The diagnostic performance of AccuFFRangio for evaluating coronary artery stenosis under different computational conditions.

Background: A computational method (AccuFFrangio) based on invasive coronary angiography (ICA) and computational fluid dynamics (CFD) to calculate fractional flow reserve (FFR) without a pressure wire has been devised to clarify the physiological significance of coronary stenosis. This study aimed to evaluate the diagnostic performance of AccuFFRangio computation under different boundary conditions and vessel reconstruction approaches.
Methods: Consecutive patients with stable angina pectoris who underwent ICA and FFR assessment from 2 centers were analyzed retrospectively. Using wire-based FFR as the reference standard, the diagnostic performances of AccuFFRangio and its variations were evaluated and compared. The calculation of AccuFFRangio involves several key boundary conditions, including patient-specific aortic pressure, contrast flow velocity derived from the thrombolysis in myocardial infarction (TIMI) frame count method, and vessel reconstruction based on 2 angiographic views. We considered the following 3 variations: (I) a fixed aortic pressure [fixed pressure AccuFFRangio (pAccuFFRangio)], (II) an empirical hyperemic velocity [fixed velocity AccuFFRangio (vAccuFFRangio)], and (III) vessel reconstruction using a single angiographic view [single view AccuFFRangio (sAccuFFRangio)].
Results: A total of 230 patients with 230 vessels were included in the final analysis. The accuracy for standard AccuFFRangio, pAccuFFRangio, vAccuFFRangio, and sAccuFFRangio was 93.91%, 86.52%, 81.74%, and 83.48%, respectively; the sensitivity was 90.74%, 51.85%, 83.33%, and 46.30%, respectively; the specificity was 94.89%, 97.16%, 81.25%, and 94.89%, respectively; and the area under the receiver operating characteristic curve was 0.971, 0.928, 0.892, and 0.870, respectively.
Conclusions: The comparison suggested that the overall performance of the standard AccuFFRangio was superior to other variations and had the highest accuracy among all the cases.
Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-893/coif). XS received grants from the Major Project of Social Development of Jinhua Science and Technology Project (No. 2021-3-028). LD received grants from the National Natural Science Foundation of China (No. 82170329). CL received grants from the Zhejiang Provincial Public Welfare Technology Research Project (No. LGF20H020012). JJ received grants from the National Natural Science Foundation of China (No. 81100141, 81570322, and 82170332) and grants from the Zhejiang Provincial Key Research and Development Plan (No. 2020C03016). YH and XY are the employees of ArteryFlow. Xiaochang Leng is a cofounder of ArteryFlow. JX is the chief executive officer of ArteryFlow. The other authors have no conflicts of interest to declare.
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