Agreement of wall shear stress distribution between two core laboratories using three-dimensional quantitative coronary angiography

Author:

Kageyama Shigetaka1,Tufaro Vincenzo2,Torii Ryo3,Karamasis Grigoris4,Rakhit Roby5,Poon Eric6,Aben Jean-Paul7,Baumbach Andreas2,Serruys Patrick1,Onuma Yoshinobu1,Bourantas Christos2

Affiliation:

1. National University of Ireland, Galway

2. Barts Health NHS Trust

3. University College London

4. Basildon and Thurrock University Hospitals NHS Foundation Trust

5. The Royal Free Hospital

6. University of Melbourne

7. Pie Medical Imaging

Abstract

Abstract Purpose: Wall shear stress (WSS) estimated in models reconstructed from intravascular imaging and 3-dimensional-quantitative coronary angiography (3D-QCA) data provides important prognostic information and enables identification of high-risk lesions. However, these analyses are time-consuming and require expertise, limiting WSS adoption in clinical practice. Recently, a novel software has been developed for real-time computation of time-averaged WSS (TAWSS) and multidirectional WSS distribution. This study aims to examine its inter-corelab reproducibility. Methods: Sixty lesions (20 coronary bifurcations) with a borderline negative fractional flow reserve were processed using the CAAS Workstation WSS prototype to estimate WSS and multi-directional WSS values. Analysis was performed by two corelabs and their estimations for the WSS in 3mm segments across each reconstructed vessel were extracted and compared. Results: In total 700 segments (256 located in bifurcated vessels) were included in the analysis. A high intra-class correlation was noted for all the 3D-QCA and TAWSS metrics between the estimations of the two corelabs irrespective of the presence (range: 0.90-0.92) or absence (range: 0.89-0.90) of a coronary bifurcation, while the ICC was good-moderate for the multidirectional WSS (range: 0.72-0.86). Lesion level analysis demonstrated a high agreement of the two corelabls for detecting lesions exposed to an unfavourable haemodynamic environment (WSS >8.24Pa, κ=0.77) that had a high-risk morphology (area stenosis >61.3%, κ=0.71) and were prone to progress and cause events. Conclusion: The CAAS Workstation WSS enables reproducible 3D-QCA reconstruction and computation of WSS metrics. Further research is needed to explore its value in detecting high-risk lesions.

Publisher

Research Square Platform LLC

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