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Prospective evaluation of the learning curve and diagnostic accuracy for Pre-TAVI cardiac computed tomography analysis by cardiologists in training: The LEARN-CT study

Published:March 11, 2022DOI:https://doi.org/10.1016/j.jcct.2022.03.002

      Abstract

      Background

      To investigate the learning curve and the minimum number of cases required for a cardiologist in training to acquire the skills to an accurate pre-TAVI cardiac CT (CCT) analysis using a semi-automatic software.

      Methods

      In this prospective, observational study, 40 CCTs of patients scheduled for TAVI were independently evaluated twice by 5 readers (80 readings each, 400 in total): a certified TAVI-CT specialist served as the reference reader (RR) and 4 cardiology fellows (2 interventional and 2 non-invasive cardiac imaging) as readers. The primary outcome was the minimum number of cases required to achieve an accuracy in imaging interpretation ≥80%, defined as the agreement between each reader and the RR in both balloon and self-expandable valve size choice. The secondary outcomes were the intra- and inter-observer variability.

      Results

      After 50 readings (25 cases repeated twice) cardiology fellows were able to select the appropriate valve size with 80% of accuracy compared to the RR, independently of valve calcification, image quality and slice thickness. Learning curves of both interventional and non-invasive cardiac imaging fellows showed a similar trend. Cardiology fellows achieved a very high intra- and inter-observer reliability for both perimeter and area assessment, with an intraclass correlation coefficient (ICC) ranging from 0.96 to 0.99.

      Conclusions

      Despite the individual differences, cardiology fellows required 50 readings (25 cases repeated twice) to get adequately skilled in the pre-TAVI CCT interpretation. These results provide valuable information for developing adequate training sessions and education protocols for both companies and cardiologists involved.

      Graphical abstract

      Keywords

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