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Onsite Computed Tomography Fractional Flow Reserve in Patients with Suspected Stable Coronary Artery Disease Initial Experience
Introduction Computed tomographic coronary angiographic (CTCA) has evolved into a robust technique to detect significant stenosis; however, there is a discordance in the anatomic and functional significance of stenosis. Therefore, patients with stable coronary artery disease need to be further evaluated before deciding for any revascularization procedure.
Material and Methods A total of 100 consecutive patients of suspected stable coronary artery disease who underwent CTCA were evaluated for functional significance of lesions using onsite computed tomography fractional flow reserve (CTFFR) and the results were compared for detection of both significant and hemodynamically significant/severe stenosis on per vessel and per patient basis and differences were statistically analyzed. Impact of these differences were analyzed for the final outcome and management plan.
Results CTCA detected 33 patients with severe stenosis, while 54 patients had hemodynamically significant stenosis on CTFFR. The sensitivity and specificity of CTCA for the detection of significant coronary artery stenosis per vessel basis were 97.7 and 93.3%, respectively, with a negative predictive value of 98.0%. For severe coronary artery stenosis, sensitivity, specificity, and negative and positive predictive values were 51.6, 89, 68.8, and 80%, respectively, on per vessel basis with CTCA. On per patient basis, CTCA showed as sensitivity and specificity of 61 and 84%, respectively, with area under curve (AUC) being 0.92 and 0.67 for significant and severe stenosis, respectively.
Discussion Onsite CTFFR is a useful tool to calculate functionally significant stenosis and also improves the sensitivity and specificity of CTCA. CTFFR detected 12% more stenotic vessels in the present study on per vessel basis and 21% on per patient basis.
28 July 2021 (online)
© 2021. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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