A clinical model to identify patients with high-risk coronary artery disease

Yang Y, Chen L, Yam Y, Achenbach S, Al-Mallah M, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng VY, Chinnaiyan K, Cury R, Delago A, Dunning A, Feuchtner G, Hadamitzky M, Hausleiter J, Karlsberg RP, Kaufmann PA, Kim YJ, Leipsic J, Labounty T, Lin F, Maffei E, Raff GL, Shaw LJ, Villines TC, Min JK, Chow BJW (2015)

Publication Type: Journal article

Publication year: 2015

Journal

Book Volume: 8

Pages Range: 427-34

Journal Issue: 4

DOI: 10.1016/j.jcmg.2014.11.015

Abstract

This study sought to develop a clinical model that identifies patients with and without high-risk coronary artery disease (CAD).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.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.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.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.

Authors with CRIS profile

Involved external institutions

University of Ottawa Canada (CA) Wayne State University (WSU) United States (USA) (US) Cedars-Sinai Medical Center United States (USA) (US) Harbor–UCLA Medical Center United States (USA) (US) Casa di Cura Giovanni XXIII Italy (IT) Tennessee Heart & Vascular United States (USA) (US) Yonsei University Health System (YUHS) Korea, Republic of (KR) Beaumont Health System United States (USA) (US) Baptist Health South Florida United States (USA) (US) Capitol Cardiology Associates United States (USA) (US) Cornell University United States (USA) (US) Medizinische Universität Innsbruck Austria (AT) Technische Universität München (TUM) Germany (DE) Cardiovascular Medical Group of Southern California United States (USA) (US) Universitätsspital Zürich (USZ) Switzerland (CH) Seoul National University (SNU) / 서울대학교 Korea, Republic of (KR) University of British Columbia Canada (CA) Emory University United States (USA) (US) Walter Reed Army Medical Center (WRAMC) United States (USA) (US)

How to cite

APA:

Yang, Y., Chen, L., Yam, Y., Achenbach, S., Al-Mallah, M., Berman, D.S.,... Chow, B.J.W. (2015). A clinical model to identify patients with high-risk coronary artery disease. Journal of the American College of Cardiology : Cardiovascular imaging, 8(4), 427-34. https://doi.org/10.1016/j.jcmg.2014.11.015

MLA:

Yang, Yelin, et al. "A clinical model to identify patients with high-risk coronary artery disease." Journal of the American College of Cardiology : Cardiovascular imaging 8.4 (2015): 427-34.

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