borderline negative fractional flow reserve (0.81–0.85), 100 lesions were randomly selected. 3D-QCA and CFD analysis were performed using the conventional approach and the novel CAAS Workstation WSS software, and QCA as well as WSS estimations of the two approaches were compared. The reproducibility of the two methodologies was evaluated in a subgroup of 50 lesions.
Results
A good agreement was noted between the conventional approach and the novel software for 3D-QCA metrics (ICC range: 0.73–0-93) and maximum WSS at the lesion site (ICC: 0.88). Both methodologies had a high reproducibility in assessing lesion severity (ICC range: 0.83–0.97 for the conventional approach; 0.84–0.96 for the CAAS Workstation WSS software) and WSS distribution (ICC: 0.85–0.89 and 0.83–0.87, respectively). Simulation time was significantly shorter using the CAAS Workstation WSS software compared to the conventional approach (4.13 ± 0.59 min vs 23.14 ± 2.56 min, p < 0.001).
Conclusion
CAAS Workstation WSS software is fast, reproducible, and accurate in assessing WSS distribution. Therefore, this software is expected to enable the broad use of WSS metrics in the clinical arena to identify high-risk lesions and vulnerable patients.
Shigetaka Kageyama, Vincenzo Tufaro, Ryo Torii, Grigoris V. Karamasis, Roby Rakhit, Eric Poon, Jean‐Paul Aben, Andreas Baumbach, Patrick W. Serruys, Yoshinobu Onuma, Christos V. Bourantas
The International Journal of Cardiovascular Imaging
Shigetaka Kageyama, Vincenzo Tufaro, Ryo Torii, Grigoris V. Karamasis, Roby Rakhit, Eric Poon, Jean‐Paul Aben, Andreas Baumbach, Patrick W. Serruys, Yoshinobu Onuma, Christos V. Bourantas
Shigetaka Kageyama, Vincenzo Tufaro, Ryo Torii, Grigoris V. Karamasis, Roby Rakhit, Eric Poon, Jean-Paul Aben, Andreas Baumbach, Patrick W. Serruys, Yoshinobu Onuma, Christos V. Bourantas
Vincenzo Tufaro, Ryo Torii, Jean‐Paul Aben, Ramya Parasa, Bon‐Kwon Koo, Roby Rakhit, Grigoris V. Karamasis, İbrahım Halıl Tanboğa, Ameer Khan, Michael McKenna, Murat Çap, Mazen Abou Gamrah, Patrick W. Serruys, Yoshinobu Onuma, Giulio Stefanini, Daniel A. Jones, Krishnaraj S. Rathod, Anthony Mathur, Andreas Baumbach, Christos V. Bourantas
Maik J. Grundeken, Carlos Collet, Yuki Ishibashi, Philippe Généreux, Takashi Muramatsu, Laura LaSalle, Aaron V. Kaplan, Joanna J. Wykrzykowska, Marie-Angèle Morel, Jan G.P. Tijssen, Robbert J. de Winter, Yoshinobu Onuma, Martin B. Leon, Patrick W. Serruys
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