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CCTA in the diagnosis of coronary artery disease

  • Cardiac radiology
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Abstract

The world of cardiac imaging is proposing to physicians an ever-increasing spectrum of options and tools with the disadvantages of patients presently submitted to multiple, sequential, time-consuming, and costly diagnostic procedures and tests, sometimes with contradicting results. In the last two decades, the CCTA has evolved into a valuable diagnostic test in today’s patient care, changing the official existing guidelines and clinical practice with a pivotal role to exclude significant CAD, in the referral of patients to the Cath-Lab, in the follow-up after coronary revascularization, and finally in the cardiovascular risk stratification.

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References

  1. Virani SS, Alonso A, Benjamin EJ et al (2020) heart disease and stroke statistics-2020 update: a report from the American heart association. Circulation 14:e139–e596

    Google Scholar 

  2. Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477

    Google Scholar 

  3. La Russa R, Catalano C, Di Sanzo M et al (2019) Postmortem computed tomography angiography (PMCTA) and traditional autopsy in cases of sudden cardiac death due to coronary artery disease: a systematic review and meta-analysis. Radiol Med 124:109–117

    Google Scholar 

  4. Kannel WB, D’Agostino RB, Sullivan L, Wilson PW (2004) Concept and usefulness of cardiovascular risk profiles. Am Heart J 148:16–26

    Google Scholar 

  5. Lee UW, Ahn S, Shin YS et al (2020) Comparison of the CAD consortium and updated Diamond-Forrester scores for predicting obstructive coronary artery disease. Am J Emerg Med [Epub ahead of print]

  6. Patel MR, Peterson ED, Dai D et al (2010) Low diagnostic yield of elective coronary angiography. N Engl J Med 362:886–895

    CAS  Google Scholar 

  7. Bittencourt MS, Hulten E, Polonsky TS et al (2016) European society of cardiology-recommended coronary artery disease consortium pretest probability scores more accurately predict obstructive coronary disease and cardiovascular events than the diamond and Forrester score: the partners registry. CAD Consortium Scores. Circulation 134:201–211

    Google Scholar 

  8. Agliata G, Schicchi N, Agostini A et al (2019) Radiation exposure related to cardiovascular CT examination: comparison between conventional 64-MDCT and third-generation dual-source MDCT. Radiol Med 124:753–761

    Google Scholar 

  9. Pundziute G, Schuijf JD, Jukema JW et al (2007) Prognostic value of multislice computed tomography coronary angiography in patients with known or suspected coronary artery disease. J Am Coll Cardiol 49:62–70

    Google Scholar 

  10. Feuchtner G, Kerber J, Burghard P et al (2017) The high-risk criteria low-attenuation plaque < 60 HU and the napkin-ring sign are the most powerful predictors of MACE: a long-term follow-up study. Eur Heart J Cardiovasc Imaging 18:772–779

    Google Scholar 

  11. Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336

    CAS  Google Scholar 

  12. Budoff MJ, Dowe D, Jollis JG et al (2008) Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 52:1724–1732

    Google Scholar 

  13. Meijboom WB, Meijs MF, Schuijf JD et al (2008) Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. J Am Coll Cardiol 52:2135–2144

    Google Scholar 

  14. Marano R, De Cobelli F, Floriani I et al (2009) Italian multicenter, prospective study to evaluate the negative predictive value of 16- and 64-slice MDCT imaging in patients scheduled for coronary angiography (NIMISCAD-Non Invasive Multicenter Italian Study for Coronary Artery Disease). Eur Radiol 19:1114–1123

    Google Scholar 

  15. Marano R, Savino G, Merlino B et al (2013) MDCT coronary angiography- postprocessing, reading, and reporting: last but not least. Acta Radiol 54:249–258

    Google Scholar 

  16. Cheng V, Gutstein A, Wolak A et al (2008) Moving beyond binary grading of coronary arterial stenoses on coronary computed tomographic angiography: insights for the imager and referring clinician. JACC Cardiovasc Imaging 1:460–471

    Google Scholar 

  17. Cury RC, Abbara S, Achenbach S et al (2016) CAD-RADS coronary artery disease e reporting and data system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr 10:269–281

    Google Scholar 

  18. Nallamothu BK, Spertus JA, Lansky AJ et al (2013) Comparison of clinical interpretation with visual assessment and quantitative coronary angiography in patients undergoing percutaneous coronary intervention in contemporary practice: the Assessing Angiography (A2) project. Circulation 127:1793–1800

    Google Scholar 

  19. Motoyama S, Sarai M, Harigaya H et al (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54:49–57

    Google Scholar 

  20. Tanaka A, Shimada K, Yoshida K et al (2008) Non-invasive assessment of plaque rupture by 64-slice multidetector computed tomography–comparison with intravascular ultrasound. Circ J 72:1276–1281

    Google Scholar 

  21. Dalager MG, Bttcher M, Andersen G et al (2011) Impact of luminal density on plaque classification by CT coronary angiography. Int J Cardiovasc Imaging 27:593–600

    Google Scholar 

  22. SCOT-HEART investigators (2015) CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet 385:2383–2391

    Google Scholar 

  23. Investigators SCOT-HEART (2018) Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med 379:924–933

    Google Scholar 

  24. Shaw LJ, Hausleiter J, Achenbach S et al (2012) Coronary computed tomographic angiography as a gatekeeper to invasive diagnostic and surgical procedures: results from the multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: an International Multicenter) registry. J Am Coll Cardiol 60:2103–2114

    Google Scholar 

  25. Douglas PS, Pontone G, Hlatky MA et al (2015) Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies versus usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study. Eur Heart J 36:3359–3367

    CAS  Google Scholar 

  26. Dewey M, Rief M, Martus P et al (2016) Evaluation of computed tomography in patients with atypical angina or chest pain clinically referred for invasive coronary angiography: randomised controlled trial. BMJ 355:i5441

    Google Scholar 

  27. Chang HJ, Lin FY, Gebow D et al (2019) Selective referral using CCTA versus direct referral for individuals referred to invasive coronary angiography for suspected CAD: a randomized, controlled, open-label trial. JACC Cardiovas Imaging 12:1303–1312

    Google Scholar 

  28. https://www.nice.org.uk/guidance/cg95

  29. Koo BK, Erglis A, Doh JH et al (2011) 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 58:1989–1997

    Google Scholar 

  30. Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245

    CAS  Google Scholar 

  31. Nørgaard BL, Leipsic J, Gaur S et al (2014) 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 63:1145–1155

    Google Scholar 

  32. Bamberg F, Becker A, Schwarz F et al (2011) Detection of hemodynamically significant coronary artery stenosis: incremental diagnostic value of dynamic CT-based myocardial perfusion imaging. Radiology 260:689–698

    Google Scholar 

  33. Gonzalez JA, Lipinski MJ, Flors L, Shaw PW, Kramer CM, Salerno M (2015) Meta-analysis of diagnostic performance of coronary computed tomography angiography, computed tomography perfusion, and computed tomography-fractional flow reserve in functional myocardial ischemia assessment versus invasive fractional flow reserve. Am J Cardiol 116:1469–1478

    Google Scholar 

  34. Li S, Tang X, Peng L, Luo Y, Dong R, Liu J (2015) The diagnostic performance of CT-derived fractional flow reserve for evaluation of myocardial ischaemia confirmed by invasive fractional flow reserve: a meta-analysis. Clin Radiol 70:476–486

    CAS  Google Scholar 

  35. Tonino PA, Fearon WF, De Bruyne B et al (2010) Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 55:2816–2821

    Google Scholar 

  36. Danad I, Szymonifka J, Twisk JWR et al (2017) Diagnostic performance of cardiac imaging methods to diagnose ischaemia-causing coronary artery disease when directly compared with fractional flow reserve as a reference standard: a meta-analysis. Eur Heart J 38:991–998

    Google Scholar 

  37. Pijls NH, Sels JW (2012) Functional measurement of coronary stenosis. J Am Coll Cardiol 59:1045–1057

    Google Scholar 

  38. Nørgaard BL, Hjort J, Gaur S, Hansson N et al (2017) Clinical use of coronary CTA-derived FFR for decision-making in stable CAD. JACC Cardiovasc Imaging 10:541–550

    Google Scholar 

  39. Fairbairn TA, Nieman K, Akasaka T et al (2018) Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE Registry. Eur Heart J 39:3701–3711

    Google Scholar 

  40. Ihdayhid AR, Norgaard BL, Gaur S et al (2019) Prognostic value and risk continuum of noninvasive fractional flow reserve derived from coronary CT angiography. Radiology 292:343–351

    Google Scholar 

  41. Nørgaard BL, Gaur S, Leipsic J et al (2015) Influence of coronary calcification on the diagnostic performance of CT angiography derived FFR in coronary artery disease: a substudy of the NXT trial. JACC Cardiovasc Imaging 8:1045–1055

    Google Scholar 

  42. Packard RR, Li D, Budoff MJ, Karlsberg RP et al (2017) Fractional flow reserve by computerized tomography and subsequent coronary revascularization. Eur Heart J Cardiovasc Imaging 18:145–152

    Google Scholar 

  43. Hlatky MA, Saxena A, Koo BK, Erglis A, Zarins CK, Min JK et al (2013) Projected costs and consequences of computed tomography-determined fractional flow reserve. Clin Cardiol 36:743–748

    Google Scholar 

  44. Kimura T, Shiomi H, Kuribayashi S et al (2015) Cost analysis of non-invasive fractional flow reserve derived from coronary computed tomographic angiography in Japan. Cardiovasc Interv Ther 30:38–44

    Google Scholar 

  45. Falk E, Shah PK, Fuster V (1995) Coronary plaque disruption. Circulation 92:657–671

    CAS  Google Scholar 

  46. Little WC, Constantinescu M, Applegate RJ et al (1988) Can coronary angiography predict the site of a subsequent myocardial infarction in patients with mild-to-moderate coronary artery disease? Circulation 78:1157–1166

    CAS  Google Scholar 

  47. Grundy SM, Cleeman JI, Merz CN et al (2004) Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 110:227–239

    Google Scholar 

  48. Karim R, Hodis HN, Detrano R, Liu CR, Liu CH, Mack WJ (2008) Relation of Framingham risk score to subclinical atherosclerosis evaluated across three arterial sites. Am J Cardiol 102:825–830

    Google Scholar 

  49. Ridker PM, Buring JE, Rifai N, Cook NR (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 297:611–619

    CAS  Google Scholar 

  50. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR (2008) C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 118:2243–2251

    CAS  Google Scholar 

  51. De Backer G, Ambrosioni E, Borch-Johnsen K et al (2004) European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force of European and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of eight societies and by invited experts). Atherosclerosis 173:381–391

    Google Scholar 

  52. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (2001) Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285:2486–2497

    Google Scholar 

  53. Adabag AS, Luepker RV, Roger VL, Gersh BJ (2010) Sudden cardiac death: epidemiology and risk factors. Nat Rev Cardiol 7:216–225

    Google Scholar 

  54. Narula J, Garg P, Achenbach S, Motoyama S, Virmani R, Strauss HW (2008) Arithmetic of vulnerable plaques for noninvasive imaging. Nat Clin Pract Cardiovasc Med 5(Suppl 2):S2–S10

    Google Scholar 

  55. Naghavi M, Libby P, Falk E, Casscells SW et al (2003) From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: part I. Circulation 108:1664–1672

    Google Scholar 

  56. Jang IK, Tearney GJ, MacNeill B et al (2005) In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 111:1551–1555

    Google Scholar 

  57. Nakazato R, Otake H, Konishi A et al (2015) Atherosclerotic plaque characterization by CT angiography for identification of high-risk coronary artery lesions: a comparison to optical coherence tomography. Eur Heart J Cardiovasc Imaging 16:373–379

    Google Scholar 

  58. Arnett DK, Blumenthal RS, Albert MA et al (2019) 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 140:e563–e595

    Google Scholar 

  59. Di Cesare E, Patriarca L, Panebianco L et al (2018) Coronary computed tomography angiography in the evaluation of intermediate risk asymptomatic individuals. Radiol Med 123:686–694

    Google Scholar 

  60. Gaur S, Øvrehus KA, Dey D et al (2016) Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J 37:1220–1227

    Google Scholar 

  61. Øvrehus KA, Gaur S, Leipsic J et al (2018) CT-based total vessel plaque analyses improves prediction of hemodynamic significance lesions as assessed by fractional flow reserve in patients with stable angina pectoris. J Cardiovasc Comput Tomogr 12:344–349

    Google Scholar 

  62. Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28:2655–2664

    Google Scholar 

  63. Kontos MC, Jesse RL (2000) Evaluation of the emergency department chest pain patient. Am J Cardiol 85:32B–39B

    CAS  Google Scholar 

  64. Taylor AJ, Cerqueira M, Hodgson JM et al (2010) ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriateuse criteria for cardiac computed tomography. a report of the American college of cardiology foundation appropriate use criteria task force, the society of cardiovascular computed tomography, the American college of radiology, the American heart association, the American society of echocardiography, the American society of nuclear cardiology, the north American society for cardiovascular imaging, the society for Cardiovascular Angiography and interventions, and the society for cardiovascular magnetic resonance. Circulation 122:e525–e555

    Google Scholar 

  65. Rybicki FJ, Udelson JE, Peacock WF et al (2016) 2015 ACR/ACC/AHA/AATS/ACEP/ASNC/NASCI/SAEM/SCCT/SCMR/SCPC/SNMMI/STR/STS appropriate utilization of cardiovascular imaging in emergency department patients with chest pain: a joint document of the American College of Radiology Appropriateness Criteria Committee and the American College of Cardiology Appropriate Use Criteria TaskForce. J Am Coll Cardiol 67:853–879

    Google Scholar 

  66. Roffi M, Patrono C, Collet JP et al (2016) 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC). Eur Heart J 37:267–315

    CAS  Google Scholar 

  67. Hoffmann U, Bamberg F, Chae CU et al (2009) Coronary Computed Tomography Angiography For Early Triage of Patients with Acute Chest Pain - The Rule Out Myocardial Infarction Using Computer Assisted Tomography (ROMICAT) Trial. J Am Coll Cardiol 53:1642–1650

    Google Scholar 

  68. Goldstein JA, Chinnaiyan KM, Abidov A et al (2011) The CT-STAT (Coronary Computed Tomographic Angiography for Systematic Triage of Acute Chest Pain Patients to Treatment) trial. J Am Coll Cardiol 58:1414–1422

    Google Scholar 

  69. Litt HI, Gatsonis C, Snyder B et al (2012) CT angiography for safe discharge of patients with possible acute coronary syndromes. N Engl J Med 366:1393–1403

    CAS  Google Scholar 

  70. Hoffmann U, Truong QA, Schoenfeld DA et al (2012) Coronary CT angiography versus standard evaluation in acute chest pain. N Engl J Med 367:299–308

    CAS  Google Scholar 

  71. Ferencik M, Liu T, Mayrhofer T et al (2015) highly sensitive troponin I followed by advanced coronary artery disease assessment using computed tomography angiography improves acute coronary syndrome risk stratification accuracy and work-up in acute chest pain patients: results from ROMICAT II trial. JACC Cardiovasc Imaging 8:1272–1281

    Google Scholar 

  72. Levsky JM, Spevack DM, Travin MI et al (2015) Coronary computed tomography angiography versus radionuclide myocardial perfusion imaging in patients with chest pain admitted to telemetry: a randomized trial. Ann Intern Med 163:174–183

    Google Scholar 

  73. Linde JJ, Hove JD, Sørgaard M et al (2015) Long-term clinical impact of coronary CT angiography in patients with recent acute-onset chest pain: the randomized controlled CATCH trial. JACC Cardiovasc Imaging 8:1404–1413

    Google Scholar 

  74. Haaf P, Reichlin T, Twerenbold R et al (2014) Risk stratification in patients with acute chest pain using three high-sensitivity cardiac troponin assays. Eur Heart J 35:365–375

    CAS  Google Scholar 

  75. Dedic A, Lubbers MM, Schaap J et al (2016) Coronary CT angiography for suspected ACS in the era of high-sensitivity troponins: randomized multicenter study. J Am Coll Cardiol 67:16–26

    Google Scholar 

  76. Dai T, Wang HuPF (2018) Diagnostic performance of computed tomography angiography in the detection of coronary artery in-stent restenosis: evidence from an updated meta-analysis. Eur Radiol 28:1373–1382

    Google Scholar 

  77. Marano R, Liguori C, Rinaldi P et al (2007) Coronary artery bypass grafts and MDCT imaging: what to know and what to look for. Eur Radiol 17:3166–3178

    Google Scholar 

  78. Jungmann F, Emrich T, Mildenberger P et al (2018) Multidetector computed tomography angiography (MD-CTA) of coronary artery bypass grafts—update 2017. Rofo 190:237–249

    Google Scholar 

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  1. Department of Radiological and Hematological Sciences, Section of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, L.go Agostino Gemelli 8, 00168, Rome, Italy

    Riccardo Marano, Giuseppe Rovere, Giancarlo Savino, Francesco Ciriaco Flammia, Maria Rachele Pia Carafa, Lorenzo Steri, Biagio Merlino & Luigi Natale

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  1. Riccardo Marano
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  2. Giuseppe Rovere
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  3. Giancarlo Savino
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  4. Francesco Ciriaco Flammia
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Correspondence to Riccardo Marano.

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Marano, R., Rovere, G., Savino, G. et al. CCTA in the diagnosis of coronary artery disease. Radiol med 125, 1102–1113 (2020). https://doi.org/10.1007/s11547-020-01283-y

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