Diagnostic accuracy of a novel on-site virtual fractional flow reserve parallel computing system

Hyung Bok Park, Yeonggul Jang, Reza Arsanjani, Minh Tuan Nguyen, Sang Eun Lee, Byunghwan Jeon, Sunghee Jung, Youngtaek Hong, Seongmin Ha, Sekeun Kim, Sang Wook Lee, Hyuk Jae Chang

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1 Citation (Scopus)


Purpose: To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA). Materials and Methods: We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75. Results: The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI-0.011 to 0.021) with 95% limits of agreement of-0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75. Conclusion: Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.

Original languageEnglish
Pages (from-to)137-144
Number of pages8
JournalYonsei medical journal
Issue number2
Publication statusPublished - 2020 Feb

Bibliographical note

Funding Information:
This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0101-16-0171, Development of Multi-modality Imaging and 3D Simulation-Based Integrative Diagnosis-Treatment Support Software System for Cardiovascular Diseases).

Publisher Copyright:
© Yonsei University College of Medicine 2020.

All Science Journal Classification (ASJC) codes

  • Medicine(all)


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