James K Min1, Allison Dunning2, Heidi Gransar3, Stephan Achenbach4, Fay Y Lin5, Mouaz Al-Mallah6, Matthew J Budoff7, Tracy Q Callister8, Hyuk-Jae Chang9, Filippo Cademartiri10, Erica Maffei10, Kavitha Chinnaiyan11, Benjamin J W Chow12, Ralph D'Agostino13, Augustin DeLago14, John Friedman3, Martin Hadamitzky15, Joerg Hausleiter16, Sean W Hayes3, Philipp Kaufmann17, Gilbert L Raff11, Leslee J Shaw18, Louise Thomson3, Todd Villines19, Ricardo C Cury20, Gudrun Feuchtner21, Yong-Jin Kim22, Jonathon Leipsic23, Hugo Marques24, Daniel S Berman3, Michael Pencina25. 1. Department of Radiology, Weill Cornell Medical College and the New York-Presbyterian Hospital, New York. Electronic address: jkm2001@med.cornell.edu. 2. Department of Public Health, Weill Cornell Medical College and the New York-Presbyterian Hospital, New York. 3. Department of Imaging, Cedars Sinai Medical Center, Los Angeles, Calif. 4. Department of Medicine, University of Erlangen, Erlangen, Germany. 5. Department of Medicine, Weill Cornell Medical College and the New York-Presbyterian Hospital, New York. 6. Department of Medicine, Wayne State University, Henry Ford Hospital, Detroit, Mich. 7. Department of Medicine, Harbor UCLA Medical Center, Los Angeles, Calif. 8. Tennessee Heart and Vascular Institute, Hendersonville. 9. Division of Cardiology, Severance Cardiovascular Hospital, Seoul, Korea. 10. Cardiovascular Radiology Unit, Giovanni XXIII Hospital, Monastier di Treviso, Italy; Department of Radiology and Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands. 11. William Beaumont Hospital, Royal Oaks, Mich. 12. Department of Medicine and Radiology, University of Ottawa Heart Institute, Ontario, Canada. 13. Department of Biostatistics, Boston University, Boston, Mass. 14. Capitol Cardiology Associates, Albany, NY. 15. Division of Cardiology, Deutsches Herzzentrum München, Munich, Germany. 16. Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. 17. University Hospital, Zurich, Switzerland. 18. Department of Medicine, Emory University School of Medicine, Atlanta, Ga. 19. Department of Medicine, Walter Reed Medical Center, Washington, DC. 20. Baptist Cardiac and Vascular Institute, Miami, Fla. 21. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. 22. Department of Medicine and Radiology, Seoul National University Hospital, Seoul, South Korea. 23. Division of Cardiology and Department of Medical Imaging, St. Paul's Hospital, University of British Columbia, Vancouver, Canada. 24. Hospital da Luz, Lisboa, Portugal. 25. Department of Biostatistics, Boston University and Harvard Clinical Research Institute, Boston, Mass.
Abstract
OBJECTIVE: To develop a clinical cardiac risk algorithm for stable patients with suspected coronary artery disease based upon angina typicality and coronary artery disease risk factors. METHODS: Between 2004 and 2011, 14,004 adults with suspected coronary artery disease referred for cardiac imaging were followed: 1) 9093 patients for coronary computed tomography angiography (CCTA) followed for 2.0 years (CCTA-1); 2) 2132 patients for CCTA followed for 1.6 years (CCTA-2); and 3) 2779 patients for exercise myocardial perfusion scintigraphy (MPS) followed for 5.0 years. A best-fit model from CCTA-1 for prediction of death or myocardial infarction was developed, with integer values proportional to regression coefficients. Discrimination was assessed using C-statistic. The validated model was tested for estimation of the likelihood of obstructive coronary artery disease, defined as ≥50% stenosis, as compared with the method of Diamond and Forrester. Primary outcomes included all-cause mortality and nonfatal myocardial infarction. Secondary outcomes included prevalent angiographically obstructive coronary artery disease. RESULTS: In CCTA-1, best-fit model discriminated individuals at risk of death or myocardial infarction (C-statistic 0.76). The integer model ranged from 3 to 13, corresponding to 3-year death risk or myocardial infarction of 0.25% to 53.8%. When applied to CCTA-2 and MPS cohorts, the model demonstrated C-statistics of 0.71 and 0.77, respectively. Both best-fit (C = 0.76; 95% confidence interval [CI], 0.746-0.771) and integer models (C = 0.71; 95% CI, 0.693-0.719) performed better than Diamond and Forrester (C = 0.64; 95% CI, 0.628-0.659) for estimating obstructive coronary artery disease. CONCLUSIONS: For stable symptomatic patients with suspected coronary artery disease, we developed a history-based method for prediction of death and obstructive coronary artery disease.
OBJECTIVE: To develop a clinical cardiac risk algorithm for stable patients with suspected coronary artery disease based upon angina typicality and coronary artery disease risk factors. METHODS: Between 2004 and 2011, 14,004 adults with suspected coronary artery disease referred for cardiac imaging were followed: 1) 9093 patients for coronary computed tomography angiography (CCTA) followed for 2.0 years (CCTA-1); 2) 2132 patients for CCTA followed for 1.6 years (CCTA-2); and 3) 2779 patients for exercise myocardial perfusion scintigraphy (MPS) followed for 5.0 years. A best-fit model from CCTA-1 for prediction of death or myocardial infarction was developed, with integer values proportional to regression coefficients. Discrimination was assessed using C-statistic. The validated model was tested for estimation of the likelihood of obstructive coronary artery disease, defined as ≥50% stenosis, as compared with the method of Diamond and Forrester. Primary outcomes included all-cause mortality and nonfatal myocardial infarction. Secondary outcomes included prevalent angiographically obstructive coronary artery disease. RESULTS: In CCTA-1, best-fit model discriminated individuals at risk of death or myocardial infarction (C-statistic 0.76). The integer model ranged from 3 to 13, corresponding to 3-year death risk or myocardial infarction of 0.25% to 53.8%. When applied to CCTA-2 and MPS cohorts, the model demonstrated C-statistics of 0.71 and 0.77, respectively. Both best-fit (C = 0.76; 95% confidence interval [CI], 0.746-0.771) and integer models (C = 0.71; 95% CI, 0.693-0.719) performed better than Diamond and Forrester (C = 0.64; 95% CI, 0.628-0.659) for estimating obstructive coronary artery disease. CONCLUSIONS: For stable symptomatic patients with suspected coronary artery disease, we developed a history-based method for prediction of death and obstructive coronary artery disease.
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