Joo Myung Lee1, Gilwoo Choi2, Bon-Kwon Koo3, Doyeon Hwang4, Jonghanne Park4, Jinlong Zhang4, Kyung-Jin Kim4, Yaliang Tong5, Hyun Jin Kim2, Leo Grady2, Joon-Hyung Doh6, Chang-Wook Nam7, Eun-Seok Shin8, Young-Seok Cho9, Su-Yeon Choi10, Eun Ju Chun11, Jin-Ho Choi1, Bjarne L Nørgaard12, Evald H Christiansen12, Koen Niemen13, Hiromasa Otake14, Martin Penicka15, Bernard de Bruyne15, Takashi Kubo16, Takashi Akasaka16, Jagat Narula17, Pamela S Douglas18, Charles A Taylor19, Hyo-Soo Kim4. 1. Department of Internal Medicine and Cardiovascular Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 2. HeartFlow, Inc., Redwood City, California. 3. Department of Medicine, Seoul National University Hospital, Seoul, South Korea; Institute on Aging, Seoul National University, Seoul, Korea. Electronic address: bkkoo@snu.ac.kr. 4. Department of Medicine, Seoul National University Hospital, Seoul, South Korea. 5. Department of Cardiology, China-Japan Union Hospital of Jilin University, Changchun, China. 6. Department of Medicine, Inje University Ilsan Paik Hospital, Goyang, South Korea. 7. Department of Medicine, Keimyung University Dongsan Medical Center, Daegu, South Korea. 8. Department of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea. 9. Department of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea. 10. Department of Internal Medicine, Seoul National University Healthcare System Gangnam Center, Seoul National University College of Medicine, Seoul, South Korea. 11. Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea. 12. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark. 13. Erasmus University Medical Center, Rotterdam, the Netherlands; Cardiovascular Institute, Stanford University, School of Medicine, Stanford, California. 14. Department of Internal Medicine, Division of Cardiovascular and Respiratory Medicine, Kobe University Graduate School of Medicine, Kobe, Japan. 15. Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium. 16. Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan. 17. Icahn School of Medicine at Mount Sinai Hospital, New York, New York. 18. Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina. 19. HeartFlow, Inc., Redwood City, California; Department of Bioengineering, Stanford University, Stanford, California.
Abstract
OBJECTIVES: The authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). BACKGROUND: ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. METHODS: Seventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA) acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC) was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, △FFRCT, WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS] and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). RESULTS: The culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001) and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001) than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFRCT and higher △FFRCT, WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index] 0.789 vs. 0.747; p = 0.014) and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001) than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001) and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). CONCLUSIONS: Noninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775).
OBJECTIVES: The authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). BACKGROUND: ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. METHODS: Seventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA) acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC) was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, △FFRCT, WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS] and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). RESULTS: The culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001) and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001) than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFRCT and higher △FFRCT, WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index] 0.789 vs. 0.747; p = 0.014) and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001) than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001) and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). CONCLUSIONS: Noninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775).
Authors: Habib Samady; David S Molony; Ahmet U Coskun; Anubodh S Varshney; Bernard De Bruyne; Peter H Stone Journal: J Cardiovasc Comput Tomogr Date: 2019-12-04
Authors: Jagat Narula; Y Chandrashekhar; Amir Ahmadi; Suhny Abbara; Daniel S Berman; Ron Blankstein; Jonathon Leipsic; David Newby; Edward D Nicol; Koen Nieman; Leslee Shaw; Todd C Villines; Michelle Williams; Harvey S Hecht Journal: J Cardiovasc Comput Tomogr Date: 2020-11-20
Authors: Anubodh S Varshney; Ahmet U Coskun; Gerasimos Siasos; Charles C Maynard; Zhongyue Pu; Kevin J Croce; Nicholas V Cefalo; Michelle A Cormier; Dimitris Fotiadis; Kostas Stefanou; Michail I Papafaklis; Lampros Michalis; Stacie VanOosterhout; Abbey Mulder; Ryan D Madder; Peter H Stone Journal: Atherosclerosis Date: 2020-12-28 Impact factor: 5.162
Authors: Khaled M Abdelrahman; Marcus Y Chen; Amit K Dey; Renu Virmani; Aloke V Finn; Ramzi Y Khamis; Andrew D Choi; James K Min; Michelle C Williams; Andrew J Buckler; Charles A Taylor; Campbell Rogers; Habib Samady; Charalambos Antoniades; Leslee J Shaw; Matthew J Budoff; Udo Hoffmann; Ron Blankstein; Jagat Narula; Nehal N Mehta Journal: J Am Coll Cardiol Date: 2020-09-08 Impact factor: 24.094