Inter-corelab agreement of angiography-derived wall shear stress distribution

Aims Wall shear stress (WSS) estimated in models reconstructed from intravascular imaging and 3D-quantitative coronary angiography (QCA) data provides important prognostic information and enables the identification of high-risk lesions. However, these analyses are time-consuming and require expertise limiting WSS applications and adoption in clinical practice. Recently, a novel software has been developed for real-time computation of WSS and multidirectional WSS distribution. The aim of this study is to examine its inter-corelab reproducibility. Methods and results Sixty lesions (20 in coronary bifurcations) with a borderline negative fractional flow reserve were selected and processed using the CAAS Workstation WSS prototype (version 6.0 PIE MEDICAL) to estimate WSS and multi-directional WSS. Analysis was performed by two Core laboratories; the estimations of the two corelabs for the WSS in 3 mm segments across each reconstructed vessel were extracted and compared. To examine the concordance between inter-observer analysis of clinically relevant values, the scatterplot was divided into 4 quadrants with AS 61.3% and maximum WSS 8.24 Pa based on the previous publications and Cohen’s Kappa value was presented using these cut-off values. In total 700 segments (256 located in bifurcated vessels) were included in the present analysis. Average analysis time on the software was 219±45s in straight model and 291±61s in bifurcation model. A high intraclass correlation was noted for all the WSS metrics for the estimations of the two core labs 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). In addition, the bias between the WSS estimations was low and the limits of agreement were narrow in Bland-Altman analyses (Figure 1). When using the cut-off value of maximum WSS and % AS, a substantial agreement was noted between the two estimations of the two core labs; κ=0.77 for identifying maximum WSS >8.24Pa and κ=0.71 for identifying AS >61.3% (Figure 2). Conclusion The CAAS workstation enables reproducible 3D-QCA reconstruction and computation of WSS metrics. Further research is needed to explore its efficacy in predicting cardiovascular events.Figure 1Figure 2