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, 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

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.

Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT03298282].

Keywords: computational fluid dynamics (CFD); coronary computed tomography angiography (coronary CTA); fractional flow reserve (FFR); fusion image; optical coherence tomography (OCT).

Conflict of interest statement

GG was a consultant in St. Jude Medical and has received institutional research grants. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Lee, Kim, Ha, Kim, Guagliumi, Granada, Lee, Lee, Cho, Yoon, Lee, Kim, Jang, Oh, Lee, Hong, Ahn, Kim, Chang, Ko, Choi, Hong, Jang, Lee and Kim.

Figures

FIGURE 1
FIGURE 1
Overview of estimating CFD-based computational FFR from coronary CTA-OCT fusion images in patients with intermediate coronary stenosis. The current study evaluated a novel approach to estimate CFD-based computational FFR from coronary CTA-OCT fusion images in patients with intermediate coronary stenosis in the left anterior descending artery. CFD, computational fluid dynamics; CTA, computed tomography angiography; CT-FFR, computational FFR from coronary CTA; FFR, fractional flow reserve; Fusion-FFR, computational FFR from coronary CTA-OCT fusion images; OCT, optical coherence tomography; OCT-FFR, computational FFR from OCT.
FIGURE 2
FIGURE 2
Relationship between pressure wire-based FFR and Fusion-FFR. Correlation (A) and agreement (B) between pressure wire-based FFR and Fusion-FFR. FFR, fractional flow reserve; Fusion-FFR, computational FFR from coronary CTA-OCT fusion images.
FIGURE 3
FIGURE 3
Correlation between pressure wire-based FFR and CFD-based computational FFR from coronary CTA or OCT images. Correlation between pressure wire-based FFR and CT-FFR (A), and between FFR and OCT-FFR (B). CFD, computational fluid dynamics; CTA, computed tomography angiography; CT-FFR, computational FFR from coronary CTA; FFR, fractional flow reserve; OCT, optical coherence tomography; OCT-FFR, computational FFR from OCT.
FIGURE 4
FIGURE 4
Receiver operating characteristics curves in assessing functionally significant stenosis for CFD-based computational FFRs and anatomic variables. Receiver operating characteristics (ROC) curves with area under the curve to assess functionally significant stenosis for Fusion-FFR, OCT-FFR, CT-FFR, percentage area stenosis on OCT, and percentage coronary CTA stenosis. CFD, computational fluid dynamics; CTA, computed tomography angiography; CT-FFR, computational FFR from coronary CTA; FFR, fractional flow reserve; Fusion-FFR, computational FFR from coronary CTA-OCT fusion images; OCT, optical coherence tomography; OCT-FFR, computational FFR from OCT.

References

    1. Pijls NH, De Bruyne B, Peels K, Van Der Voort PH, Bonnier HJ, Bartunek JKJJ, et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med. (1996) 334:1703–8. 10.1056/nejm199606273342604
    1. Kern MJ, Samady H. Current concepts of integrated coronary physiology in the catheterization laboratory. J Am Coll Cardiol. (2010) 55:173–85. 10.1016/j.jacc.2009.06.062
    1. Fihn SD, Gardin JM, Abrams J, Berra K, Blankenship JC, Dallas AP, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. Circulation. (2012) 126:e354–471. 10.1161/CIR.0b013e318277d6a0
    1. Neumann FJ, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. (2019) 40:87–165. 10.1093/eurheartj/ehy394
    1. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. (2020) 41:407–77. 10.1093/eurheartj/ehz425
    1. Pijls NH, van Schaardenburgh P, Manoharan G, Boersma E, Bech JW, van’t Veer M, et al. Percutaneous coronary intervention of functionally nonsignificant stenosis: 5-year follow-up of the DEFER Study. J Am Coll Cardiol. (2007) 49:2105–11. 10.1016/j.jacc.2007.01.087
    1. Tonino PA, De Bruyne B, Pijls NH, Siebert U, Ikeno F, van’ t Veer M, et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. (2009) 360:213–24. 10.1056/NEJMoa0807611
    1. Min JK, Shaw LJ, Berman DS. The present state of coronary computed tomography angiography a process in evolution. J Am Coll Cardiol. (2010) 55:957–65. 10.1016/j.jacc.2009.08.087
    1. Mintz GS. Clinical utility of intravascular imaging and physiology in coronary artery disease. J Am Coll Cardiol. (2014) 64:207–22. 10.1016/j.jacc.2014.01.015
    1. Sinclair H, Bourantas C, Bagnall A, Mintz GS, Kunadian V. OCT for the identification of vulnerable plaque in acute coronary syndrome. J Am Coll Cardiol Img. (2015) 8:198–209. 10.1016/j.jcmg.2014.12.005
    1. Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. (2011) 58:1989–97. 10.1016/j.jacc.2011.06.066
    1. Norgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of coronary blood flow using CT angiography: next steps). J Am Coll Cardiol. (2014) 63:1145–55. 10.1016/j.jacc.2013.11.043
    1. Ha J, Kim JS, Lim J, Kim G, Lee S, Lee JS, et al. Assessing computational fractional flow reserve from optical coherence tomography in patients with intermediate coronary stenosis in the left anterior descending artery. Circ Cardiovasc Interv. (2016) 9:e003613. 10.1161/circinterventions.116.003613
    1. Yu W, Huang J, Jia D, Chen S, Raffel OC, Ding D, et al. Diagnostic accuracy of intracoronary optical coherence tomography-derived fractional flow reserve for assessment of coronary stenosis severity. EuroIntervention. (2019) 15:189–97. 10.4244/eij-d-19-00182
    1. Yonetsu T, Jang IK. Advances in intravascular imaging: new insights into the vulnerable plaque from imaging studies. Korean Circ J. (2018) 48:1–15. 10.4070/kcj.2017.0182
    1. Abbara S, Blanke P, Maroules CD, Cheezum M, Choi AD, Han BK, et al. SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: a report of the society of Cardiovascular Computed Tomography Guidelines Committee: endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr. (2016) 10:435–49. 10.1016/j.jcct.2016.10.002
    1. Leipsic J, Abbara S, Achenbach S, Cury R, Earls JP, Mancini GJ, et al. SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr. (2014) 8:342–58. 10.1016/j.jcct.2014.07.003
    1. Akbar A, Khwaja TS, Javaid A, Kim JS, Ha J. Automated accurate lumen segmentation using L-mode interpolation for three-dimensional intravascular optical coherence tomography. Biomed Opt Express. (2019) 10:5325–36. 10.1364/boe.10.005325
    1. Kang DY, Ahn JM, Kim YW, Moon JY, Lee JS, Koo BK, et al. Impact of coronary lesion geometry on fractional flow reserve: data from interventional cardiology research in-cooperation society-fractional flow reserve and intravascular ultrasound registry. Circ Cardiovasc Imaging. (2018) 11:e007087. 10.1161/circimaging.117.007087
    1. Kim YW, Yu H-J, Kim J-S, Ha J, Choi J, Lee JS. Coronary artery decision algorithm trained by two-step machine learning algorithm. RSC Adv. (2020) 10:4014–22. 10.1039/C9RA08999C
    1. Boyd J, Buick JM, Green S. Analysis of the Casson and Carreau-Yasuda non-Newtonian blood models in steady and oscillatory flows using the lattice Boltzmann method. Phys Fluids. (2007) 19:093103. 10.1063/1.2772250
    1. Madhavan S, Kemmerling EMC. The effect of inlet and outlet boundary conditions in image-based CFD modeling of aortic flow. BioMed Eng Online. (2018) 17:1–20. 10.1186/s12938-018-0497-1
    1. Schäfer M, Barker AJ, Kheyfets V, Stenmark KR, Crapo J, Yeager ME, et al. Helicity and vorticity of pulmonary arterial flow in patients with pulmonary hypertension: quantitative analysis of flow formations. J Am Heart Assoc. (2017) 6:e007010. 10.1161/jaha.117.007010
    1. Gijsen F, Katagiri Y, Barlis P, Bourantas C, Collet C, Coskun U, et al. Expert recommendations on the assessment of wall shear stress in human coronary arteries: existing methodologies, technical considerations, and clinical applications. Eur Heart J. (2019) 40:3421–33. 10.1093/eurheartj/ehz551
    1. Park SJ, Kang SJ, Ahn JM, Shim EB, Kim YT, Yun SC, et al. Visual-functional mismatch between coronary angiography and fractional flow reserve. J Am Coll Cardiol Intv. (2012) 5:1029–36. 10.1016/j.jcin.2012.07.007
    1. Kweon J, Kang SJ, Kim YH, Lee JG, Han S, Ha H, et al. Impact of coronary lumen reconstruction on the estimation of endothelial shear stress: in vivo comparison of three-dimensional quantitative coronary angiography and three-dimensional fusion combining optical coherent tomography. Eur Heart J Cardiovasc Imaging. (2018) 19:1134–41. 10.1093/ehjci/jex222
    1. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. (2013) 61:2233–41. 10.1016/j.jacc.2012.11.083
    1. Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging. (2018) 11:e007217. 10.1161/circimaging.117.007217
    1. Tesche C, Otani K, De Cecco CN, Coenen A, De Geer J, Kruk M, et al. Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry. J Am Coll Cardiol Img. (2020) 13:760–70. 10.1016/j.jcmg.2019.06.027
    1. Min HS, Yoo JH, Kang SJ, Lee JG, Cho H, Lee PH, et al. Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning. EuroIntervention. (2020) 16:404–12. 10.4244/eij-d-1x00487
    1. Yang S, Koo BK, Hoshino M, Lee JM, Murai T, Park J, et al. CT Angiographic and plaque predictors of functionally significant coronary disease and outcome using machine learning. J Am Coll Cardiol Img. (2020) 14:629–41. 10.1016/j.jcmg.2020.08.025
    1. Kang SJ, Lee JY, Ahn JM, Mintz GS, Kim WJ, Park DW, et al. Validation of intravascular ultrasound-derived parameters with fractional flow reserve for assessment of coronary stenosis severity. Circ Cardiovasc Interv. (2011) 4:65–71. 10.1161/circinterventions.110.959148

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi