James K Min1, Troy M Labounty2, Millie J Gomez2, Stephan Achenbach3, Mouaz Al-Mallah4, Matthew J Budoff5, Filippo Cademartiri6, Tracy Q Callister7, Hyuk-Jae Chang8, Victor Cheng2, Kavitha M Chinnaiyan9, Benjamin Chow10, Ricardo Cury11, Augustin Delago12, Allison Dunning13, Gudrun Feuchtner14, Martin Hadamitzky15, Jorg Hausleiter16, Philipp Kaufmann17, Yong-Jin Kim18, Jonathon Leipsic19, Fay Y Lin20, Erica Maffei6, Gilbert Raff9, Leslee J Shaw21, Todd C Villines22, Daniel S Berman2. 1. Department of Radiology, Weill Cornell Medical College and The NewYork-Presbyterian Hospital, New York, NY, USA. Electronic address: james.min@cshs.org. 2. Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 3. Department of Medicine, University of Erlangen, Erlangen, Germany. 4. Department of Medicine, Wayne State University, Henry Ford Hospital, Detroit, MI, USA. 5. Department of Medicine, Harbor UCLA Medical Center, Los Angeles, CA, USA. 6. Department of Radiology, Giovanni XXIII Hospital, Monastier, Treviso, Italy; Department of Radiology, Erasmus Medical Center, Rotterdam, Netherlands. 7. Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. 8. Division of Cardiology, Severance Cardiovascular Hospital, Seoul, South Korea. 9. William Beaumont Hospital, Royal Oak, MI, USA. 10. Department of Medicine and Radiology, University of Ottawa, Ontario, Canada. 11. Baptist Cardiac and Vascular Institute, Miami, FL, USA. 12. Capitol Cardiology Associates, Albany, NY, USA. 13. Department of Public Health, Weill Cornell Medical College, New York, NY, USA. 14. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. 15. University of Munich, Munich, Germany. 16. Cardiovascular Medical Group, Los Angeles, CA, USA. 17. University Hospital, Zurich, Switzerland. 18. Seoul National University Hospital, Seoul, South Korea. 19. Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. 20. Department of Medicine, Weill Cornell Medical College, New York, NY, USA. 21. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA. 22. Walter Reed Medical Center, Bethesda, MD, USA.
Abstract
BACKGROUND: Coronary artery disease (CAD) diagnosis by coronary computed tomographic angiography (CCTA) is useful for identification of symptomatic diabetic individuals at heightened risk for death. Whether CCTA-detected CAD enables improved risk assessment of asymptomatic diabetic individuals beyond clinical risk factors and coronary artery calcium scoring (CACS) remains unexplored. METHODS: From a prospective 12-center international registry of 27,125 individuals undergoing CCTA, we identified 400 asymptomatic diabetic individuals without known CAD. Coronary stenosis by CCTA was graded as 0%, 1-49%, 50-69%, and ≥70%. CAD was judged on a per-patient, per-vessel and per-segment basis as maximal stenosis severity, number of vessels with ≥50% stenosis, and coronary segments weighted for stenosis severity (segment stenosis score), respectively. We assessed major adverse cardiovascular events (MACE) - inclusive of mortality, nonfatal myocardial infarction (MI), and late target vessel revascularization ≥90 days (REV) - and evaluated the incremental utility of CCTA for risk prediction, discrimination and reclassification. RESULTS: Mean age was 60.4 ± 9.9 years; 65.0% were male. At a mean follow-up 2.4 ± 1.1 years, 33 MACE occurred (13 deaths, 8 MI, 12 REV) [8.25%; annualized rate 3.4%]. By univariate analysis, per-patient maximal stenosis [hazards ratio (HR) 2.24 per stenosis grade, 95% confidence interval (CI) 1.61-3.10, p < 0.001], increasing numbers of obstructive vessels (HR 2.30 per vessel, 95% CI 1.75-3.03, p < 0.001) and segment stenosis score (HR 1.14 per segment, 95% CI 1.09-1.19, p < 0.001) were associated with increased MACE. After adjustment for CAD risk factors and CACS, maximal stenosis (HR 1.80 per grade, 95% CI 1.18-2.75, p = 0.006), number of obstructive vessels (HR 1.85 per vessel, 95% CI 1.29-2.65, p < 0.001) and segment stenosis score (HR 1.11 per segment, 95% CI 1.05-1.18, p < 0.001) were associated with increased risk of MACE. Beyond age, gender and CACS (C-index 0.64), CCTA improved discrimination by maximal stenosis, number of obstructive vessels and segment stenosis score (C-index 0.77, 0.77 and 0.78, respectively). Similarly, CCTA findings improved risk reclassification by per-patient maximal stenosis [integrated discrimination improvement (IDI) index 0.03, p = 0.03] and number of obstructive vessels (IDI index 0.06, p = 0.002), and by trend for segment stenosis score (IDI 0.03, p = 0.06). CONCLUSION: For asymptomatic diabetic individuals, CCTA measures of CAD severity confer incremental risk prediction, discrimination and reclassification on a per-patient, per-vessel and per-segment basis.
BACKGROUND:Coronary artery disease (CAD) diagnosis by coronary computed tomographic angiography (CCTA) is useful for identification of symptomatic diabetic individuals at heightened risk for death. Whether CCTA-detected CAD enables improved risk assessment of asymptomatic diabetic individuals beyond clinical risk factors and coronary artery calcium scoring (CACS) remains unexplored. METHODS: From a prospective 12-center international registry of 27,125 individuals undergoing CCTA, we identified 400 asymptomatic diabetic individuals without known CAD. Coronary stenosis by CCTA was graded as 0%, 1-49%, 50-69%, and ≥70%. CAD was judged on a per-patient, per-vessel and per-segment basis as maximal stenosis severity, number of vessels with ≥50% stenosis, and coronary segments weighted for stenosis severity (segment stenosis score), respectively. We assessed major adverse cardiovascular events (MACE) - inclusive of mortality, nonfatal myocardial infarction (MI), and late target vessel revascularization ≥90 days (REV) - and evaluated the incremental utility of CCTA for risk prediction, discrimination and reclassification. RESULTS: Mean age was 60.4 ± 9.9 years; 65.0% were male. At a mean follow-up 2.4 ± 1.1 years, 33 MACE occurred (13 deaths, 8 MI, 12 REV) [8.25%; annualized rate 3.4%]. By univariate analysis, per-patient maximal stenosis [hazards ratio (HR) 2.24 per stenosis grade, 95% confidence interval (CI) 1.61-3.10, p < 0.001], increasing numbers of obstructive vessels (HR 2.30 per vessel, 95% CI 1.75-3.03, p < 0.001) and segment stenosis score (HR 1.14 per segment, 95% CI 1.09-1.19, p < 0.001) were associated with increased MACE. After adjustment for CAD risk factors and CACS, maximal stenosis (HR 1.80 per grade, 95% CI 1.18-2.75, p = 0.006), number of obstructive vessels (HR 1.85 per vessel, 95% CI 1.29-2.65, p < 0.001) and segment stenosis score (HR 1.11 per segment, 95% CI 1.05-1.18, p < 0.001) were associated with increased risk of MACE. Beyond age, gender and CACS (C-index 0.64), CCTA improved discrimination by maximal stenosis, number of obstructive vessels and segment stenosis score (C-index 0.77, 0.77 and 0.78, respectively). Similarly, CCTA findings improved risk reclassification by per-patient maximal stenosis [integrated discrimination improvement (IDI) index 0.03, p = 0.03] and number of obstructive vessels (IDI index 0.06, p = 0.002), and by trend for segment stenosis score (IDI 0.03, p = 0.06). CONCLUSION: For asymptomatic diabetic individuals, CCTA measures of CAD severity confer incremental risk prediction, discrimination and reclassification on a per-patient, per-vessel and per-segment basis.
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