Yelin Yang1, Li Chen1, Yeung Yam1, Stephan Achenbach2, Mouaz Al-Mallah3, Daniel S Berman4, Matthew J Budoff5, Filippo Cademartiri6, Tracy Q Callister7, Hyuk-Jae Chang8, Victor Y Cheng4, Kavitha Chinnaiyan9, Ricardo Cury10, Augustin Delago11, Allison Dunning12, Gudrun Feuchtner13, Martin Hadamitzky13, Jörg Hausleiter14, Ronald P Karlsberg15, Philipp A Kaufmann16, Yong-Jin Kim17, Jonathon Leipsic18, Troy LaBounty4, Fay Lin19, Erica Maffei6, Gilbert L Raff9, Leslee J Shaw20, Todd C Villines21, James K Min4, Benjamin J W Chow22. 1. Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Canada. 2. Department of Medicine, University of Erlangen, Erlangen, Germany. 3. Department of Medicine, Wayne State University, Henry Ford Hospital, Detroit, Michigan. 4. Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. 5. Department of Medicine, Harbor UCLA Medical Center, Los Angeles, California. 6. Department of Radiology, Giovanni XXIII Hospital, Monastier di Treviso, Italy; Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands. 7. Tennessee Heart and Vascular Institute, Hendersonville, Tennessee. 8. Division of Cardiology, Severance Cardiovascular Hospital, Seoul South Korea. 9. Department of Cardiology, William Beaumont Hospital, Royal Oak, Michigan. 10. Baptist Cardiac and Vascular Institute, Miami, Florida. 11. Capitol Cardiology Associates, Albany, New York. 12. Department of Public Health, Weill Cornell Medical College and the New York Presbyterian Hospital, New York, New York. 13. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. 14. Division of Cardiology, Technische Universität München, Munich, Germany. 15. Cardiovascular Medical Group, Los Angeles, California. 16. Cardiac Imaging, University Hospital, Zurich, Switzerland. 17. Seoul National University Hospital, Seoul, South Korea. 18. Department of Medicine and Radiology, University of British Columbia, Vancouver, Canada. 19. Department of Medicine, Weill Cornell Medical College and the New York Presbyterian Hospital, New York, New York; Department of Radiology, Weill Cornell Medical College and the New York Presbyterian Hospital, New York, New York. 20. Department of Medicine, Emory University School of Medicine, Atlanta, Georgia. 21. Department of Medicine, Walter Reed Medical Center, Washington, DC. 22. Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Canada. Electronic address: bchow@ottawaheart.ca.
Abstract
OBJECTIVES: This study sought to develop a clinical model that identifies patients with and without high-risk coronary artery disease (CAD). BACKGROUND: Although current clinical models help to estimate a patient's pre-test probability of obstructive CAD, they do not accurately identify those patients with and without high-risk coronary anatomy. METHODS: Retrospective analysis of a prospectively collected multinational coronary computed tomographic angiography (CTA) cohort was conducted. High-risk anatomy was defined as left main diameter stenosis ≥50%, 3-vessel disease with diameter stenosis ≥70%, or 2-vessel disease involving the proximal left anterior descending artery. Using a cohort of 27,125, patients with a history of CAD, cardiac transplantation, and congenital heart disease were excluded. The model was derived from 24,251 consecutive patients in the derivation cohort and an additional 7,333 nonoverlapping patients in the validation cohort. RESULTS: The risk score consisted of 9 variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms. Patients were divided into 3 risk categories: low (≤7 points), intermediate (8 to 17 points) and high (≥18 points). The model was statistically robust with area under the curve of 0.76 (95% confidence interval [CI]: 0.75 to 0.78) in the derivation cohort and 0.71 (95% CI: 0.69 to 0.74) in the validation cohort. Patients who scored ≤7 points had a low negative likelihood ratio (<0.1), whereas patients who scored ≥18 points had a high specificity of 99.3% and a positive likelihood ratio (8.48). In the validation group, the prevalence of high-risk CAD was 1% in patients with ≤7 points and 16.7% in those with ≥18 points. CONCLUSIONS: We propose a scoring system, based on clinical variables, that can be used to identify patients at high and low pre-test probability of having high-risk CAD. Identification of these populations may detect those who may benefit from a trial of medical therapy and those who may benefit most from an invasive strategy.
OBJECTIVES: This study sought to develop a clinical model that identifies patients with and without high-risk coronary artery disease (CAD). BACKGROUND: Although current clinical models help to estimate a patient's pre-test probability of obstructive CAD, they do not accurately identify those patients with and without high-risk coronary anatomy. METHODS: Retrospective analysis of a prospectively collected multinational coronary computed tomographic angiography (CTA) cohort was conducted. High-risk anatomy was defined as left main diameter stenosis ≥50%, 3-vessel disease with diameter stenosis ≥70%, or 2-vessel disease involving the proximal left anterior descending artery. Using a cohort of 27,125, patients with a history of CAD, cardiac transplantation, and congenital heart disease were excluded. The model was derived from 24,251 consecutive patients in the derivation cohort and an additional 7,333 nonoverlapping patients in the validation cohort. RESULTS: The risk score consisted of 9 variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms. Patients were divided into 3 risk categories: low (≤7 points), intermediate (8 to 17 points) and high (≥18 points). The model was statistically robust with area under the curve of 0.76 (95% confidence interval [CI]: 0.75 to 0.78) in the derivation cohort and 0.71 (95% CI: 0.69 to 0.74) in the validation cohort. Patients who scored ≤7 points had a low negative likelihood ratio (<0.1), whereas patients who scored ≥18 points had a high specificity of 99.3% and a positive likelihood ratio (8.48). In the validation group, the prevalence of high-risk CAD was 1% in patients with ≤7 points and 16.7% in those with ≥18 points. CONCLUSIONS: We propose a scoring system, based on clinical variables, that can be used to identify patients at high and low pre-test probability of having high-risk CAD. Identification of these populations may detect those who may benefit from a trial of medical therapy and those who may benefit most from an invasive strategy.
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