Sang-Eun Lee1,2, Ji Min Sung1,2, Asim Rizvi3, Fay Y Lin3, Amit Kumar3, Martin Hadamitzky4, Yong-Jin Kim5, Edoardo Conte6, Daniele Andreini, Gianluca Pontone6, Matthew J Budoff7, Ilan Gottlieb8, Byoung Kwon Lee9, Eun Ju Chun10, Filippo Cademartiri11, Erica Maffei12, Hugo Marques13, Jonathon A Leipsic14, Sanghoon Shin15, Jung Hyun Choi16, Kavitha Chinnaiyan17, Gilbert Raff17, Renu Virmani18, Habib Samady19, Peter H Stone20, Daniel S Berman21, Jagat Narula22, Leslee J Shaw19, Jeroen J Bax23, James K Min3, Hyuk-Jae Chang24,2. 1. Division of Cardiology, Severance Cardiovascular Hospital (H.-J.C., S.-E.L., J.M.S.). 2. Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center (H.-J.C., S.-E.L., J.M.S.). 3. Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and Weill Cornell Medical College (A.K., A.R., F.Y.L., J.K.M.). 4. Department of Radiology and Nuclear Medicine, German Heart Center Munich (M.H.). 5. Seoul National University College of Medicine, Seoul National University Hospital, South Korea (Y.-J.K.). 6. Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico Milan, Italy (E.C., G.A., G.P.). 7. Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA (M.J.B.). 8. Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil (I.G.). 9. Gangnam Severance Hospital (B.K.L.), Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. 10. Seoul National University Bundang Hospital, South Korea (E.J.C.). 11. Cardiovascular Imaging Center, SDN Foundation IRCCS, Naples, Italy (F.C.). 12. Department of Radiology, Area Vasta 1/Azienda Sanitaria Unica Regionale Marche Marche, Urbino, Italy (E.M.). 13. Hospital da Luz, Lisbon, Portugal (H.M.). 14. Department of Radiology, St Paul's Hospital, University of British Columbia, Vancouver, Canada (J.A.L.). 15. National Health Insurance Service Ilsan Hospital, South Korea (S.S.). 16. Busan University Hospital, South Korea (J.H.C.). 17. Department of Cardiology, William Beaumont Hospital, Royal Oak, MI (G.R., K.C.). 18. Department of Pathology, CVPath Institute, Gaithersburg, MD (R.V.). 19. Division of Cardiology, Emory University School of Medicine, Atlanta, GA (H.S., L.J.S.). 20. Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA (P.H.S.). 21. Department of Imaging, Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (D.S.B.). 22. Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, Zena and Michael A. Wiener Cardiovascular Institute, and Marie-Josee and Henry R. Kravis Center for Cardiovascular Health, New York, NY (J.N.). 23. Department of Cardiology, Leiden University Medical Center, The Netherlands (J.J.B.). 24. Division of Cardiology, Severance Cardiovascular Hospital (H.-J.C., S.-E.L., J.M.S.) hjchang@yuhs.ac.
Abstract
BACKGROUND: Diagnosis of coronary artery disease and management strategies have relied solely on the presence of diameter stenosis ≥50%. We assessed whether direct quantification of plaque burden (PB) and plaque characteristics assessed by coronary computed tomography angiography could provide additional value in terms of predicting rapid plaque progression. METHODS AND RESULTS: From a 13-center, 7-country prospective observational registry, 1345 patients (60.4±9.4 years old; 57.1% male) who underwent repeated coronary computed tomography angiography >2 years apart were enrolled. For conventional angiographic analysis, the presence of stenosis ≥50%, number of vessel involved, segment involvement score, and the presence of high-risk plaque feature were determined. For quantitative analyses, PB and annual change in PB (△PB/y) in the entire coronary tree were assessed. Clinical outcomes (cardiac death, nonfatal myocardial infarction, and coronary revascularization) were recorded. Rapid progressors, defined as a patient with ≥median value of △PB/y (0.33%/y), were older, more frequently male, and had more clinical risk factors than nonrapid progressors (all P<0.05). After risk adjustment, addition of baseline PB improved prediction of rapid progression to each angiographic assessment of coronary artery disease, and the presence of high-risk plaque further improved the predictive performance (all P<0.001). For prediction of adverse outcomes, adding both baseline PB and △PB/y showed best predictive performance (C statistics, 0.763; P<0.001). CONCLUSIONS: Direct quantification of atherosclerotic PB in addition to conventional angiographic assessment of coronary artery disease might be beneficial for improving risk stratification of coronary artery disease. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02803411.
BACKGROUND: Diagnosis of coronary artery disease and management strategies have relied solely on the presence of diameter stenosis ≥50%. We assessed whether direct quantification of plaque burden (PB) and plaque characteristics assessed by coronary computed tomography angiography could provide additional value in terms of predicting rapid plaque progression. METHODS AND RESULTS: From a 13-center, 7-country prospective observational registry, 1345 patients (60.4±9.4 years old; 57.1% male) who underwent repeated coronary computed tomography angiography >2 years apart were enrolled. For conventional angiographic analysis, the presence of stenosis ≥50%, number of vessel involved, segment involvement score, and the presence of high-risk plaque feature were determined. For quantitative analyses, PB and annual change in PB (△PB/y) in the entire coronary tree were assessed. Clinical outcomes (cardiac death, nonfatal myocardial infarction, and coronary revascularization) were recorded. Rapid progressors, defined as a patient with ≥median value of △PB/y (0.33%/y), were older, more frequently male, and had more clinical risk factors than nonrapid progressors (all P<0.05). After risk adjustment, addition of baseline PB improved prediction of rapid progression to each angiographic assessment of coronary artery disease, and the presence of high-risk plaque further improved the predictive performance (all P<0.001). For prediction of adverse outcomes, adding both baseline PB and △PB/y showed best predictive performance (C statistics, 0.763; P<0.001). CONCLUSIONS: Direct quantification of atheroscleroticPB in addition to conventional angiographic assessment of coronary artery disease might be beneficial for improving risk stratification of coronary artery disease. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02803411.
Authors: Andrew Lin; Márton Kolossváry; Sebastien Cadet; Priscilla McElhinney; Markus Goeller; Donghee Han; Jeremy Yuvaraj; Nitesh Nerlekar; Piotr J Slomka; Mohamed Marwan; Stephen J Nicholls; Stephan Achenbach; Pál Maurovich-Horvat; Dennis T L Wong; Damini Dey Journal: JACC Cardiovasc Imaging Date: 2022-01-12
Authors: Donghee Han; Kranthi K Kolli; Subhi J Al'Aref; Lohendran Baskaran; Alexander R van Rosendael; Heidi Gransar; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Kavitha Chinnaiyan; Jung Hyun Choi; Edoardo Conte; Hugo Marques; Pedro de Araújo Gonçalves; Ilan Gottlieb; Martin Hadamitzky; Jonathon A Leipsic; Erica Maffei; Gianluca Pontone; Gilbert L Raff; Sangshoon Shin; Yong-Jin Kim; Byoung Kwon Lee; Eun Ju Chun; Ji Min Sung; Sang-Eun Lee; Renu Virmani; Habib Samady; Peter Stone; Jagat Narula; Daniel S Berman; Jeroen J Bax; Leslee J Shaw; Fay Y Lin; James K Min; Hyuk-Jae Chang Journal: J Am Heart Assoc Date: 2020-02-22 Impact factor: 5.501
Authors: Qing Cao; Alexander Broersen; Pieter H Kitslaar; Mingyuan Yuan; Boudewijn P F Lelieveldt; Jouke Dijkstra Journal: Med Phys Date: 2020-01-20 Impact factor: 4.071