Frontiers in Cardiovascular Medicine (Jun 2022)

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis

  • Yong-Joon Lee,
  • Young Woo Kim,
  • Jinyong Ha,
  • Minug Kim,
  • Giulio Guagliumi,
  • Juan F. Granada,
  • Seul-Gee Lee,
  • Jung-Jae Lee,
  • Yun-Kyeong Cho,
  • Hyuck Jun Yoon,
  • Jung Hee Lee,
  • Ung Kim,
  • Ji-Yong Jang,
  • Seung-Jin Oh,
  • Seung-Jun Lee,
  • Sung-Jin Hong,
  • Chul-Min Ahn,
  • Byeong-Keuk Kim,
  • Hyuk-Jae Chang,
  • Young-Guk Ko,
  • Donghoon Choi,
  • Myeong-Ki Hong,
  • Yangsoo Jang,
  • Joon Sang Lee,
  • Jung-Sun Kim

DOI
https://doi.org/10.3389/fcvm.2022.925414
Journal volume & issue
Vol. 9

Abstract

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BackgroundCoronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images.MethodsAmong patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR.ResultsFusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012).ConclusionCFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03298282].

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