Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography-Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis
- Author(s)
- 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
- Keimyung Author(s)
- Cho, Yun Kyeong; Yoon, Hyuck Jun
- Department
- Dept. of Internal Medicine (내과학)
- Journal Title
- Front Cardiovasc Med
- Issued Date
- 2022
- Volume
- 9
- Keyword
- fractional flow reserve (FFR); coronary computed tomography angiography (coronary CTA); optical coherence tomography (OCT); fusion image; computational fluid dynamics (CFD)
- Abstract
- Background:
Coronary 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.
Methods:
Among 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.
Results:
Fusion-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).
Conclusion:
CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.
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