Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve

Published:January 30, 2021DOI:https://doi.org/10.1016/j.ijcard.2021.01.040

      Highlights

      • CTA-based plaque morphology predicts fractional flow reserve with high accuracy.
      • Sensitivity and specificity are on par compared to commercial CT-FFR derivation.
      • Plaque morphology predicts the continuous value of fractional flow reserve.

      Abstract

      Background

      To evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR.

      Methods

      Patients (n = 113, 59 ± 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR.

      Results

      A total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis.

      Conclusions

      Our results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches.

      Keywords

      To read this article in full you will need to make a payment

      Subscribe:

      Subscribe to International Journal of Cardiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Tonino P.A.
        • De Bruyne B.
        • Pijls N.H.
        • 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: 213-224
        • De Bruyne B.
        • Fearon W.F.
        • Pijls N.H.
        • Barbato E.
        • Tonino P.
        • Piroth Z.
        • et al.
        Fractional flow reserve-guided PCI for stable coronary artery disease.
        N. Engl. J. Med. 2014; 371: 1208-1217
        • Pijls N.H.
        • Fearon W.F.
        • Tonino P.A.
        • Siebert U.
        • Ikeno F.
        • Bornschein B.
        • et al.
        Fractional flow reserve versus angiography for guiding percutaneous coronary intervention in patients with multivessel coronary artery disease: 2-year follow-up of the FAME (fractional flow reserve versus angiography for multivessel evaluation) study.
        J. Am. Coll. Cardiol. 2010; 56: 177-184
        • Schwartz F.R.
        • Koweek L.M.
        • Norgaard B.L.
        Current evidence in cardiothoracic imaging: computed tomography-derived fractional flow reserve in stable chest pain.
        J. Thorac. Imaging. 2019; 34: 12-17
        • Benton Jr., S.M.
        • Tesche C.
        • De Cecco C.N.
        • Duguay T.M.
        • Schoepf U.J.
        • Bayer 2nd., R.R.
        Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review.
        J. Thorac. Imaging. 2018; 33: 88-96
        • Maroules C.D.
        • Rajiah P.
        • Bhasin M.
        • Abbara S.
        Current evidence in cardiothoracic imaging: growing evidence for coronary computed tomography angiography as a first-line test in stable chest pain.
        J. Thorac. Imaging. 2019; 34: 4-11
        • Chalkidou A.
        • Keevil S.
        • Lewis C.
        • McMillan V.
        HeartFlow Technical Evaluation.
        KiTEC - King’s Technology Evaluation Centre, 2015
        • Kishi S.
        • Giannopoulos A.A.
        • Tang A.
        • Kato N.
        • Chatzizisis Y.S.
        • Dennie C.
        • et al.
        Fractional flow reserve estimated at coronary CT angiography in intermediate lesions: comparison of diagnostic accuracy of different methods to determine coronary flow distribution.
        Radiology. 2018; 287: 76-84
        • Min J.K.
        • Chandrashekhar Y.
        • Narula J.
        Noninvasive FFRCT After STEMI: looking for the guilty bystander.
        JACC Cardiovasc. Imaging. 2017; 10: 500-502
        • Liu R.
        • Yang Q.
        • Qiao A.
        • Hou Y.
        • Ma Y.
        Noninvasive numerical simulation of coronary fractional flow reserve based on lattice Boltzmann method.
        Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018; 35: 384-389
        • Giannopoulos A.A.
        • Tang A.
        • Ge Y.
        • Cheezum M.K.
        • Steigner M.L.
        • Fujimoto S.
        • et al.
        Diagnostic performance of a lattice Boltzmann-based method for CT-based fractional flow reserve.
        EuroIntervention. 2018; 13: 1696-1704
        • Ahmadi A.
        • Leipsic J.
        • Ovrehus K.A.
        • Gaur S.
        • Bagiella E.
        • Ko B.
        • et al.
        Lesion-specific and vessel-related determinants of fractional flow reserve beyond coronary artery stenosis.
        JACC Cardiovasc. Imaging. 2018; 11: 521-530
        • Ahmadi A.
        • Stone G.W.
        • Leipsic J.
        • Serruys P.W.
        • Shaw L.
        • Hecht H.
        • et al.
        Association of coronary stenosis and plaque morphology with fractional flow reserve and outcomes.
        JAMA Cardiol. 2016; 1: 350-357
        • Narula J.
        • Nakano M.
        • Virmani R.
        • Kolodgie F.D.
        • Petersen R.
        • Newcomb R.
        • et al.
        Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques.
        J. Am. Coll. Cardiol. 2013; 61: 1041-1051
        • Baumann S.
        • Kryeziu P.
        • Tesche C.
        • Shuler D.C.
        • Becher T.
        • Rutsch M.
        • et al.
        Association of serum lipid profile with coronary computed tomographic angiography-derived morphologic and functional quantitative plaque markers.
        J. Thorac. Imaging. 2019; 34: 26-32
        • Glagov S.
        • Weisenberg E.
        • Zarins C.K.
        • Stankunavicius R.
        • Kolettis G.J.
        Compensatory enlargement of human atherosclerotic coronary arteries.
        N. Engl. J. Med. 1987; 316: 1371-1375
        • Ahmadi A.
        • Kini A.
        • Narula J.
        Discordance between ischemia and stenosis, or PINSS and NIPSS: are we ready for new vocabulary?.
        JACC Cardiovasc. Imaging. 2015; 8: 111-114
        • Lavi S.
        • Bae J.-H.
        • Rihal C.S.
        • Prasad A.
        • Barsness G.W.
        • Lennon R.J.
        • et al.
        Segmental coronary endothelial dysfunction in patients with minimal atherosclerosis is associated with necrotic core plaques.
        Heart. 2009; 95: 1525-1530
        • Choi H.
        • Uceda D.E.
        • Dey A.K.
        • Abdelrahman K.M.
        • Aksentijevich M.
        • Rodante J.A.
        • et al.
        Treatment of psoriasis with biologic therapy is associated with improvement of coronary artery plaque lipid-rich necrotic core: results from a prospective, observational study.
        Circ. Cardiovasc. Imaging. 2020; 13e011199
        • Abdelrahman K.M.
        • Chen M.Y.
        • Dey A.K.
        • Virmani R.
        • Finn A.V.
        • Khamis R.Y.
        • et al.
        Coronary computed tomography angiography from clinical uses to emerging technologies: JACC state-of-the-art review.
        J. Am. Coll. Cardiol. 2020; 76: 1226-1243
        • Zhu G.
        • Li Y.
        • Ding V.
        • Jiang B.
        • Ball R.L.
        • Rodriguez F.
        • et al.
        Semiautomated characterization of carotid artery plaque features from computed tomography angiography to predict atherosclerotic cardiovascular disease risk score.
        J. Comput. Assist. Tomogr. 2019; 43: 452-459
        • Rafailidis V.
        • Chryssogonidis I.
        • Grisan E.
        • Xerras C.
        • Cheimariotis G.
        • Tegos T.
        • et al.
        Imaging carotid artery-vulnerable plaque with ultrasound and contrastenhanced ultrasound: correlation of cerebrovascular symptoms with quantitative and multi-parametric indexes.
        Insights Imaging. 2019; 10: S541
        • Buckler A.
        510(k) K183012.
        FDA, 2018 (editor)
        • Sheahan M.
        • Ma X.
        • Paik D.
        • Obuchowski N.A.
        • St Pierre S.
        • Newman 3rd, W.P.
        • et al.
        Atherosclerotic plaque tissue: noninvasive quantitative assessment of characteristics with software-aided measurements from conventional CT angiography.
        Radiology. 2018; 286: 622-631
        • Gupta A.
        • Al-Dasuqi K.
        • Kamel H.
        • Gialdini G.
        • Baradaran H.
        • Ma X.
        • et al.
        Semi-Automated Detection of High-Risk Atherosclerotic Carotid Artery Plaque Features from Computed Tomography Angiography.
        European Stroke Conference, Berlin2017
        • Wan T.
        • Madabhushi A.
        • Phinikaridou A.
        • Hamilton J.A.
        • Hua N.
        • Pham T.
        • et al.
        Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model.
        Med. Phys. 2014; 41042303
        • Buckler A.J.
        • Keith J.C.
        Advanced Biomarkers. European Biopharmaceutical Review.
        2014: 10-14
        • Ripley B.D.
        Pattern Recognition and Neural Networks.
        Cambridge University Press, 2007
        • Hoerl A.K.
        • RW.
        Ridge regression: biased estimation for nonorthogonal problems.
        Technometrics. 1970; 12: 55-67
        • Kuhn M.
        • Johnson K.
        Applied Predictive Modeling.
        Springer, New York2013
        • Tesche C.
        • De Cecco C.N.
        • Baumann S.
        • Renker M.
        • McLaurin T.W.
        • Duguay T.M.
        • et al.
        Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling.
        Radiology. 2018; 288: 64-72
        • Ibrahimi P.
        • Jashari F.
        • Nicoll R.
        • Bajraktari G.
        • Wester P.
        • Henein M.Y.
        Coronary and carotid atherosclerosis: how useful is the imaging?.
        Atherosclerosis. 2013; 231: 323-333
        • Carter H.H.
        • Atkinson C.L.
        • Heinonen I.H.
        • Haynes A.
        • Robey E.
        • Smith K.J.
        • et al.
        Evidence for shear stress-mediated dilation of the internal carotid artery in humans.
        Hypertension. 2016; 68: 1217-1224
        • Coenen A.
        • Kim Y.H.
        • 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; 11e007217
        • Diaz-Zamudio M.
        • Dey D.
        • Schuhbaeck A.
        • Nakazato R.
        • Gransar H.
        • Slomka P.J.
        • et al.
        Automated quantitative plaque burden from coronary CT angiography noninvasively predicts hemodynamic significance by using fractional flow Reserve in Intermediate Coronary Lesions.
        Radiology. 2015; 276: 408-415
        • Kolossvary M.
        • Karady J.
        • Szilveszter B.
        • Kitslaar P.
        • Hoffmann U.
        • Merkely B.
        • et al.
        Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign.
        Circ. Cardiovasc. Imaging. 2017; 10
        • Dey D.
        • Commandeur F.
        Radiomics to identify high-risk atherosclerotic plaque from computed tomography: the power of quantification.
        Circ. Cardiovasc. Imaging. 2017; 10
        • Dey D.
        • Schepis T.
        • Marwan M.
        • Slomka P.J.
        • Berman D.S.
        • Achenbach S.
        Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US.
        Radiology. 2010; 257: 516-522
        • Tesche C.
        • Otani K.
        • De Cecco C.N.
        • 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.
        JACC Cardiovasc. Imaging. 2020; 13: 760-770