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.
Bibliographical noteFunding Information:
Dr. Chow holds the Saul and Edna Goldfarb Chair in Cardiac Imaging Research and receives research support from GE Healthcare ; and educational support from TeraRecon Inc . Dr. Achenbach has received grant support from Siemens and Bayer Schering Pharma ; and is a consultant for Servier. Dr. Al-Mallah is a consultant for GE Healthcare. Dr. Budoff is on the Speakers Bureau for GE Healthcare. Dr. Cademartiri receives grant support from GE Healthcare ; is a consultant for Servier; is on the Speakers Bureau of Bracco; is a consultant for GE Healthcare; and has given expert testimony for Siemens. Dr. Chinnaiyan has received grant support from Bayer Pharma and Blue Cross Blue Shield Blue Care Network of Michigan . Dr. Hadamitzky’s department has an unrestricted research grant from Siemens Healthcare. Dr. Kaufmann receives grant support from the Swiss National Science Foundation and GE Healthcare . Dr. Leipsic is a consultant for GE Healthcare. Dr. Maffei has received grant support from GE Healthcare . Dr. Min has received research support and is on the Speakers Bureau for GE Healthcare; is a consultant for Heartflow, Abbott Vascular, Neograft Technologies, and CardioDx; is on the scientific advisory board of Arineta; and has ownership in MDDX, Autoplaq, and TC3. Dr. Raff has received grant support from Siemens , Blue Cross Blue Shield Blue Care Network of Michigan and Bayer Schering Pharma . All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Udo Hoffmann, MD, served as Guest Editor for this paper.
© 2015 American College of Cardiology Foundation.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Cardiology and Cardiovascular Medicine