Reproducibility of aortic valve calcification scoring with computed tomography - An interplatform analysis

To investigate whether aortic valve calcification (AVC) scoring performed with different workstation platforms generates comparable and thus software-independent results.

In this IRB-approved retrospective study, we included 100 consecutive patients with symptomatic aortic stenosis undergoing CT prior to transcatheter aortic valve implantation. Two independent observers performed AVC scoring on non-enhanced images with commercially available software platforms of four vendors (GE, Philips, Siemens, 3mensio). Gender-specific Agatston score cut-off values were applied according to current recommendations to assign patients to different likelihood categories of aortic stenosis (unlikely to very likely). Comparative analysis of Agatston scores between the four platforms were performed by using Kruskal-Wallis analysis, Spearman rank correlation, linear regression analysis, and Bland-Altman analysis. Differences in category assignment were compared using Fisher's exact test and Cohen's kappa.

For both observers, each workstation platform produced slightly different numeric AVC Agatston scores, however, without statistical significance (p = 0.96 and p = 0.98). Excellent correlation was found between platforms, with r = 0.991-0.996 (Spearman) and r² = 0.981-0.992 (regression analysis) for both observers. Bland-Altman analyses revealed small mean differences with narrow limits of agreement between platforms (mean differences: 6 ± 128 to 100 ± 179), for inter-observer (mean differences: 1 ± 43 to 12 ± 70), and intra-observer variability (mean differences: 9 ± 42 to 20 ± 96). Observer 1 assigned 11 (kappa: 0.85-0.97) and observer 2 assigned 10 patients (kappa: 0.88-0.95) to different likelihood groups of severe aortic stenosis with at least one platform. Overall, there was no significant difference of likelihood assignment between platforms (p = 0.98 and p = 1.0, respectively).

While absolute values differ slightly, common commercially available software platforms produce comparable results for AVC scoring, which indicates software-independence of the method.